Mechanical carbon emission assessment during prefabricated building deconstruction based on BIM and multi-objective optimization | Scientific Reports

Scientific Reports volume 14, Article number: 27103 (2024) Cite this article

3240 Accesses

6 Citations

Metrics details

Machinery operation is a major source of carbon emissions in building deconstruction. Early intervention through Design for Deconstruction (DfD) is crucial for emission reduction, yet the factors influencing these emissions are underexplored. This study integrates parametric BIM with multi-objective optimization (MOO) to assess mechanical carbon emissions in deconstruction. Using the Octopus solver in Grasshopper for Rhino, the study analyzes independent variables—possible working hours (PWH), vertical speed (VS), and horizontal speed (HS)—and dependent variables—minimum mechanical carbon emissions (MCE (min)), minimum deconstruction period (DP (min)), and maximum working efficiency (WE (max)). A lightweight steel roof truss structure is analyzed, comparing real-world deconstruction with optimized DfD schemes. Sensitivity analysis for BIM-MOO optimized results reveal that: (1) Adjusting PWH, VS, and HS significantly affects WE and DP, though with limited impact on carbon emissions; (2) VS influences WE and DP more than HS; (3) Limiting DP is essential for balancing WE, DP, and MCE, with WE adjusted to 20–60% and modifications to PWH and VS achieving balanced management. This study underscores the importance of early design and real-time adjustments for efficient, low-emission deconstruction, supporting the advancement of green building practices.

In recent years, the building construction sector has emerged as one of the major contributors to carbon emissions and energy consumption with the architecture, engineering, and construction (AEC) industry. According to IPCC Sixth Assessment Report1, energy use in residential and non-residential buildings contributed 50% and 32% respectively to global building CO2 emissions, while embodied emissions accounted for 18%. The environmental impacts of building’s product, construction, maintenance, repair and replacement, and end-of-life phase are defined as embodied environmental impacts2, which are commonly assessed using Life Cycle Assessment (LCA) tool. Recently, there has been growing interest in embodied environmental impacts evaluation for end-of-life phase, with a focus on materials or components recycling3,4,5 and waste management6,7,8,9. Research10 has shown that the deconstruction phase accounts for 28% to 30% of the total emissions across two whole life cycles of a recyclable building, second only to the emissions from material production and building construction stage. As a building’s life cycle extends, carbon emissions across all phases, including deconstruction, decrease proportionally. However, whether it is in construction or deconstruction processes, the primary energy consumption stems from machinery operations. Although carbon emissions during the construction stage are lower than that in the operation stage, they remain significant due to the concentrated energy consumption within the relatively short construction or deconstruction periods. This highlights the importance of addressing machinery carbon emissions during building’s end-of-life phase.

According to EN15978 (2011) standard11, five stages of Building Life Cycle Assessment (BLCA) are outlined: material production stage, construction stage, transportation stage, use stage, and end-of-life stage. However, the processes of fabrication of prefabricated components, transportation from the factory to the site, and the component assembly on-site are involved in prefabricated buildings construction. Hence, EN 15978 is not entirely suitable for assessing the environmental impacts of prefabricated buildings. In response, several studies have proposed a more comprehensive framework for Prefabricated Building Life Cycle Assessment (PBLCA), which includes seven stages: material production stage12, component manufacture stage13, component transportation stage14,15, building construction stage16, operation stage17, renovation and reuse stage16, deconstruction and reuse stage16. Building deconstruction and reuse, also referred to as selective demolition18, emphasizes the systematic disassembly of structures to maximize the recycling of materials and components while minimizing material waste and environmental impacts. This stage reflects the separation and recycle of building components rather than abandonment, thereby extending the building’s lifespan and facilitating reuse19.

Building information modelling (BIM) is widely recognized for its ability to explore design solutions that enhance life cycle performance20. BIM enables the creation of Bill of Quantities (BoQ) and provides project teams with real-time insights into the impact of design decisions on building performance. Additionally, BIM allows for the integration of user-defined parameters through Visual Programming Language (VPL) environment21,22,23. However, existing studies have rarely focused on the building deconstruction and reuse stage, particularly concerning process-based machinery operation. Most research has concentrated on scenario analysis, evaluation system development, or methodology creation. Consequently, there has been little investigation into the mechanical carbon emission associated with the deconstruction and reuse stage, despite earlier studies indicating that machinery is a significant carbon emitter during production-intensive activities in the agriculture and manufacturing sectors24,25,26. Additionally, it remains uncertain whether similar conclusions apply to the building deconstruction and reuse stages, underscoring the need for further research in this area.

The Multi-objective optimization (MOO) method has seen widespread application in the construction industry. It has been applied in optimizing building design for selecting appropriate shapes and orientations27, minimizing life cycle cost and environmental impacts28, and balancing embodied and operation carbon29. Recently, MOO method has also been demonstrated as an effective tool for facilitate Design optimization for Deconstruction, particularly for optimizing selective disassembly22,30. Benjamin22 highlighted that MOO is crucial for developing several effective deconstruction plans that promote adaptive reuse. However, many of these studies are not grounded in real-world deconstruction processes, and none have investigated machinery activities to validate the MOO results. This gap highlights the need for further research into the integration of machinery operations within MOO frameworks in DfD.

Accordingly, this paper aims to evaluate the mechanical carbon emissions during deconstruction phase for existing prefabricated buildings through BIM-MOO integration. The optimization objectives are minimizing mechanical carbon emissions (MCE (min)), reducing the deconstruction period (DP (min)), and maximizing working efficiency (WE (max)). The structure of the paper is as follows: Section "Literature review" offers a critical literature review, covering design principles for deconstruction, the role of BIM for building deconstruction, MCE assessment through MOO method, and identifying research gaps. Section "Methodology" outlines the research methodology, detailing three main steps for BIM-MOO integration. Section "Results" presents the results of proposed procedure using sensitivity analysis. Section "Discussion" discusses the comparison between actual scenarios and simulated results. Finally, Section "Conclusions and future work" concludes the study and suggests potential improvements for future research.

In recent years, the design principle for deconstruction has been explored and refined through various scientific studies. These principles are generally based on whether the building design is related to deconstruction and whether it involves reuse and recycling process (Table 1). Deconstruction, defined as “the whole or partial disassembly of buildings to facilitate component reuse and materials recycling”, has been identified as an essential approach for promoting a closed-loop for building component reuse31. The concept of “design for deconstruction (DfD)” refers to design principles aimed at facilitating deconstruction of building components for reuse and recycling. It is crucial to consider not only the recycling potential of building components at the end of their service life, but also how easily they can be disassembled and reassembled32.

Early in Habraken’ study33, it is demonstrated that building layers makes DfD technically feasible, as the interfaces between these layers become key points of deconstruction. As a result, the complexity of component connection has been identified as primary principle of deconstruction. Habraken33 specifically categorized component connection types into fixed, bolted, nailed, and dowel connections, aligning with the principle of connection complexity. In addition to this primary principle, factors such as the sequence of deconstruct process21,22,34,35,36,37,38,39, the direction and overlay of joint21,22,34,35,36,37,38,39, whether the building is prefabricated9,31,36,40,41,42 and the time required for deconstruction35,36,37 are also crucial when applying DfD principles. In terms of reusability and recyclability, the design principles emphasize the use of reusable materials and the avoidance of composite materials during design phase9,34,36,37,41,43,44. These principles have evolved to include the use of non-toxic, reusable, and recyclable materials9,36,40,44, minimizing the types34,40,42 and quantities of building components34,36,44, and avoiding the use of composite materials with secondary finishes9,36,37,41,42,43. Xiao45 further developed these principles into a green deconstruction model, which is used to evaluate the deconstruct ability of building structures.

