By Nidhi DhullReviewed by Susha Cheriyedath, M.Sc.Nov 19 2024A recent article published in Scientific Reports presented a performance calculator for construction waste management (CWM) during highway construction. The site waste management plan (SWMP) was used as a foundation to develop the proposed Construction Waste Management Performed Assessment Method (CWMPAM).
Study: Developing a construction waste management performance calculator for highway construction. Image Credit: helloRuby/Shutterstock.com
Background
CWM has emerged as a new core task in construction management, impacting overall project performance measurement. The SWMP has proven to be an effective measure to enhance CWM performance in construction activities.
However, SWMP focuses solely on general CWM guidelines, with limited attention to CWM’s dynamic achievements during construction. For instance, SWMP can quantify the CWM results during tasks like cleaning road base layers and excavating tunnel shafts, but it does not measure the excavation process.
A lack of standardized assessments of each activity during highway construction can compromise the accurate implementation of CWM, resulting in severe environmental pollution and economic losses. Moreover, this hinders continuous improvement in CWM. As a solution, Earned Value Management (EVM) is a promising tool that can measures the variances between planned and actual performance of construction activities. Therefore, this study adopted SWMP as the foundation for assessing CWM to develop a new extension of EVM.
Methods
A computational tool was developed through deductive reasoning as an extension within EVM to facilitate the efficient control of CW generation during highway construction.
The Work Breakdown Structure (WBS) was utilized to break down benchmark projects. The weights corresponding to sub-projects were calculated using the Least Absolute Shrinkage and Selection Operator (LASSO) based on duration and CW production. These served as standard references for CWM practices.
CWMPAM was integrated with Construction Inspection Requests (RFI) in highway engineering to enable practical application. Gray Linguistic Terms were adopted to determine the best and worst scenarios for CWM performed (CWMP). Additionally, specific calculation rules were adopted in the CWMPAM.
Each sub-project inspection was sampled from 10 checkpoints. The inspection results ranged from “VL” (Very Low) to “VH” (Very High). A result was considered qualified if it was “MH” (Moderate-High) or higher; otherwise, a second inspection was required after rectification. Once CWMPAM was determined, Construction Waste Consumption (CWC) was introduced as a new value (product of the executed CWM to date and the actual cost) in EVM.
The model validation was divided into a pilot run and scenario analysis for CWMPAM and CWM-embedded EVM. Post-verification, the CWM-Embedded EVM was computerized into a calculator to monitor the performance of CWM automatically. This calculator integrated Microsoft Excel and Matlab 2021a for construction applications. Finally, three completed highway construction projects were selected randomly to validate the calculator’s accuracy and applicability.
Discussion
The developed CWM-embedded EVM offers numerous advantages. It allows monitoring of the triple constraints, cost, schedule, and CWM within a single environment for highway construction. Additionally, it introduces different metrics for project managers to monitor CWM performance according to their goals.
The CWMP calculator facilitates integrated, multidimensional monitoring and feedback during highway construction. Unlike post-project evaluation methods such as lifecycle assessment, the calculator allows for real-time monitoring and feedback on CWMP. Moreover, project managers can evaluate CWM using SWMP and other standards, inputting the corresponding cost and schedule data into the calculator to estimate performance indices based on the outputs.
Combining flexibility with standardization, the CWMP calculator accounts for the specific characteristics of highway construction projects and the responsibilities of all stakeholders. Additionally, the grey numbers method enhances the scientific accuracy of both CWC and the CWM-embedded EVM. This helps account for hidden works and the potential data falsification by construction personnel while calculating CWC.
The trial runs on hypothetical cases, and scenario analyses demonstrated that while the unified scoring system keeps the final CWMP consistent, varying weights of sub-processes can significantly influence CWM performance and performance indices. Thus, the developed calculator has sufficient sensitivity and normalization capability. Additionally, the weighting system allows project managers to adjust based on the specific characteristics of their companies and projects during actual construction.
Conclusion
Overall, the researchers successfully utilized SWMP as the foundation to develop the CWMPAM, introducing new values. Grey processing was applied in CWM-embedded EVM to mitigate the subjective impact. The potential benefits of the developed CWMPAM for CWM practices in highway construction were demonstrated through multiple pilot runs and scenario analyses.
The CWMPAM facilitates continuous improvement during the project implementation phase, along with cross-project comparisons through performance measurements of unit projects. The newly introduced values and indicators complement traditional EVM, expanding its applicability.
However, this study is limited solely to highway engineering, excluding other infrastructure projects. Thus, specific construction tasks and management plans require adjustments for applying this approach to other engineering projects. The researchers suggest integrating blockchain technology into the calculator to ensure accurate data reporting and expand its application potential.
Journal Reference
Zheng, Z., Su, Y., Wang, X., & Zhou, Z. (2024). Developing a construction waste management performance calculator for highway construction. Scientific Reports, 14(1). DOI: 10.1038/s41598-024-79522-9, https://www.nature.com/articles/s41598-024-79522-9
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