000			04227aab a2200253 4500
008			231005b20232023|||mr||| |||| 00| 0 eng d
022			_a0733-9364
100			_aDarko, Amos _9677653
100			_aGlushakova, Luliia _9878391
100			_aBoateng, Emmanuel B. _9878392
100			_aChan, Albert P.C. _9677654
245			_aUsing Machine Learning to Improve Cost and Duration Prediction Accuracy in Green Building Projects
300			_a1-21 p.
520			_aA major source of risk in green building projects (GBPs) is inaccurate human prediction of the final project cost and duration, which in turn results in cost and schedule overruns (i.e., poor project performance). This paper presents promising new models to mitigate such risk based upon machine learning (ML). Historical data from 198 GBPs in Hong Kong were used to develop and train two fully connected deep neural networks (DNN) models to learn and predict cost and duration, respectively, based on green building rating (GBR) and other project parameters. The models can predict cost and duration with mean absolute percentage error (MAPE) values of 0.07 and 0.09, respectively. They were then integrated with support vector regression (SVR), and results indicated that the integrated DNN-SVR models improve prediction accuracy, decreasing the MAPE from 0.07 to 0.06 (cost) and 0.09 to 0.07 (duration), respectively. The validated models were for the first time deployed as a ML-based web application for automated, fast, and accurate GBP cost and duration prediction. The feature importance analysis results revealed that the most influential parameters on the GBP cost and duration are project area and weather, respectively, not the GBR. Theoretically, the outcomes of this study provide new insights into the impact of GBR on project cost and duration, which are useful for the promotion of GBPs to improve sustainability. Practically, the study provides policymakers and practitioners with novel ML-based models and a web application to improve GBP delivery performance. Practical Applications Green building projects help to combat climate change and improve our health, wellbeing, and quality of life, but they face two key challenges in their execution: cost overruns and schedule delays. To address these challenges, there is a need for accurate prediction of the final project cost and duration from the early stages of the project. The practical relevance of this study is in the development of the first data-driven and machine learning-based web application for addressing this need. New integrated optimized machine learning models are developed. For practitioners to have access to and use these models without the need to possess machine learning expertise, a corresponding easy-to-use web application is offered. From the design and construction stages of their project, practitioners only need to input the green building ratings in sustainable site, materials and waste, energy use, water use, health and wellbeing, and innovations and additions they want to achieve for the project. The project type and area (type and size), original budget, planned duration, and start month (SM) and year should also be input. Once this project data input is completed, the web application automatically and instantaneously predicts with a high level of accuracy the total costs and time needed to deliver the project successfully—on time and on budget. These cost and duration prediction outputs are a valuable tool in helping practitioners adjust green building project plans and budgets to develop more realistic and accurate project budgets and programs to avoid cost overruns and schedule delays.
650			_aArtificial Intelligence
650			_aMachine Learning (ML) _9878393
650			_aWeb Application _9684222
650			_aAccurate Cost and duration Prediction _9878394
650			_aGreen Building Projects (GBPs) _9878395
650			_aProject Management
773	0		_tASCE: Journal of Construction Engineering and Management _x07339364 _dReston,Virginia, U.S.A : American Society of Civil Engineers/ American Concrete Institute
856			_uhttps://doi.org/10.1061/JCEMD4.COENG-13101
942			_2ddc _n0 _cART _o14993 _pMr. Muhammad Rafique Al Haj Rajab Ali (Late)
999			_c814228 _d814228