Over the past few decades, the use of BIM in building deconstruction has grown significantly. The relevant literature on BIM integration technology has been shown in Table 2. BIM is an object-oriented intelligent model8 that provides detailed information about the relationships and the physical properties of objects46,47. This capability allows for the automation of deconstruction schemes generation and evaluation by offering insights into the properties, quantities and connection relationships of materials and components required for performance and environmental impact assessment of deconstructive building41. Moreover, researchers can simulate and analyze deconstruction and reuse stage of a building within a virtual environment through VPL, such as Dynamo in Revit and Grasshopper in Rhino. This approach has demonstrated its potential in deconstruction-related studies. For example, Kim8 utilized VPL and Disassembly Assessment System (DAS) to implement deconstruct ability assessment calculations on BIM platform. And it is demonstrated by Salehabadi48 and Cavalliere49 that , VPL presents a high degree of control and deconstruction-related customizability of BIM due to its geometric modelling functionality and ability to create algorithms.

Other research has mainly focused on scenario analysis, evaluation system establishment and methodology development. Scenario analysis using BIM has been conducted to various deconstruction principles, including building construction cost50 and carbon footprint51. Akbarnezhad50 mentioned that BIM supports project scenario analysis by allowing user-defined attributes to handle variables. For instance, attributes have been introduced related to handling, installation, disassembly, the thickness of cover concrete for embedded steel connections to be removed, and the necessary fixing points for disassembly.

Interest in BIM-based deconstruction evaluation systems has also grown recently. Akinade9 proposed a BIM-DAS framework in 2015 aimed at ensuring building deconstruct ability and minimizing waste from demolished buildings. Andrew41 further established a BIM-based DAS model for steel structures, using Revit and Dynamo to automate index calculations. Afterwards, DAS has been recognized as an effective tool to identify the building deconstruction performance8. Building on these, Akinade52 developed Disassembly and Deconstruction Analytics System (D-DAS), and implemented it as a plug-in for Revit 2017. The results showed that this method could reduce CO2 emissions and costs up to 40.1% and 13%, respectively, comparing to the original alternatives.

Given that BIM for building deconstruction is still in its early stage, recent research has primarily concentrated on methodology development. Akinade32 conducted Focus Group Interviews with professionals experienced in using BIM to explore its potential for DfD. This research highlighted the key components of functional framework and emphasized that developing deconstruction plans, evaluating performance, and simulating end-of-life alternatives are essential factor to building deconstruction.

Multi-objective optimization (MOO) for mechanical carbon emission assessment has recently been applied to manufacturing industry, agricultural industry, and logistic industry. Wang et al.24 developed a multi-optimization model by considering manufacturing cost, quality loss, and carbon emissions of the grinding process. This model demonstrated that it is possible to reduce carbon emissions while maintaining the accuracy of the machine. Zheng et al.25 optimized single-target emissions for biodiesel-diesel blend ratio, engine speed, and engine load, achieving error rates of 3%-10% compared to predicted values. In logistics, Arsham26 devised mixed integer programming and dynamic programming methods to optimize order picker forklift routes, considering factors like item-platform friction, forklift acceleration, deceleration, and load indicators. The research found that these solutions not only save energy but also reduced time in the order-picking process.

However, there are few studies examining the variables affecting the energy consumption of construction or deconstruction machinery in the building sector. Most research has focused on optimizing the internal structural design of construction machinery to reduce environmental impact. For example, Ismael53 centered on the quantitative design analysis of electric scissor lift components, such as actuator selection for the wheel drive unit and lift motion, aiming to enhance the operational efficiency of the machinery. Stawinski54 conducted an experimental investigation into the energy consumption of low-lifting capacity hydraulic scissor lifts, emphasizing the power supply to each control component as a key factor in reducing energetic efficiency. Existing research has shown that construction machinery used in earthwork, piling, concrete, mortar, lifting, and transportation can account for about 90% of the total CO2 emissions during the building construction process55. Similarly, Wang’s study14 demonstrated that optimizing the load ratio and average speed in real-world battery electric vehicles (BEVs) for component transportation can reduce greenhouse gas emissions by 36.18%-54.69%. Therefore, process-based studies on the operational mechanisms of construction machinery are particularly important for understanding the carbon emissions impact of construction projects. Furthermore, research56 has indicated that 29% of operation time can influence an average of 50% of the total environmental impact of construction machinery. Consequently, the operating time of machinery in specific construction projects should be considered an important parameter. Sizirici57 suggests that improving energy and working efficiency, as well as optimizing the operational processes of construction machinery, can significantly reduce direct carbon emissions in the construction industry. However, this research often remains at the recommendation stage without in-depth exploration.

Despite the limited clarity on influencing factors, research on Mechanical Carbon Emission (MCE) assessment through Multi-objective Optimization (MOO) methods in the construction industry remains sparse. Some studies have used single-objective optimization approach14,58, but few have focused on building deconstruction. He58 proposed a nodal optimization procedure based on carbon emissions of tower cranes lifting to explore optimization schemes for ultra-high-rise steel structure construction. The results demonstrated that the optimized lifting scheme reduced carbon emissions from 11,288.25 kgCO2 to 10,824.85 kgCO2, and the frequency of lifting operations decreased from 6,880 to 6,100. However, no studies have focused on mechanical carbon emission assessment during building deconstruction using the MOO approach.

For DfD scheme optimization, more studies exist, especially in mechanical and manufacturing industry30,59,60,61. As for construction industry, research has mainly focused on DfD optimization22,62. Benjamin22 conducted through MOO approach to generate the set of noninferior solutions for deconstruction plan that minimizes environmental impacts and building cost. Mehran62 optimized disassembly sequence time, cost and environmental impacts using an MOO algorithm, resulting in a 17.6%-23.4% time reduction. However, these studies did not investigate real-world deconstruction processes, nor did they consider mechanical emissions during deconstruction.

The review of existing literature reveals a theoretical foundation for DfD principles, but practical validation is still lacking. Although BIM technology has been integrated into building deconstruction, its application has primarily focused on scenario analysis, evaluation system establishment and methodology development. MCE evaluation in construction industry has gained increased attention. However, the application of MOO method, which have been explored in the manufacturing, agricultural, and logistic industry, has not yet been fully investigated in the context of building deconstruction stage. As a result, the evaluation of mechanical carbon emissions in existing deconstructive buildings using BIM-MOO integration has not been thoroughly demonstrated.

To address these gaps, this study including three main partitions:

A parametric BIM and mathematical model are developed to automatically calculate the mechanical carbon emissions of deconstructed buildings that adhere to Design for Disassembly (DfD) principles.

