Digital twin for next gen concretes: On-demand tuning of vulnerable mixtures through Explainable and Anomalous Machine Learning

Abstract

This paper presents a framework for integrating Explainable and Anomalous Machine Learning (EAML) into a digital twin to enable finetuning of mixtures as a mean to realize next-gen concretes with favorable performance. In this framework, both anomalous unsupervised and explainable supervised ML algorithms are joined to create a virtual assistant capable of exploring the influence of mixture materials and proportions on the required performance of concrete. This virtual assistant is not only trained to detect inherent vulnerabilities within mixtures but can also finetune such mixtures to overcome potential weaknesses – especially when concrete is expected to serve under extreme loading conditions. The proposed framework has been rigorously examined on three case studies to identify vulnerable mixtures to: 1) fire-induced spalling, 2) chloride penetration, and 3) failing to attain full design strength in job sites, using small and large datasets comprised from actual measurements. Results from our analysis show how the proposed framework was capable of identifying vulnerable concrete mixtures and of satisfying various performance metrics. While the proposed framework is designed to be algorithm-independent and hence can be scalable across multiple platforms, this work showcases the application of anomaly detecting and clustering algorithms, together with an ensemble of classifiers encompassing extreme and light gradient boosted trees (GBT), generalized additive models (GAM), and keras deep residual neural network (KDP).