Abstract: Autonomous structural health monitoring (SHM) of many structures and bridges became a topic of paramount importance for maintenance purposes and safety reasons. This paper proposes a set of machine learning (ML) tools to perform automatic feature selection and detection of anomalies in a bridge from vibrational data and compare different feature extraction schemes to increase the accuracy and reduce the amount of data collected. As a case study, the Z-24 bridge is considered because of the extensive database of accelerometric data in both standard and damaged conditions. The proposed framework starts from the first four fundamental frequencies extracted through operational modal analysis (OMA) and clustering, followed by time-domain filtering (tracking). The fundamental frequencies extracted are then fed to a dimensionality reduction block implemented through two different approaches: feature selection (intelligent multiplexer) that tries to estimate the most reliable frequencies based on the evaluation of some statistical features (i.e., entropy, variance, kurtosis), and feature extraction (auto-associative neural network (ANN)) that combine the fundamental frequencies to extract new damage sensitive features in a low dimensional feature space. Finally, one-class classification (OCC) algorithms perform anomaly detection, trained with standard condition points, and tested with normal and anomaly ones. In particular, principal component analysis (PCA), kernel principal component analysis (KPCA), and autoassociative neural network (ANN) are presented and their performance are compared. It is also shown that, by evaluating the correct features, the anomaly can be detected with accuracy and an F1 score greater than 95%.
Abstract: The early-stage damage detection in offshore
structures requires continuous structural health monitoring and for the
large area the position of sensors will also plays an important role in
the efficient damage detection. Determining the dynamic behavior of
offshore structures requires dense deployment of sensors. The wired
Structural Health Monitoring (SHM) systems are highly expensive
and always needs larger installation space to deploy. Wireless sensor
networks can enhance the SHM system by deployment of scalable
sensor network, which consumes lesser space. This paper presents the
results of wireless sensor network based Structural Health Monitoring
method applied to a scaled experimental model of offshore structure
that underwent wave loading. This method determines the
serviceability of the offshore structure which is subjected to various
environment loads. Wired and wireless sensors were installed in the
model and the response of the scaled BLSRP model under wave
loading was recorded. The wireless system discussed in this study is
the Raspberry pi board with Arm V6 processor which is programmed
to transmit the data acquired by the sensor to the server using Wi-Fi
adapter, the data is then hosted in the webpage. The data acquired
from the wireless and wired SHM systems were compared and the
design of the wireless system is verified.