A BIM-based Multi-objective Optimization (MOO) analysis is performed to generate the optimal solution set for the study. This is combined with sensitivity analysis to investigate the impact of three direct parameters—Possible Working Hours (PWH), Vertical Speed (VS), and Horizontal Speed (HS)—and two indirect parameters—Deconstruction Period (DP) and Working Efficiency (WE)—on minimizing mechanical carbon emissions (MCE).

Sensitivity analysis is further employed through MOO to explore the trade-offs between minimizing DP, maximizing WE, and minimizing MCE. Based on these findings, the study proposes an optimal deconstruction scheme.

By validating these steps, this research aims to provide insights into the following questions:

How to integrate parametric BIM and MOO to evaluate the mechanical carbon emissions in building deconstruction stage?

What are the relationships between the five factors- PWH, VS, HS, DP (min), WE (min)-and MCE (min), during the building deconstruction processes?

Is it possible to simultaneously minimize MCE, minimize DP, and maximize WE during the building deconstruction processes, and what requirements must be met to achieve trades-offs?

This research applies a BIM-based multi-objective optimization (BIM-MOO) framework to assess mechanical carbon emissions during deconstruction phase of existing prefabricated building. The methodology comprises four primary steps (Fig. 1):

A lightweight steel roof truss structure is used to create a parametric physical model for deconstruction design. The deconstruction process is simulated, focusing on machinery activities, and the attributes required for assessing mechanical carbon emissions are customized using Visual Studio.

Parametric mathematical models are established within a BIM environment using visual programming languages (VPL). A comparison is conducted between actual and simulated outcomes, with sensitivity analysis highlighting the impact of variables across 100 optimal solutions, including possible working hours (PWH), vertical speed (VS), horizontal speed (HS), deconstruction period (DP), and working efficiency (WE). And the optimal solution has been proposed. Trade-offs among minimizing mechanical carbon emissions (MCE), reducing the deconstruction period, and maximizing working efficiency are also explored.

Research methodology.

The procedure (Fig. 2) was applied to lightweight steel roof truss structure originally located in the square of the inner yard in Qiangong building on the Sipailou Campus of Southeast University. The structure, which faced east and west, had a roof with peak elevation of 10.30 m. After its initial life cycle, the structure was reconstructed at the Jiulong Lake campus of the same university. The building featured a depth of 16 m, a net span of 15 m, a maximum height of 9.5 m, and a minimum height of 6 m (Appendix A). Data for this study were collected from construction drawings and photos captured with GoPro cameras during the deconstruction and reuse stage. The entire process of deconstructing the roof truss was carried out using a 12-m electric scissor lift. This type of aerial work platform, which consists of a platform raised or lowered vertically by linked folding support, offers both horizontal mobility and vertical access. The electric scissor lift is advantageous in reducing carbon emissions by eliminating the need for fossil fuels, allowing construction workers to access elevated heights while moving across the construction site with minimal environmental impact. For this study, an electric scissor lift supplied by China Construction Jin Cheng Machinery Equipment Co., Ltd. was selected (Table 3).

Project overview for deconstruction and reuse of lightweight steel truss structure.

In the actual deconstruction process, only the removal of the roof component system required the use of an electric scissor lift. Therefore, this study focuses on the deconstruction of structural components within the roof component system, including roof purlins, roof trusses, central supports, and cables, as the subject of BIM-MOO research (Fig. 3). The study specifically covers the C1 stage of the life cycle, corresponds to the deconstruction phase11, and includes the horizontal and vertical movements of the electric scissor lift. Other deconstruction activities were performed manually using simple mobile scaffolding. The mechanical operations considered in this study encompass the movement of the electric scissor lift as it reaches the connection points of the roof structural components. The time spent disassembling the connections while on the scissor lift is excluded, as the machinery is considered inactive during this period. Additionally, this study assumes that the electric scissor lift operates without mechanical losses or idle time, focusing solely on the carbon emissions generated by the lift’s movement during the deconstruction process.

Project component system and scope of case study.

The first step involved creating a BIM model of the case study with user-defined attributes, focusing on three categories: the structural component system (e.g., foundations, columns, cables, central supports, roof trusses), envelope component system (e.g., roof purlins and roof panels), and connection component system (Fig. 3). This model serves as the foundation for automated quantity take-off during deconstruction.

To calculate mechanical carbon emissions, specific attributes for components and nodes were customized according to deconstruction design principle (Table 4). These attributes were encoded in XML format and imported into the Rhino platform using Visual studio code, enabling the platform to extract relevant components for carbon emission calculations.

Finally, the native Rhino plugin Elefront was used to assign serial number attributes to geometries via reference by key/value, requiring the geometries to be established in “block” format. The attributes were then extracted, calculated, and updated using member index and replace items. The resulting data, detailing carbon emission attributes and values, were exported to Microsoft Excel for further analysis.

This step focused on visualizing the machinery routes for deconstruction process. The deconstruction process was arranged based on the following design principles of DfD: complexity of connections, sequence of deconstruction, machinery working time, and the overlay relationships between components. As illustrated in Fig. 4 using the three truss components of the first frame as an example, the deconstruction strategy was developed based on these principles. The purlins and trusses exhibited an overlay relationship, requiring that purlins (E-W) be removed before the trusses due to their connection at the truss’s top nodes. For the three truss components, the sequence of deconstruction involved identifying node associations and planning the order of removal. For example, the truss (N-S) and the truss (E-W) in the middle of the 1st frame are connected by four M10 bolts, while the inclined truss (N-S) at the northwestern corner connects to the middle truss (N-S) with five M10 bolts. The top purlins (E-W) are fastened to the top of the truss (N-S) with two M10 bolts. Therefore, the deconstruction strategy for the 1st frame involves removing the outer middle truss (N-S) first (Fig. 4.①), followed by the truss (E-W) (Fig. 4. ②), and finally the inclined truss (N-S) at the northwest corner (Fig. 4.③).

Design logic and strategy for deconstruction.

Next, machinery routes were arranged based on the deconstruction process. A vertical lifting route plan and horizontal driving route plan were developed for the entire roof truss system. The vertical lifting route plan was created using Rhino and Grasshopper, accounting for elevation differences and machinery working height, as shown in Fig. 5. The horizontal driving route plan is depicted in Fig. 6. The roof structure consists of a total of five frames from the west to the east (Axis A—Axis E) and three main axes from the north to the south (Axis 1—Axis 3). Assuming the scissor lift starts at A-2, the driving route involves two sections in the N-S direction (1–2 and 2–3) and four sections in the E-W direction (A-B, B-C, C-D, D-E). Appendix B details the specific horizontal driving route for the scissor lift. During the actual deconstruction process, the scissor lift will stop at designated deconstruction point, complete a lifting and lowering cycle, and then proceed to the next point, such as from axis 2nd to axis 1st. To deconstruct the nodes between purlins and trusses, the electric scissor lift will repeat the process of lifting, driving, and lifting again. To simplify, the horizontal driving distance is considered as the straight line from axis 2nd to the axis 1st. Finally, the command component serial number was used to extract geometry with a customized deconstruction sequence. The Slider Animate component generated coded-color images, while the Blindfold plugin was used to hide components scheduled for deconstruction.

