In the age of the smart city, things like the Internet of Things (IoT) and big data analytics are
making big changes to the way traditional structural health monitoring (SHM) is done. Also …

The challenges of urban administration are growing, as the population, automobiles, and
cities rise. Making cities smarter is thus one of the most effective solutions to urban issues. A …

Statistical pattern recognition has recently emerged as a promising set of complementary
methods to system identification for automatic structural damage assessment. Its essence is …

Structural health monitoring (SHM) systems are used to analyze the health of infrastructures
such as bridges, using data from various types of sensors. While SHM systems consist of …

The main scope of the research is to develop an integrated monitoring system for durability
assessment of bridges. The system must interface and integrate the actual practice mainly …

Structural health monitoring systems have been employed throughout history to assess the
structural responses of bridges to both natural and man-made hazards. Continuous …

A signal‐based pattern‐recognition approach is used for structural damage diagnosis with a
single or limited number of input/output signals. The approach is based on extraction of the …

In this paper, an approach based on the synergistic use of proper orthogonal decomposition
and Kalman filtering is proposed for the online health monitoring of damaged structures. The …

Time series analysis is being used popularly in structural health monitoring mainly because
of its output‐only and non‐modal approach. Generally, the damage features are extracted …

Statistical and machine learning analysis of structural health monitoring data is a popular
approach for efficient structural level defect detection. However, the application of these …