Abstracts – Browse Results
Click on the titles below to expand the information about each abstract.
Viewing 20 results ...
Abdalla, A, Li, X and Yang, F (2024) Expatriate construction professionals' performance in international construction projects: The role of cross-cultural adjustment and job burnout. Journal of Construction Engineering and Management, 150(03).
Chen, S, Chen, D, Li, L, Miramini, S and Zhang, L (2024) Optimized bridge maintenance strategies: A system reliability-based approach to enhancing road network performance. Journal of Construction Engineering and Management, 150(03).
Do, Q, Le, T and Le, C (2024) Uncovering critical causes of highway work zone accidents using unsupervised machine learning and social network analysis. Journal of Construction Engineering and Management, 150(03).
Gu, J and Guo, F (2024) Promoting digital sustainability through project digital responsibility implementation: An empirical analysis. Journal of Construction Engineering and Management, 150(03).
Guo, J and Kato, H (2024) Role of government equity investment in capital structure of project finance: Global evidence from PPP projects in developing countries. Journal of Construction Engineering and Management, 150(03).
Halder, A and Batra, S (2024) Navigating the ethical discourse in construction: A state-of-the-art review of relevant literature. Journal of Construction Engineering and Management, 150(03).
Harode, A, Thabet, W and Leite, F (2024) Formulation of feature and label space using modified Delphi in support of developing a machine-learning algorithm to automate clash resolution. Journal of Construction Engineering and Management, 150(03).
Heydari, M, Heravi, G, Raeisinafchi, R and Karimi, H (2024) A dynamic model to assess the role of site supervision systems in the safety performance of construction projects. Journal of Construction Engineering and Management, 150(03).
Ibrahim, A, Nnaji, C, Namian, M and Shakouri, M (2024) Evaluating the impact of hazard information on fieldworkers' safety risk perception. Journal of Construction Engineering and Management, 150(03).
Ko, T, Lee, J and David Jeong, H (2024) Project requirements prioritization through NLP-driven classification and adjusted work items analysis. Journal of Construction Engineering and Management, 150(03).
Lee, D, Nie, G Y and Han, K (2024) Automatic and real-time joint tracking and three-dimensional scanning for a construction welding robot. Journal of Construction Engineering and Management, 150(03).
Li, Q, Yang, Y, Yao, G, Wei, F, Xue, G and Qin, H (2024) Multiobject real-time automatic detection method for production quality control of prefabricated laminated slabs. Journal of Construction Engineering and Management, 150(03).
Oguz Erkal, E D, Hallowell, M R, Ghriss, A and Bhandari, S (2024) Predicting serious injury and fatality exposure using machine learning in construction projects. Journal of Construction Engineering and Management, 150(03).
- Type: Journal Article
- Keywords: construction safety; machine learning; prediction; serious injury and fatality
- ISBN/ISSN: 0733-9364
- URL: http://doi.org/10.1061/JCEMD4.COENG-13741
- Abstract:
Safety academics and practitioners in construction typically use safety prediction models that employ information associated with past incidents to predict the likelihood of future injury or fatality on site. However, most prevailing models utilize only information related to failure (i.e., incident), so they cannot distinguish effectively between success and failure without well-informed comparison. Furthermore, recordable incidents on construction sites are extremely rare, which results in data that are too sparse to make predictions with high statistical power. This paper empirically reviews different approaches to safety to increase the understanding of conditions associated with safety success and failure. Empirical data about business-, project-, and crew-related factors were collected to predict serious injury and fatality (SIF) exposure conditions. A variety of modeling techniques were tested in a machine learning pipeline to identify the most accurate and stable predictive models. Results showed that the multilayer perceptron (MLP) approach best distinguished SIF exposure conditions from safety success conditions using nonlinear decision boundaries. The most influential factors in the models included the crew experience working together, supervisor experience with the crew, total number of workers under the supervisor's purview, and the maturity of leadership development programs for frontline supervisors. This study showed that data sets with both success and failure information yield more reliable and meaningful predictions than data sets with failure alone. Such an approach to safety data collection, analysis, and prediction could be used by future researchers to generate new insights into the causes of serious incidents and the relationships among causal factors.
Qureshi, A H, Alaloul, W S, Murtiyoso, A, Hussain, S J, Saad, S and Musarat, M A (2024) Automated scaling of point cloud rebar model via aruco-supported controlled markers. Journal of Construction Engineering and Management, 150(03).
Rajabi Asadabadi, M and Zwikael, O (2024) Unrealistic project goals: Detection and modification. Journal of Construction Engineering and Management, 150(03).
Sadeghi, N, Dehghani, M S and Ingolfsson, A (2024) Choice of probability distributions for activity durations in project networks with limited sample size. Journal of Construction Engineering and Management, 150(03).
Seo, W, Kim, B, Bang, S and Kang, Y (2024) Identifying key financial variables predicting the financial performance of construction companies. Journal of Construction Engineering and Management, 150(03).
Tarekegn Gurmu, A and Mahmood, M N (2024) Critical factors affecting quality in building construction projects: Systematic review and meta-analysis. Journal of Construction Engineering and Management, 150(03).
Withrow, J, Dadi, G, Nassereddine, H and Sturgill, R (2024) Asphalt material e-ticketing workflow: Qualitative and quantitative analysis. Journal of Construction Engineering and Management, 150(03).
Zhang, Y, Ren, X, Zhang, J and Ma, Z (2024) A method for deformation detection and reconstruction of shield tunnel based on point cloud. Journal of Construction Engineering and Management, 150(03).