The vertical lifting route arranged by VPL in Rhino.

The horizontal driving route of the whole roof frames.

The parametric mathematical model for mechanical carbon emissions was developed based on the Standard for Building Carbon Emission Calculation (GB/T51366-2019). The daily operation hours of machinery are crucial for estimating carbon emissions during building deconstruction, as discussed in Section "Literature review". Thus, the possible working hours must be included in the model. Given the characteristics of the electric scissor lift, the model calculates operating time based on the deconstruction plan, which includes vertical and horizontal travel distances and speeds. Therefore, this study selects possible working hours (PWH), vertical speed (VS), and horizontal speed (HS) as the direct calculation parameters for MCE calculation. Additionally, working efficiency (WE) and deconstruction period (DP) are selected as indirect impact parameters, following the research requirements outlined in Section "MCE assessment through MOO method". The relationships between these direct and indirect parameters and the MCE calculation method are detailed as follows:

Machinery carbon emission (MCE) represents the carbon output produced by construction machinery during the assembly or disassembly of building components. In this study, the system boundary is defined specifically for the C1 deconstruction stage of the building lifecycle11. According to Standard for Building Carbon Emission Calculation (GB/T51366-2019), carbon emissions during building deconstruction stage should be calculated using the following formula:

where: \(C_{Decon}\) represents the carbon emissions per kg component during building deconstruction phase (\({\text{kgCO}}_{2} /{\text{kgCom}}\)). \(E_{d,i}\) is the total energy consumption of the energy source i (kWh or kg). \(F_{i}\) is carbon emission factor of the energy source i (\({\text{kgCO}}_{2} /{\text{kWh}}\), \({\text{kgCO}}_{2} /{\text{kg}}\)).\(Q_{d,i}\) is the quantity of the building component i (t, kg, m3, m2) .\(CC_{m}\) represents the carbon content per unit calorific value (\(tC/TJ\)). \(OF\) denotes the carbon oxidation rate. \(NCV_{m}\) is the net calorific value (\(TJ/10^{4} t\)).For non-electric machinery, the carbon emission factor should be sourced from the IPCC1 and be calculated through Formula (2). However, since the electric scissor lift is powered by electricity, the carbon emission factor of 0.695 \({\text{kgCO}}_{2} /{\text{kWh}}\) is referenced from the China Regional Power Grids Carbon Dioxide Emission Factors (2023). Additionally, the conversion factor for carbon oxidation into CO2 is applied, considering the change in molecular weight from 12 to 44.

The total energy consumption for machinery during building deconstruction follows the formulas below:

where: \(f_{d,i}\) is the energy consumption coefficient per unit of the building component i (kWh/(t, kg, m3, m2) com), \(M_{i,j}\) is the consumption of the machinery j due to the building deconstructive component i (unit: shift/kg com, the time a machine works per day is regulated no more than 8 h), \(E_{j}\) is the energy consumption of the machinery j per shift due to the building deconstructive component i (kWh/shifts), PWH is the possible working hours per day (hrs/day).

The deconstruction period (DP) represents the total operational duration required for machinery to complete a deconstruction project, calculated using the following formula:

Where: \(DP\) refers to the number of days required for machinery operation to complete a certain amount of deconstruction project,\(T\) is the total operating time of deconstruction machinery (hrs). \(T_{h}\) and \(T_{v}\) represent the time consumed for horizontal and vertical movements (hrs), and \(HS\) and \(VS\) represent the horizontal and vertical speeds (km/hrs).

The horizontal distance of the machinery is calculated as follows:

where: \(D_{h}\) is the total horizontal distance for machinery (m), and the construction machinery has been assumed travelled in a straight line. \(N_{{_{hi} }}\) and \(N_{{_{hj} }}\) are the number of trips driving in the E-W and N-S directions, respectively. \(D_{hi(hj)}\) represents the horizontal and vertical travel distance covered in each trip (m). The vertical travel distance of the machinery is determined using Grasshopper parametric modeling, which calculates the height differences between the component connection nodes and the machinery’s working plane.

Machinery working efficiency (WE) measures the efficiency of construction machinery operations during a project. \(WE\) refers to the ratio of the machinery operational time to the total working time in one day (%). This value is the reciprocal of the value of deconstruction period (DP). It is calculated as follow:

The maximum estimated time required for the electric scissor lift to complete deconstruction, considering the slowest vertical and horizontal transport speeds, is no more than 3 h. Therefore, the possible working hours per day (PWH) are set between 0.1 and 3 h. As detailed in Table 3, the vertical speed of the scissor lift ranges from 6 m/min to 9 m/min, while the horizontal speed ranges from 0.8 km/h to 3.5 km/h.

According to the CHINA Standard Consumption Quota for Prefabricated Building Projects, an 8-h shift is the standard daily working period for machinery, which is used in this study’s calculations. The daily working shifts for the electric scissor lift can be determined using Eq. (5). The scissor lift, which is electrically powered, consumes 48.52 kWh per shift, with a carbon emission factor of 0.695 kgCO2/kWh (as provided by the China Reginal Power Grids Carbon Dioxide Emission Factors (2023)). By designing the deconstruction plan and calculating travel distances (using Eqs. (9) and (10)), and setting the parameter ranges for machinery speed, the time required for the full deconstruction of the roof frame can be estimated. The deconstruction period is calculated using Eqs. (6) through (8), with working efficiency assessed as the ratio of possible working hours to total working time, using Eq. (11). Mechanical carbon emissions during deconstruction are then determined using Eqs. (1) through (4). To ensure PWH reflects actual conditions, the parametric scripts set the boolean toggle for working efficiency (WE) to True when it is 100% or less.

Mechanical carbon emissions are assessed using a BIM-MOO integration method, with PWH, vertical speed (VS), and horizontal speed (HS) as independent variables. The objectives are to minimize mechanical carbon emissions (MCE), reduce the deconstruction period (DP), and maximize working efficiency (WE). The Octopus evolutionary solver, based on the SPEA-2 reduction algorithm, performs 100 iterations to obtain Pareto optimal solutions. Sensitivity analyses, including both Univariate and Multivariate Sensitivity Analysis, are conducted to examine the impacts of PWH, VS, and HS on WE, DP, and MCE. Univariate sensitivity analysis also explores the trade-offs between WE, DP, and MCE. Based on these analyses, the optimal solution is proposed.

During the deconstruction phase, a total of 13,605 GoPro photos were taken, with 5,753 photos specifically capturing the deconstruction of the roof structure (Appendix C). The deconstruction was divided into 53 sequences, eight steps fewer than in the simulated procedures (Appendix D). Due to some missing data from manual collection, only 47 sequences were available for analysis, compared to 65 sequences outlined in the design schemes. As shown in Table 5, the deconstruction of the roof frame occurred between October 9 and October 15, 2023, with a two-day suspension. The photos captured only the vertical movement of the machinery, without recording the time spent manually disassembling components on the working plane. The simulation had assumed that manual disassembly did not consume energy from the electric scissor lift. Calculations revealed that the vertical movement of the machinery during the roof frame deconstruction lasted 2.28 h, resulting in mechanical carbon emissions of 9.614 kgCO2 per kg component. A comparison between the simulated and the actual deconstruction process revealed deviations in two scenarios:

Scenario one involved determining whether the components would require separate operation of the machinery to deconstruct the nodes between the purlins and trusses (where D = 1 indicates “yes”, and D = 0 indicates “no”) (Fig. 7). The electric scissor lift has no need to operate separately to disassemble the nodes of the trusses and purlins when deconstruct the trusses, taking 2320 s. In contrast, the scissor lift does not separately operate to disassemble the nodes when deconstructing purlins, taking 200 s. The time for which scissor lift operates separately to disassemble the nodes when deconstructing the trusses, is 1890s. Similarly, the time for which scissor lift operates separately to disassemble the nodes when deconstructing purlins, is 2430 s.

