MARC details
| 000 -LEADER |
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| fixed length control field |
04227aab a2200253 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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231005b20232023|||mr||| |||| 00| 0 eng d |
| 022 ## - INTERNATIONAL STANDARD SERIAL NUMBER |
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| International Standard Serial Number |
0733-9364 |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Darko, Amos |
| 9 (RLIN) |
677653 |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Glushakova, Luliia |
| 9 (RLIN) |
878391 |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Boateng, Emmanuel B. |
| 9 (RLIN) |
878392 |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Chan, Albert P.C. |
| 9 (RLIN) |
677654 |
| 245 ## - TITLE STATEMENT |
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| Title |
Using Machine Learning to Improve Cost and Duration Prediction Accuracy in Green Building Projects |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
1-21 p. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc. |
A 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.<br/>Practical Applications<br/><br/>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.<br/> |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Artificial Intelligence |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Machine Learning (ML) |
| 9 (RLIN) |
878393 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Web Application |
| 9 (RLIN) |
684222 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Accurate Cost and duration Prediction |
| 9 (RLIN) |
878394 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Green Building Projects (GBPs) |
| 9 (RLIN) |
878395 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Project Management |
| 773 0# - HOST ITEM ENTRY |
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| Title |
ASCE: Journal of Construction Engineering and Management |
| International Standard Serial Number |
07339364 |
| Place, publisher, and date of publication |
Reston,Virginia, U.S.A : American Society of Civil Engineers/ American Concrete Institute |
| 856 ## - ELECTRONIC LOCATION AND ACCESS |
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| Uniform Resource Identifier |
<a href="https://doi.org/10.1061/JCEMD4.COENG-13101">https://doi.org/10.1061/JCEMD4.COENG-13101</a> |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Suppress in OPAC |
No |
| Koha item type |
Articles |
| -- |
14993 |
| -- |
Mr. Muhammad Rafique Al Haj Rajab Ali (Late) |
