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Antunes, R, González, V A, Walsh, K, Rojas, O, O’Sullivan, M and Odeh, I (2018) Benchmarking Project-Driven Production in Construction Using Productivity Function: Capacity and Cycle Time. Journal of Construction Engineering and Management, 144(03).
Atherinis, D, Bakowski, B, Velcek, M and Moon, S (2018) Developing and Laboratory Testing a Smart System for Automated Falsework Inspection in Construction. Journal of Construction Engineering and Management, 144(03).
Castillo, T, Alarcón, L F and Salvatierra, J L (2018) Effects of Last Planner System Practices on Social Networks and the Performance of Construction Projects. Journal of Construction Engineering and Management, 144(03).
Chiang, Y, Wong, F K and Liang, S (2018) Fatal Construction Accidents in Hong Kong. Journal of Construction Engineering and Management, 144(03).
Darwish, M, Elsayed, A Y and Nassar, K (2018) Design and Constructability of a Novel Funicular Arched Steel Truss Falsework. Journal of Construction Engineering and Management, 144(03).
Huo, T, Ren, H, Cai, W, Shen, G Q, Liu, B, Zhu, M and Wu, H (2018) Measurement and Dependence Analysis of Cost Overruns in Megatransport Infrastructure Projects: Case Study in Hong Kong. Journal of Construction Engineering and Management, 144(03).
Jang, W, Yu, G, Jung, W, Kim, D and Han, S H (2018) Financial Conflict Resolution for Public-Private Partnership Projects Using a Three-Phase Game Framework. Journal of Construction Engineering and Management, 144(03).
Liao, P, Shi, H, Su, Y and Luo, X (2018) Development of Data-Driven Influence Model to Relate the Workplace Environment to Human Error. Journal of Construction Engineering and Management, 144(03).
- Type: Journal Article
- Keywords: Construction safety; Human error; Bayesian network; Structured learning; Labor and personnel issues;
- ISBN/ISSN: 0733-9364
- URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0001448
- Abstract:
Because human error plays a direct role in accidents, studying the causal relationship between the environment and human error is essential to prevent mishaps. However, these relationships have been explored solely using bivariate statistical analysis and thus require more intermediate factors to emphasize the need for monitoring and controlling human error by improving the workplace environment. Moreover, prevalent studies rely heavily on expert experience, which is subjective and creates potential estimation noise. In this study, the mechanism whereby environmental factors influence behavior and its associate factors is learned with an algorithm using a Bayesian network structure. Rather than being simply data-driven, the algorithm initiates learning from prior knowledge, the theoretical causal chain in the cognitive reliability and error analysis method (CREAM), and revises the learning approach against safety inspection data if necessary. The learned Bayesian network shows that human error and incorrect sequencing result from a combination of limited cognitive functions and improper spatial/workmanship arrangements caused by equipment defects, improper design, and management problems. Bridging the gaps in previous studies, the action interface revealed by this study is useful for on-site quality control.
Shrestha, P and Behzadan, A H (2018) Chaos Theory–Inspired Evolutionary Method to Refine Imperfect Sensor Data for Data-Driven Construction Simulation. Journal of Construction Engineering and Management, 144(03).
Tatum, C B ( (2018) Construction Engineering Research: Integration and Innovation. Journal of Construction Engineering and Management, 144(03).
Tatum, C B ( (2018) Learning Construction Engineering: Why, What, and How?. Journal of Construction Engineering and Management, 144(03).