Machinery operation separately to deconstruct the nodes between the purlins and trusses.

Scenario two explored whether the components would require separate operation of the machinery to tie ropes or not (where J = 1 indicates “yes” and J = 0 indicates “no”) (Fig. 8). The time required for operation of scissor lift for rope tying during truss deconstruction (Truss (J = 1), 3140 s), is longer compared to situations when rope tying during purlin deconstruction (Purlin (J = 1), 130 s). Notably, the time spent solely on operating the machinery to release nodes while simultaneously tying ropes accounted for 1450s during truss deconstruction (Truss (D = 1, J = 1)). Conversely, during purlin deconstruction, the need for separate rope-tying operations was less frequent, especially when there were nodes between the purlins and trusses (Purlin (D = 1, J = 1)).

Machinery operation separately to tie or untie ropes for components.

To assess the sensitivity of optimization results to the primary factors, Univariate Sensitivity Analysis were conducted on possible working hours (PWH) and machinery speed concerning WE, DP, and MCE. The sensitivity coefficients are illustrated in Fig. 9. The order of influence for both WE and DP was PWH > VS > HS. PWH and VS positively affected WE but negatively influenced DP. For MCE, the influence order was also PWH > VS > HS. HS negatively impacted MCE, while PWH and VS had positive effects. However, the effects of HS and VS on MCE were not statistically significant (p > 0.05) (Appendix E.1). When PWH varied independently, it had a strong positive effect on WE and MCE but a significant negative impact on DP, suggesting that increasing PWH improves WE and MCE while reducing DP. Therefore, PWH is a key factor affecting WE, DP, and MCE. Changes in HS positively impacted WE and negatively affected DP, with minimal significance on MCE (p > 0.05) (Appendix E.1). A similar pattern was observed with VS, which had a greater impact on WE and DP compared to HS, indicating VS is crucial for these metrics.

Univariate sensitivity analysis of PWH, HS, and VS on WE, DP, and MCE.

A Multivariate Sensitivity Analysis was conducted to explore the interactions of PWH and speed with WE, DP, and MCE. The sensitivity coefficients (Fig. 10) and scatter plot (Fig. 11) revealed the following: For WE, the interactions ranked as PWH*VS > PWH*HS > HS*VS, all showing positive influence. For DP, the rank was PWH*HS > HS*VS > PWH*VS, with PWH*HS and HS*VS negatively impacting DP. For MCE, the rank was PWH*VS > PWH*HS > HS*VS, with PWH*VS negatively affecting MCE, while HS*VS had a positive effect. The impact of PWH*HS on MCE was negative but not statistically significant (p > 0.05) (Appendix E.2).

Multivariate sensitivity analysis of PWH, HS, and VS on WE, DP, and MCE.

The scatter plot for PWH and machinery speed versus the objective variables.

When PWH and VS varied together, there was a stronger positive impact on WE than on DP, with a minor reduction in MCE. This trend was evident in Fig. 11a, where increases in PWH and VS slightly reduces MCE, particularly when PWH exceeded 50% and VS exceeded 60%. When PWH and HS changed together, there was a significant reduction in DP, with a slight but not significant decrease in MCE and a notable increase in WE. Figure 11b indicates MCE decreased only when HS was between 20 and 80%. For simultaneous changes in HS and VS, a greater positive impact on MCE were observed compared to WE, with a significant decrease in DP. Figure 11c supports this, showing uniform effects of speed variations on WE. However, when HS and VS were not increased together, increasing VS individually proved more effective in reducing MCE and shortening DP compared to increasing HS.

The effects of PWH, VS on WE and DP are direct, but their influence on mechanical carbon emissions (MCE) is relatively minor. Therefore, a detailed analysis was conducted to explore the balance between WE, DP, and MCE. Univariate sensitivity analysis was performed to assess the impact of WE and DP on MCE. The sensitivity coefficients, shown in Fig. 12 and Appendix E.3, reveal that increases in WE have a significantly greater positive effect on MCE compared to the negative effect of changes in DP. This suggests that an increase in WE lead to a substantial rise in MCE, while an increase in DP results in only a marginal decrease in MCE. This balance is further illustrated in Fig. 13. When mechanical WE (max) exceed 80% and DP is reduced to less than 5 days, MCE rises above 8 kgCO2/kg component. Conversely, when WE fall below 20%, although MCE decreases to less than 2 kgCO2/kg component, DP extends beyond 10 days, making it impractical. As concluded from Section "PWH and machinery speed versus WE, DP, and MCE", the most effective strategy to manage both WE and DP is to simultaneously reduce PWH and VS. This approach leads to improvements in both WE and DP. As shown in Fig. 13, optimal results are achieved when the deconstruction plan is completed within 5 days, maintaining WE between 20 and 60%. In this scenario, MCE remains between 2 and 5 kgCO2/kg component. The solution set, with WE adjusted to 20%-60% and corresponding modifications to PWH and VS, offers a balanced approach to managing WE, DP, and MCE, ensuring efficient and practical deconstruction.

Univariate sensitivity analysis of WE and DP on MCE.

The scatter plot for DP WE and MCE.

Figure 14 displays the ten Pareto-optimal solutions selected from the 100th generation of optimization, as detailed in Table 6. Analysis of these solutions, in line with the sensitivity results from Section "PWH and machinery speed versus WE, DP, and MCE" and the conclusions in Section "The trades-off between WE, DP, and MCE", indicates that only Solutions 5 through 10 meet the criteria of having working efficiency (WE) values within the 20%-60% range. These solutions also ensure that the MCE are kept between 2 and 5 kgCO2/kg component. Among these, Solution 5 achieves the lowest carbon emissions, with a calculated value of 2.613 kgCO2/kg component using Eqs. (1) through (4). The simulated deconstruction time for the roof truss structure, as determined by Eqs. (7) and (8), is 2.547 h, combining 0.268 h for horizontal lifting and 2.279 h for vertical lifting.

The 10 optimized solutions from the 100th generation by Multi-objective Optimization.

Scenario one examined whether the machinery needed to operate separately when disassembling the connections between purlins and trusses. It was found that deconstructing purlins required significantly more time for independent operation of the machinery. The purlins, being positioned above the trusses, led to extended dismantling times as their nodes were disassembled concurrently with the purlins.

