Structural health monitoring

About: Structural health monitoring is a research topic. Over the lifetime, 11727 publications have been published within this topic receiving 186231 citations.

Wireless intelligent sensor network for autonomous structural health monitoring

Abstract: Life cycle monitoring of civil infrastructure such as bridges and buildings is critical to the long-term operational cost and safety of aging structures. The widespread use of Structural Health Monitoring (SHM) systems is limited due to unavailability of specialized data acquisition equipment, high cost of generic equipment, and absence of fully automatic decision support systems. The goals of the presented project include: first, design of a Wireless Intelligent Sensor and Actuator Network (WISAN) and creation of an inexpensive set of instrumentation for the tasks of structural health monitoring; second, development of a SHM method, which is suitable for autonomous structural health monitoring. The design of the wireless sensor network is aimed at applications of structural health monitoring, addressing the issues of achieving a low cost per sensor, higher reliability, sources of energy for the network nodes, energy-efficient distribution of the computational load, security and coexistence in the ISM radio bands. The practical applicability of the sensor network is increased through utilization of computational intelligence and support of signal generation capabilities. The automated SHM method is based on the method of modal strain energy, though other SHM methods will be supported as well. The automation tasks include automation of the modal identification through ambient vibrations, classification of the acquired mode shapes, and automatic evaluation of the structural health.

Modal Flexibility Analysis of Cable-Stayed Ting Kau Bridge for Damage Identification

Abstract: : The cable-stayed Ting Kau Bridge has been permanently instrumented with more than 230 sensors for long-term structural health monitoring. In this article, the feasibility of using the measured dynamic characteristics of the bridge for damage detection is studied. Making use of a validated three-dimensional (3D) finite element model (FEM), modal flexibility matrices of the bridge are constructed using a few truncated modes and incomplete modal vectors at the sensor locations. The relative flexibility change (RFC) between intact and damage states is then formulated as an index to locate damage. The applicability of this flexibility index for damage location in the cable-stayed bridge is examined by investigating various damage scenarios including those at stay cables, longitudinal stabilizing cables, bearings and supports, longitudinal girders and cross girders, and taking into account measurement noise in modal data. The influence of two ambient factors, that is, temperature change and traffic loading, on the damage detectability is also analyzed by approximately considering an equivalent alteration in the material and structural behaviors. It is revealed that in the absence of ambient effects the RFC index performs well for locating damage of different severities in single-damage cases. In multi-damage cases the RFC index may provide false-negative identification for damage at the members with low sensitivity. Eliminating ambient effects is requisite for reliable detection of damage at stay cables and cross girders. The capability of the RFC index for locating damage at cross girders is significantly dropped in the presence of measurement noise.

A clustering approach for structural health monitoring on bridges

Abstract: Structural health monitoring is a process for identifying damage in civil infrastructures using sensing system. It has been increasingly employed due to advances in sensing technologies and data analytic using machine learning. A common problem within this scenario is that limited data of real structural faults are available. Therefore, unsupervised and novelty detection machine learning methods must be employed. This work presents a clustering based approach to group substructures or joints with similar behaviour on bridge and then detect abnormal or damaged ones, as part of efforts in applying structural health monitoring to the Sydney Harbour Bridge, one of iconic structures in Australia. The approach is a combination of feature extraction, a nearest neighbor based outlier removal, followed by a clustering approach over both vibration events and joints representatives. Vibration signals caused by passing vehicles from different joints are then classified and damaged joints can be detected and located. The validity of the approach was demonstrated using real data collected from the Sydney Harbour Bridge. The clustering results showed correlations among similarly located joints in different bridge zones. Moreover, it also helped to detect a damaged joint and a joint with a faulty instrumented sensor, and thus demonstrated the feasibility of the proposed clustering based approach to complement existing damage detection strategies.

Predictive modeling of nonlinear wave propagation for structural health monitoring with piezoelectric wafer active sensors

Abstract: This article presents predictive modeling of nonlinear guided wave propagation for structural health monitoring using both finite element method and analytical approach. In our study, the nonlinearity of the guided waves is generated by interaction with a nonlinear breathing crack. Two nonlinear finite element method techniques are used to simulate the breathing crack: (a) element activation/deactivation method and (b) contact analysis. Both techniques are available in ANSYS software package. The solutions obtained by these two finite element method techniques compare quite well. A parametric analytical predictive model is built to simulate guided waves interacting with linear/nonlinear structural damage. This model is coded into MATLAB, and the WaveFormRevealer graphical user interface is developed to obtain fast predictive waveform solutions for arbitrary combinations of sensor, structural properties, and damage. The predictive model is found capable of describing the nonlinear wave propagation phenomen...