Predicting rework propensity and cost impact from non-conformances under heterogeneous classification regimes
提出一种利用已完成项目的不合格报告数据预测未来返工可能性和成本的方法，采用惩罚逻辑回归和伽马广义线性模型，在两个独立数据集上验证了有效性，有助于施工前决策和质量风险优先排序。
Construction organizations routinely utilise nonconformance reports to document quality deviations, yet these reports are seldom used to anticipate future quality risks, particularly rework likelihood and cost impact. This paper proposes an ex-ante predictive modeling method that uses nonconformance data from completed projects to estimate rework propensity and associated cost consequences in future work. Although applicable to other dispositions, the focus is on rework due to its adverse effects on performance and productivity. Outcome-consistent prediction tasks are defined and aligned with appropriate statistical models: penalized logistic regression for rework likelihood and Gamma generalized linear models for cost impact. Validation procedures reflect the sparse and heterogeneous nature of project data. The method is tested using two independent datasets, demonstrating that meaningful patterns in rework occurrence and cost can be identified despite differences in classification practices. The approach enables organizations to support pre-construction decision-making, benchmarking, and targeted quality risk prioritization.