Application of ensemble machine learning for construction safety risk assessment (Record no. 18883)
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| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20230217111853.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 230217b xxu||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | AIKTC-KRRC |
| Transcribing agency | AIKTC-KRRC |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 20029 |
| Author | George, M. Rijo |
| 245 ## - TITLE STATEMENT | |
| Title | Application of ensemble machine learning for construction safety risk assessment |
| 250 ## - EDITION STATEMENT | |
| Volume, Issue number | Vol.103(4), Dec |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | USA |
| Name of publisher, distributor, etc. | Springer |
| Year | 2023 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Pagination | 989-1003p. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | The rising prevalence of fatalities in the construction industry has prompted management to shift from traditional approaches to more advanced methods for analysis like machine learning (ML). Each construction project must undergo a risk assessment to understand the safety status of their construction sites and adopt preventative measures to avoid catastrophic incidents. The purpose of this study is to develop a prediction model for risk assessment of construction sites using ensemble machine learning techniques. A dataset from the Occupational Safety and Health Administration database of 4847 event reports from 2015 to 2017 is used for the analysis. Firstly, the primary risk factors causing accidents are identified and were divided into four: Before Accident, After Accident, Critical Factors-1 (CR-1), and Critical Factors-2 (CR-2). Using these attribute sets, predictive models were generated with five different classifiers with the help of different methods of resampling. The analysis was executed using both simple and ensemble modelling and the latter showed better results. The best performing classifiers under each attribute set were identified. Among the different models, the Gradient Boosting model trained with a CR-2 set of attributes, exhibited the best prediction results. Throughout the study, the application of ML in safety management has proven to be effective. The predictive model developed assists the safety management team comprehend the safety status of their specific construction projects and as a result, adopt appropriate preventive measures. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| 9 (RLIN) | 4642 |
| Topical term or geographic name entry element | Humanities and Applied Sciences |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 20030 |
| Co-Author | Nalluri, Madhusudana Rao |
| 773 0# - HOST ITEM ENTRY | |
| Place, publisher, and date of publication | Switzerland Springer |
| Title | Journal of the institution of engineers (India): Series A |
| International Standard Serial Number | 2250-2149 |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| URL | https://link.springer.com/article/10.1007/s40030-022-00690-w |
| Link text | Click here |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Articles Abstract Database |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Home library | Current library | Shelving location | Date acquired | Total Checkouts | Barcode | Date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | School of Engineering & Technology | School of Engineering & Technology | Archieval Section | 17/02/2023 | 2023-0338 | 17/02/2023 | 17/02/2023 | Articles Abstract Database |