Investigation into Explainable Regression Trees for Construction Engineering Applications

Abstract

The logic of an artificial intelligence (AI) model derived from machine learning algorithms and domain-specific data is analogous to an expert’s perception of a complex problem. Human insight based on know-how and experience also provides the best clue to verify such analytical models generalized from data. To facilitate the acceptance and implementation of AI by industry professionals, we explored the least complicated form of model that still is sufficient to represent the complexities of real-world problems. This research established a framework to apply the M5P model tree in the context of producing explainable AI for practical applications. The explanatory information derived from M5P (a decision tree with linear regressions at leaf nodes) is instrumental in explaining how the more complicated AI model reasons for the same problem, illuminating the sufficiency of problem definition and data quality, and distinguishing valid submodels from invalid ones in the obtained model tree. A steel fabrication labor cost–estimating case and a concrete strength development case were given for method validation and application demonstration.