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During subway tunnel construction, the consumed time for each ring (unit of construction progress) is highly dependent on objective factors such as geological conditions and shield performances, as well as subjective reasons including workers' proficiency and contractors' management skills. It is nontrivial to score each ring's efficiency from an objective angle, so that workers and contractors can receive fair evaluations. In this paper, a scoring method driven by construction big data is proposed. First, to resolve the high-dimensionality problem, a Gaussian mixture model (GMM) was employed to cluster rings of similar conditions in a probabilistic style so that detailed information can be retained. Second, the standard time to finish one ring was analyzed, so that each ring can be labeled as fail or pass, and our task can be considered as a classification problem. Third, for each cluster, a broad learning system (BLS) was developed as a classifier due to its advantages of fast computation and incremental learning. Finally, the BLS was trained with real tunneling data of 23,822 rings, and then scorecards were developed, where results of validation and statistical tests suggested that our method outperforms conventional ones. Feedback from the subway company and two compared contractors suggested that the proposed method is fair and practical, and it could reveal management problems that were easily overlooked. For subway shield tunnels, this study proposes a construction progress scoring method driven by big data for subway companies to evaluate the performance of each construction unit, e.g., each ring of the shield tunnel is treated as a sample analysis. First, in view of the difficulty of many geological types and risk variables, the Gaussian mixture method is used to classify the samples and obtain probability that each ring belongs to a certain geological type. Statistical analysis is used to calculate the shield standard time of each geological type, so that the time interval of pass can be determined. Second, a broad learning model with low calculation and updating cost is constructed to fit the mapping relationship between geological factors and construction progress. With this model, each ring can be scored. Finally, through the model training and verification of 23,822 ring data in seven subway intervals, results suggest that the method is practical. Our method can objectively reflect the work efficiency and management ability of different contractors and workers. In addition, suggestions can be elicited to improve the management level of construction units. [ABSTRACT FROM AUTHOR]

Recent research undertaken revealed that a significant positive relationship exists between quality and safety performance. A major limitation of this research, however, was the nature of the sample; it was heterogeneous (i.e., a combination of U.S. and international projects) and the sample was restricted to 18 projects. Building upon initial research, this paper re-examines the association between quality and safety using a homogeneous sample of 569 projects, which were derived from an Australian construction company with an annual turnover in excess of $1 billion Australian dollars (AU$). A total of 19,314 nonconformances and 17,783 injuries were used to determine the validity and reliability of previous research. A weak association between quality and safety performance was found (p < 0.01). The p-values did not indicate any significant association between first aid and quality rates, except for the injury rate and rework frequency per million scope, which yielded an r-value of 0.307 and p-value 0.046 that is significant at 0.01 level. An association, however, between injuries and rework was identified (r² = 0.70). The discrepancy between this research's findings and that of previous work led to an examination of the issues of using ratios in correlation analysis. Thus, the statistical and arithmetic issues associated with the use of ratios are discussed, and it is recommended that estimating the relationships between quality and safety should be examined using regression techniques or analysis of covariance. Linear regression, therefore, was performed with the injury data as the dependent variable, and rework frequency and personnel hours as the independent variables. The regression results demonstrated that there is a significant association between injuries, and rework and personnel hours; it was revealed that both predictors accounted for 68.2% of the explained variability in injury frequency. The replication of the initial research has enabled a significant advancement in knowledge about relationship between quality and safety performance. [ABSTRACT FROM AUTHOR]