Scenario two investigated whether the machinery needed to operate separately when components required rope-tying. It was observed that more time was required for the scissor lift to perform separate operations for rope-tying during truss deconstruction compared to purlins. Two factors contribute to this difference. Firstly, the trusses’ weight exceeds the scissor lift’s capacity, making it challenging to transport them from high places without rope support. Secondly, structurally, central supports and cables, which are lower than the trusses, must be removed before the scissor lift can access the truss nodes. Ropes are then needed to stabilize the upper trusses temporarily, complicating the process further.

When deconstructing lower load-bearing components, it is essential to implement stabilizing measures for upper components to ensure their safe removal. This may involve adjusting machinery posture, physically securing components, and performing separate operations of machinery prior to component deconstruction. Furthermore, when components are interconnected by nodes, removing one component usually means that the connecting node has already been disassembled, preventing redundant tasks. This scenario underscores the necessity of developing detailed deconstruction plans and machinery operation strategies tailored to the project’s specific characteristics. Inadequate planning can lead to additional machinery operations due to unforeseen deconstruction conditions. While vertical transportation times may align closely with optimized results, horizontal transportation times are likely to increase due to unnecessary routing. The interrelationships between components significantly impact machinery operation methods and paths, making it crucial to accurately simulate these conditions.

The analysis revealed significant differences between actual and simulated machinery operations time, particularly in the absence of an early design scheme, affecting working efficiency and carbon emissions. Actual results showed a total of 2.28 h for vertical operation, 0.286 shifts, and an energy efficiency of 13.831 kWh/kg component, leading to an MCE of 9.614 kgCO2/kg component. In contrast, the optimized simulation for Solution 5 indicated a vertical lifting time of 2.279 h, a minor reduction in time but a 72.7% decrease in carbon emissions (from 9.614 kgCO2/kg to 2.613 kgCO2/kg).

Firstly, the Univariate Sensitivity Analysis of PWH on WE, DP, and MCE revealed that increasing daily working hours alone led to higher working efficiency but also increased MCE. For instance, the shortest vertical operation time was 0.11 h per day with an emission of 0.457 kgCO2/kg, while the longest was 0.66 h per day with 2.787 kgCO2/kg. This increase was due to a lack of a detailed deconstruction plan, leading to redundant machinery operations, such as separately operating machinery to disassemble nodes and tie ropes. Thus, simply extending working hours may not reduce MCE effectively and could even increase it.

Secondly, the actual deconstruction did not account for varying vertical and horizontal speeds of the scissor lift. Despite increased working hours, the completion rate did not improve significantly, and rushed work often led to delays. Univariate sensitivity analysis of horizontal speed (HS) and vertical speed (VS) showed that while increasing machinery speed improved efficiency, but the reduction in MCE was not substantial. Adjusting any single parameter among PWH, VS, and HS had a notable impact on work efficiency and deconstruction period but did not significantly reduce carbon emissions.

The Multivariate Sensitivity Analysis revealed that increasing both PWH and VS improved work efficiency but extended the project duration, with minimal impact on MCE reduction. Limiting the deconstruction period was crucial to balance working efficiency, deconstruction period, and MCE. For this case study, a 5-day deconstruction period with PWH at 0.62 h per day, HS at 0.815 km/h, and VS at 7.508 m/min maintained working efficiency at 24% and reduced MCE by 72% compared to actual results.

This paper presents a method for evaluating mechanical carbon emissions during the building deconstruction process based on BIM and multi-objective optimization. The key findings and recommendations are as follows:

Design principles for deconstruction Early evaluation of the environmental impact, particularly regarding to mechanical carbon emissions is crucial during the building deconstruction process. When selecting building materials, priority should be given to those that are easily deconstructed, such as recyclable metals, wood, and glass, while avoiding toxic and hazardous materials with secondary finishes. Standardized component and connection designs can enhance deconstruction efficiency, reducing both time and the energy consumption during machinery operations.

Overlay components and node disassembly Effective early-stage deconstruction planning should account for overlay components at the same elevation and the disassembly of nodes. For components that overlap, a systematic layer-by-layer deconstruction is recommended. Stabilizing upper components during the removal of lower load-bearing parts is crucial for safe deconstruction. Addressing these factors early can improve safety, lower costs, and reduce energy consumption.

Key factors influencing WE, DP, and MCE Sensitivity analysis of the multi-objective optimization showed that adjusting parameters such as daily working hours (PWH), vertical speed (VS), and horizontal speed (HS) had varying effects. Increasing PWH improved efficiency but also raised carbon emissions. Increasing VS was more effective than HS in enhancing efficiency and reducing emissions, though the reduction was modest. Combined adjustments of PWH and VS improved efficiency but extended the deconstruction period, with limited impact on emissions. Therefore, while individual or paired adjustments to PWH, VS, and HS significantly affect work efficiency and project duration, their impact on carbon emissions is limited.

Trade-offs between WE, DP, and MCE To achieve a balance between working efficiency (WE), deconstruction period (DP), and mechanical carbon emissions (MCE), both WE and DP must fall within feasible ranges, such as maintaining work efficiency between 20%-60% as proposed in this study. Sensitivity analysis indicates that increasing PWH and VS together can enhance WE but also extend DP. To achieve an optimal balance, it is crucial to adhere to a strict deconstruction timeline and optimize machinery operation paths based on a multi-objective evaluation of energy consumption.

Despite the contributions of this study, there are limitations that future work should address. For instance, the current model assumes a straight-line trajectory for machinery operations and does not consider scenarios where overlapping nodes or load-bearing components require specialized handling. More realistic simulation models and data collection methods could improve deconstruction route planning. Additionally, the study’s parametric model used fixed values for machinery energy consumption and daily working hours. Incorporating a broader range of variables could refine the results.

Despite the limitations, this study offers valuable insights into mechanical carbon emissions during deconstruction and presents several practical applications. Firstly, the findings can be directly applied to projects using electrically powered machinery, with the potential to serve as a reference for those using other energy sources. The principles outlined are applicable to all deconstruction projects following the Design for Deconstruction (DfD) approach, enabling pre-planned deconstruction paths and optimized efficiency. Furthermore, in the context of intelligent construction, the study’s conclusions provide guidance for future machinery design and construction management. By leveraging information and automation technologies—such as adaptive machinery and real-time monitoring—construction processes can be optimized to enhance efficiency and minimize emissions. The integration of BIM with multi-objective optimization and big data analysis could yield more accurate machinery operation plans, further reducing unnecessary operations and lowering carbon emissions. Additionally, advancing intelligent construction will necessitate improvements in project timeline management, especially for complex deconstruction tasks. By focusing on effective early design and making real-time adjustments during the process, it is possible to ensure both efficient and low-emission operations, thereby supporting the future of green building practices.

Data will be made available on request, and the first author Baolin Huang (email: [email protected]) should be contacted if researchers want to request the data from this study.

Architecture, engineering, and construction

Building information modelling

Bill of quantity

Deconstruction assessment scoring

Deconstruction period

Design for deconstruction

Battery electric vehicles

Horizontal speed

Building life cycle assessment

Prefabricated building life cycle assessment

Life cycle assessment

Mechanical carbon emission

Multi-objective optimization

Possible working hours

Visual programming language

Vertical speed

Working efficiency

Kikstra, J. S. et al. The IPCC Sixth Assessment Report WGIII climate assessment of mitigation pathways: From emissions to global temperatures. Geosci. Model Dev. 15, 9075–9109 (2022).

