Reliability of probabilistic numerical data for training machine learning algorithms to detect damage in bridges

Abstract

Summary In structural health monitoring of bridges, machine learning algorithms for damage detection are typically trained using an unsupervised learning strategy, with data gathered from monitoring systems, and assuming the structures are undamaged and functioning under normal operational conditions during a certain period of time. However, the scarcity of information regarding the structural response under seasonal environmental variations and less frequent operational conditions makes the distinction between these undamaged states and damaged ones very challenging and may cause damage detection algorithms to yield false indications. To overcome this limitation, hybrid approaches for the training of machine learning algorithms have recently been proposed. Rather than relying exclusively on monitoring data, hybrid approaches use finite element models of the structure to generate numerical data for less frequent undamaged scenarios. The numerical data are used for the training of machine learning algorithms together with the monitoring data. This paper addresses the reliability of numerical data for the training of machine learning algorithms by quantifying the damage detection performance of an algorithm trained with numerical data only. Monitoring data are used only for the initial calibration of the finite element model, which does not need to be exceedingly detailed, as the probabilistic variation of the uncertain parameters is considered. The damage detection performance is quantified both in terms of quality (number of ill‐classified observations) and robustness to sub‐optimal choices of the training data and algorithmic parameters. A general procedure for the generation of model‐based data for the training of machine learning algorithms to detect damage is given and validated using the well‐known Z‐24 Bridge benchmark.

Three decades of statistical pattern recognition paradigm for SHM of bridges

“…To solve it, a procedure is encoded as an iterative constrained minimization. In each calibration step, the calibration parameters are tuned to minimize the error function, while ensuring that the values of the calibration parameters remain within the limits of what is physically plausible [41]. Therefore, in the last decade, a hybrid approach aimed at combining the best capabilities of the physics-and data-based approaches has emerged.…”

“…(iii) How to generate FE data in a systematic, reliable way? To answer those questions, Bud et al [41] assessed the performance of machine learning for damage detection based on a Gaussian mixture model trained only with numerical data from a FE model, which does not need to be precise, as the probabilistic variation of uncertain parameters is considered. The number of uncertain parameters far exceeds that considered in reference [32] and the FE model is shifted from deterministic to probabilistic to allow a greater coverage of their combinations.…”

“…In each calibration step, the calibration parameters are tuned to minimize the error function, while ensuring that the values of the calibration parameters remain within the limits of what is physically plausible. 41…”

“…To answer those questions, Bud et al 41 assessed the performance of machine learning for damage detection based on a Gaussian mixture model trained only with numerical data from a FE model, which does not need to be precise, as the probabilistic variation of uncertain parameters is considered. The number of uncertain parameters far exceeds that considered in Ref.…”

“…In each calibration step, the calibration parameters are tuned to minimize the error function, while ensuring that the values of the calibration parameters remain within the limits of what is physically plausible. 41 Therefore, in the last decade, a hybrid approach aimed at combining the best capabilities of the physics-and databased approaches has emerged. This framework leverages the benefits of both approaches while mitigating their shortcomings.…”