Jointly Trained Automation of Explainable Construction Material Knowledge

ABSTRACT In the early phases of a construction project, generating accurate and timely quotations is important for assessing feasibility. Delays or significant revisions in quotations can lead to project cancellations, resulting in lost business opportunities. To address this challenge, we propose a machine learning framework called Jointly Trained Automation of Explainable Construction Material Knowledge ( JACK ), developed in collaboration with a construction company to estimate material requirements. Our methodology begins with pre‐processing estimation data, where construction materials are categorised into high‐level types to facilitate more efficient learning. To support this process, open‐source synthetic data generators were developed to help clarify structural patterns for JACK , which employs a cascaded learning approach during training. The evaluation phase leverages joint training to enhance model efficiency and presents results across 207 construction projects. We also investigate the effects of dropout layers, regression trees and synthetic data augmentation on prediction accuracy. Finally, we compare JACK against traditional regression‐based methods using a separate project set, where it demonstrates competitive performance. Overall, JACK achieves low error rates across a range of material types, with performance gains largely attributed to the benefits of cascaded learning.