Article ADS Google Scholar

Tam, V. W. Y., Zhou, Y., Shen, L. & Le, K. N. Optimal BIM and LCA integration approach for embodied environmental impact assessment. J. Clean. Prod. 385 (2023).

Wang, J., Wei, J., Liu, Z., Huang, C. & Du, X. Life cycle assessment of building demolition waste based on building information modeling. Resour. Conserv. Recycl. 178, 106095 (2022).

Article Google Scholar

Cai, G. & Waldmann, D. A material and component bank to facilitate material recycling and component reuse for a sustainable construction: Concept and preliminary study. Clean Technol. Environ. Policy 21, 2015–2032 (2019).

Article CAS Google Scholar

Lima, P. R. B. D., Rodrigues, C. D. S. & Post, J. M. Integration of BIM and design for deconstruction to improve circular economy of buildings. J. Build. Eng. 80, 108015 (2023).

Article Google Scholar

Su, S., Li, S., Ju, J., Wang, Q. & Xu, Z. A building information modeling-based tool for estimating building demolition waste and evaluating its environmental impacts. WASTE Manag. 134, 159–169 (2021).

Article PubMed Google Scholar

Ismaeel, W. S. E. & Kassim, N. An environmental management plan for construction waste management. AIN SHAMS Eng. J. 14, 102244 (2023).

Article Google Scholar

Kim, S. & Kim, S.-A. A design support tool based on building information modeling for design for deconstruction: A graph-based deconstructability assessment approach. J. Clean. Prod. 383, 135343 (2023).

Article Google Scholar

Akinade, O. O. et al. Waste minimisation through deconstruction: A BIM based Deconstructability Assessment Score (BIM-DAS). Resour. Conserv. Recycl. 105, 167–176 (2015).

Article Google Scholar

Ullah, H., Zhang, H., Huang, B. & Gong, Y. BIM-based digital construction strategies to evaluate carbon emissions in green prefabricated buildings. Buildings 14, 1689 (2024).

Article Google Scholar

European Committee for Standardization. EN 15978:2011 - Sustainability of construction works—Assessment of environmental performance of buildings - Calculation method. iTeh Standards https://standards.iteh.ai/catalog/standards/cen/62c22cef-5666-4719-91f9-c21cb6aa0ab3/en-15978-2011 (2012).

Lu, Y., Le, V. H. & Song, X. Beyond boundaries: A global use of life cycle inventories for construction materials. J. Clean. Prod. 156, 876–887 (2017).

Article Google Scholar

Xiang, Y., Mahamadu, A.-M., Florez-Perez, L. & Wu, Y. Design optimisation towards lower embodied carbon of prefabricated buildings: Balancing standardisation and customisation. Dev. Built Environ. https://doi.org/10.1016/j.dibe.2024.100413 (2024).

Wang, H. et al. Real-world carbon emissions evaluation for prefabricated component transportation by battery electric vehicles. Energy Rep. 8, 8186–8199 (2022).

Article Google Scholar

Xiang, Y. et al. Embodied carbon determination in the transportation stage of prefabricated constructions: A micro-level model using the bin-packing algorithm and modal analysis model. Energy Build. 279, 112640 (2023).

Article Google Scholar

Hei, S. et al. Implementing BIM and lean construction methods for the improved performance of a construction project at the disassembly and reuse stage: A case study in Dezhou, China. Sustainability 16, 656 (2024).

Article Google Scholar

Qu, K., Zhang, H., Zhou, X., Zhao, L. & Sun, B. Comparison analysis on simplification methods of building performance optimization for passive building design. Build. Environ. 216, 108990 (2022).

Article Google Scholar

Ramos, M., Paiva, A. & Martinho, G. Understanding the perceptions of stakeholders on selective demolition. J. Build. Eng. 82, 108353 (2024).

Article Google Scholar

Akinade, O. et al. Design for deconstruction using a circular economy approach: Barriers and strategies for improvement. Prod. Plan. Control 31, 829–840 (2020).

Article Google Scholar

Cavalliere, C., Habert, G., Dell’Osso, G. R. & Hollberg, A. Continuous BIM-based assessment of embodied environmental impacts throughout the design process. J. Clean. Prod. 211, 941–952 (2019).

Article Google Scholar

Sanchez, B., Rausch, C., Haas, C. & Hartmann, T. A framework for BIM-based disassembly models to support reuse of building components. Resour. Conserv. Recycl. 175, 105825 (2021).

Article Google Scholar

Sanchez, B., Rausch, C., Haas, C. & Saari, R. A selective disassembly multi-objective optimization approach for adaptive reuse of building components. Resour. Conserv. Recycl. 154, 104605 (2020).

Article Google Scholar

Sanchez, B., Rausch, C. & Haas, C. Deconstruction programming for adaptive reuse of buildings. Autom. Constr. 107, 102921 (2019).

Article Google Scholar

Wang, L., Han, J., Ma, F., Li, X. & Wang, D. Accuracy design optimization of a CNC grinding machine towards low-carbon manufacturing. J. Clean. Prod. 406, 137100 (2023).

Article CAS Google Scholar

Zheng, B. et al. Prediction and optimization of emission in an agricultural harvest engine with biodiesel-diesel blends by a method of ANN and CMA-ES. Comput. Electron. Agric. 197, 106903 (2022).

Article Google Scholar

Khoei, A. A., Sural, H. & Tural, M. K. Energy minimizing order picker forklift routing problem. Eur. J. Oper. Res. 307, 604–626 (2023).

Article MathSciNet Google Scholar

Jalali, Z., Noorzai, E. & Heidari, S. Design and optimization of form and facade of an office building using the genetic algorithm. Sci. Technol. Built Environ. 26, 128–140 (2020).

Article Google Scholar

Chen, Y. & Gallardo, S. A multi-objective optimization method for the design of a sustainable house in ecuador by assessing LCC and LCEI. Sustain. Switz. 16 (2024).

Lobaccaro, G. et al. Parametric design to minimize the embodied GHG emissions in a ZEB. Energy Build. 167, 106–123 (2018).

Article Google Scholar

Sun, X., Guo, S., Guo, J., Du, B. & Tang, H. An improved multi-objective evolutionary algorithm for multiple-target asynchronous parallel selective disassembly sequence planning. Proc. Inst. Mech. Eng. Part B. 237, 1553–1569 (2023).

Jaillon, L. & Poon, C. S. Life cycle design and prefabrication in buildings: A review and case studies in Hong Kong. Autom. Constr. 39, 195–202 (2014).

Article Google Scholar

Akinade, O. O. et al. BIM-based deconstruction tool: Towards essential functionalities. Int. J. Sustain. Built Environ. 6, 260–271 (2017).

Article Google Scholar

Habraken, N. J. The Structure of the Ordinary: Form and Control in the Built Environment (MIT Press, 2000).

Durmisevic, E. Green Design and Assembly of Buildings and Systems, Design for Disassembly a Key to Life Cycle Design of Buildings and Building Products (VDM Verlag Dr, 2010).

