Non-parametric empirical machine learning for short-term and long-term structural health monitoring

Abstract

Early damage detection is an initial step of structural health monitoring. Thanks to recent advances in sensing technology, the application of data-driven methods based on the concept of machine learning has significantly increased among civil engineers and researchers. On this basis, this article proposes a novel non-parametric anomaly detection method in an unsupervised learning manner via the theory of empirical machine learning. The main objective of this method is to define a new damage index by using some empirical measure and the concept of minimum distance value. For this reason, an empirical local density is initially computed for each feature and then multiplied by the minimum distance of that feature to derive a new damage index for decision-making. The minimum distance is obtained by calculating the distances between each feature and training samples and finding the minimum quantity. The major contributions of this research contain developing a novel non-parametric algorithm for decision-making under high-dimensional and low-dimensional features and proposing a new damage index. To detect early damage, a threshold boundary is computed by using the extreme value theory, generalized Pareto distribution, and peak-over-threshold approach. Dynamic and statistical features of two full-scale bridges are used to verify the effectiveness and reliability of the proposed non-parametric anomaly detection. In order to further demonstrate its accuracy and proper performance, it is compared with some classical and recently published anomaly detection techniques. Results show that the proposed non-parametric method can effectively discriminate a damaged state from its undamaged condition with high damage detectability and inconsiderable false positive and false negative errors. This method also outperforms the anomaly detection techniques considered in the comparative studies.

Review of artificial intelligence-based bridge damage detection

“…The experimental results showed that the total error of the proposed method in damage detection was less than 1.86%. Entezami et al 139 proposed a novel non-parametric anomaly detection method based on empirical machine learning theory. Some empirical measurements and minimum distance values were used to define the new damage index.…”

Online Hybrid Learning Methods for Real-Time Structural Health Monitoring Using Remote Sensing and Small Displacement Data

“…To make the final decision about the current state of the structure in the real-time damage detection scheme, it only suffices to determine a novelty score. Unlike most of the unsupervised anomaly detection techniques [10,11,25], the proposed online learning methods do not explicitly use an alarming threshold to compare novelty score with this limit and make a decision. In other words, the threshold estimation is incorporated into the algorithm of the ODTL algorithm, as shown in Figure 3.…”

“…Generally, there are numerous methods for implementing an SHM project depending upon the type of sensing technology and data acquisition system (i.e., contact-based vs. noncontact-based sensors) [4][5][6], the type of data (i.e., acceleration time histories, displacements, strains, images, videos, etc.) [5,7], the type of civil structure and built environment, the type of data processing, the type of computational/statistical method (i.e., model-driven vs. data-driven) [8,9], and the number of measurements (i.e., short-term vs. long-term [10][11][12]), etc. Despite most of the aforementioned topics have been evaluated properly, the issue of data processing of an SHM system needs further research.…”

Damage Detection in Largely Unobserved Structures under Varying Environmental Conditions: An AutoRegressive Spectrum and Multi-Level Machine Learning Methodology

“…Structural health monitoring (SHM) systems provide means to assess the health and safety of civil, mechanical, and aerospace structures by exploiting various data such as vibration responses (e.g., acceleration time histories, modal data, strain, etc.) [ 1 , 2 , 3 , 4 , 5 ], images [ 6 , 7 ], and videos [ 8 , 9 ]. The primary step of SHM is the evaluation of the state of the monitored structure for damage detection purposes: this is known as early damage detection.…”