Piling Data-Driven Framework for Optimized Pile Structures Based on Minimizing the Expected Total Cost

Abstract

The use of data in the construction industry is growing rapidly. However, projects that do not have multiple stages, such as pile foundation and cantilever wall construction, are difficult to reinforce based on the data of observation. It cannot be said that the design–build construction process is optimized by piling data and active learning. In this paper, a new data-driven framework is proposed so that it can be used even for construction under single-stage conditions. The proposed method adopts a lower safety factor (SF) in the preliminary design than that in the conventional methods, and checks the performance after the building using piling data. Countermeasures are conducted to satisfy the target reliability, if necessary. Focusing on the expected total cost, the parametric studies reveal that the proposed method can reduce the expected total cost under specific conditions, such as lower countermeasure cost, higher failure cost, and higher relative costs of safety measures. Furthermore, our method exhibits robustness, as even with low initial safety factors, the expected total cost does not become excessively larger compared to the conventional methods. The findings highlight the potential benefits of piling data for optimizing construction projects under single-stage conditions.