Google Scholar

Denis, F., Vandervaeren, C. & De Temmerman, N. Using network analysis and BIM to quantify the impact of design for disassembly. Buildings 8, 113 (2018).

Article Google Scholar

Carvalho Machado, R., Artur de Souza, H. & de Souza Veríssimo, G. Analysis of guidelines and identification of characteristics influencing the deconstruction potential of buildings. Sustainability 10, 2604 (2018).

Vandervaeren, C., Galle, W., Stephan, A. & De Temmerman, N. More than the sum of its parts: Considering interdependencies in the life cycle material flow and environmental assessment of demountable buildings. Resour. Conserv. Recycl. 177, 106001 (2022).

Article Google Scholar

Zhai, J. BIM-Based Building Circularity Assessment from the Early Design Stages (Eindh. Univ, 2020).

Google Scholar

Vliet, M. Disassembling the Steps Towards Building Circularity (Eindh. Univ, 2018).

Google Scholar

Mattaraia, L., Fabricio, M. M. & Codinhoto, R. Structure for the classification of disassembly applied to BIM models. Archit. Eng. Des. Manag. 19, 56–73 (2023).

Google Scholar

Basta, A., Serror, M. H. & Marzouk, M. A BIM-based framework for quantitative assessment of steel structure deconstructability. Autom. Constr. 111, 103064 (2020).

Article Google Scholar

Crowther, P. Design for disassembly–themes and principles. Environ. Des. Guide 1–7 (2005).

Adams, K. Design for deconstruction-helping construction unlock the benefits of the circular economy. BRE Buzz (2015).

Guy, B., Shell, S. & Esherick, H. Design for deconstruction and materials reuse. Proc. CIB Task Group 39, 189–209 (2006).

Google Scholar

Xiao, J., Zeng, L., Ding, T., Xu, H. & Tang, H. Deconstruction evaluation method of building structures based on digital technology. J. Build. Eng. 66, 105901 (2023).

Article Google Scholar

Eastman, C. M. BIM Handbook: A Guide to Building Information Modeling for Owners, Managers, Designers, Engineers and Contractors (Wiley, 2011).

Niknam, M. & Karshenas, S. A shared ontology approach to semantic representation of BIM data. Autom. Constr. 80, 22–36 (2017).

Article Google Scholar

Salehabadi, Z. M. & Ruparathna, R. User-centric sustainability assessment of single family detached homes (SFDH): A BIM-based methodological framework. J. Build. Eng. 50, 104139 (2022).

Article Google Scholar

Cavalliere, C., Dell’Osso, G. R., Favia, F. & Lovicario, M. BIM-based assessment metrics for the functional flexibility of building designs. Autom. Constr. 107, 102925 (2019).

Article Google Scholar

Akbarnezhad, A., Ong, K. C. G. & Chandra, L. R. Economic and environmental assessment of deconstruction strategies using building information modeling. Autom. Constr. 37, 131–144 (2014).

Article Google Scholar

Ness, D., Swift, J., Ranasinghe, D. C., Xing, K. & Soebarto, V. Smart steel: New paradigms for the reuse of steel enabled by digital tracking and modelling. J. Clean. Prod. 98, 292–303 (2015).

Article Google Scholar

Akanbi, L. A. et al. Disassembly and deconstruction analytics system (D-DAS) for construction in a circular economy. J. Clean. Prod. 223, 386–396 (2019).

Article Google Scholar

Ismael, O. Y., Almaged, M. & Mahmood, A. Quantitative Design Analysis of an Electric Scissor Lift (2019).

Stawinski, L., Zaczynski, J., Morawiec, A., Skowronska, J. & Kosucki, A. Energy consumption structure and its improvement of low-lifting capacity scissor lift. Energies 14, 1366 (2021).

Article Google Scholar

Wu, W., Sun, P. & Zhou, H. The case study of carbon emission in building construction process. IOP Conf. Ser. Earth Environ. Sci. 371, 022011 (2019).

Article Google Scholar

Abolhasani, S. et al. Real-world in-use activity, fuel use, and emissions for nonroad construction vehicles: A case study for excavators. J. Air Waste Manag. Assoc. (2008).

Sizirici, B., Fseha, Y., Cho, C.-S., Yildiz, I. & Byon, Y.-J. A review of carbon footprint reduction in construction industry, from design to operation. Materials 14, 6094 (2021).

Article ADS CAS PubMed PubMed Central Google Scholar

He, J. et al. Optimization analysis of ultra-high-rise steel structure construction based on carbon emission. Eng. Rep. https://doi.org/10.1002/eng2.12833 (2024).

Article Google Scholar

Ehm, F. Scheduling and process planning for the dismantling shop with flexible disassembly mode and recovery level. Comput. Ind. Eng. 189, 109927 (2024).

Article Google Scholar

Dalle Mura, M., Pistolesi, F., Dini, G. & Lazzerini, B. End-of-life product disassembly with priority-based extraction of dangerous parts. J. Intell. Manuf. 32, 837–854 (2021).

Parsa, S. & Saadat, M. Intelligent selective disassembly planning based on disassemblability characteristics of product components. Int. J. Adv. Manuf. Technol. 104, 1769–1783 (2019).

Article Google Scholar

Mahmoudi Motahar, M. & Hosseini Nourzad, S. H. A hybrid method for optimizing selective disassembly sequence planning in adaptive reuse of buildings. Eng. Constr. Archit. Manag. 29, 307–332 (2022).

Download references

The authors acknowledge the funding support provided by the National Key R&D Program of China under the Research for Strategic Scientific and Technological Innovation Cooperation.

This research has been supported by the National Key R&D Program of China—Strategic Scientific and Technological Innovation Cooperation, “Joint Research and Demonstration of Green Low-carbon Renovation of Existing Buildings and the Technology of Carbon Neutralization (2022YFE0208600)”.

School of Architecture, Southeast University, Nanjing, 210096, China

Baolin Huang, Hong Zhang, Wensheng Yang & Hongyu Ye

School of Architecture, Nanjing Tech University, Nanjing, 211816, China

Boya Jiang

Search author on:PubMed Google Scholar

Search author on:PubMed Google Scholar

Search author on:PubMed Google Scholar

Search author on:PubMed Google Scholar

Search author on:PubMed Google Scholar

Baolin Huang: Conceptualization, Methodology, Software, Formal analysis, Data Curation, Writing—Original Draft. Hong Zhang: Supervision, Project Administration, Funding acquisition. Wensheng Yang: Resources, Data Curation, Investigation, Validation. Hongyu Ye: Software, Validation, Writing—Review & Editing. Boya Jiang: Writing—Review & Editing, Validation.

Correspondence to Hong Zhang.

The authors declare no competing interests.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

Huang, B., Zhang, H., Yang, W. et al. Mechanical carbon emission assessment during prefabricated building deconstruction based on BIM and multi-objective optimization. Sci Rep 14, 27103 (2024). https://doi.org/10.1038/s41598-024-78305-6

Download citation

Received: 24 May 2024

Accepted: 30 October 2024

Published: 07 November 2024

DOI: https://doi.org/10.1038/s41598-024-78305-6

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

npj Clean Energy (2025)