A smart sensor-data-driven optimization framework for improving the safety of excavation operations. (1st May 2022)

• Record Type: Journal Article
• Authors: Costa, Alberto; Wang, Ze-Zhou; Goh, Siang Huat; Smith, Ian F.C.
• Is Part Of: Expert systems with applications. Volume 193(2022)
• Publication Date: 2022-05-01
• Subjects: Sensor data -- Surrogate model -- Excavation -- Systematic uncertainty -- Derivative-free optimization -- Physical-based behavior model; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2021.116413
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract: Excavation is a complex multistage problem, where field responses of soil properties such as deflections at one stage of the operation depend on responses at the preceding stage. In order to help asset managers make better decisions and thus improve safety, soil properties should be accurately identified using sensor-data collected at the current stage. This task is not easy to accomplish, mainly because of its intrinsic ambiguity. Sensors usually only measure effects (e.g., field responses) but not causes (e.g., soil parameter values). A strategy that helps meet this challenge is to perform inverse analysis to validate soil parameter values. Error-Domain Model Falsification (EDMF) is a methodology that achieves this goal. More precisely, EDMF helps identify good behavior models of excavation by falsifying soil parameter values for which the predictions of the corresponding behavior models cannot explain field-response measurements collected by sensors. However, a remaining challenge is the identification of soil parameter values that are not falsified by EDMF, especially when the computation of the predictions is time-consuming. This paper proposes a new framework that combines EDMF and an optimization algorithm for efficient identification of soil parameter values. Results on a full-scale excavation site in Singapore show that the new framework is robust and accurate, and it has the potential to improve current practice, which relies primarily on surrogate models without uncertainty.