Developing and Optimizing Context-Specific Fuzzy Inference System-Based Construction Labor Productivity Models

Abstract

Construction labor productivity (CLP) is affected by numerous context-sensitive influencing variables made up of subjective and objective factors, practices, and work sampling proportions (WSPs), which cause complex variability. Modeling CLP is challenging because for any given context, the complex impacts of multiple variables have to be considered simultaneously, without sacrificing accuracy or interpretability. Such challenges are addressed in this paper through the development of a methodology that explicitly represents context in CLP modeling and optimizes context-specific CLP models in order to improve accuracy. In addition, interpretable, fuzzy inference system (FIS)–based, and context-specific CLP models have been developed for the purpose of modeling concrete pouring activity. The performance of the context-specific CLP models is then compared with a generic CLP model, which is developed by combining the context-specific data sets. The results of the investigation showed that the key variables vary between the studied contexts and that the respective context-specific models have better prediction accuracy than the generic one. This study contributes to the body of knowledge in construction project management by demonstrating the essential role of context in the CLP model development process using context attributes, which provide a useful approach for characterizing existing CLP models and facilitate the use and adaptation of existing CLP models in new project contexts. In addition, this study presents a series of highly interpretable, context-specific CLP models to predict labor productivity in various building project contexts.