A literature review of next‐generation smart sensing technology in structural health monitoring

By Sandeep Sony, Shea Laventure, Ayan Sadhu. Structural Control & Health Monitoring, 2019

528 citations recorded by the source corpus.

Advent of computationally efficient smartphones, inexpensive high‐resolution cameras, drones, and robotic sensors has brought a new era of next‐generation intelligent monitoring systems for civil infrastructure. Vibration‐based condition assessment has garnered as a prominent method of evaluating the health of large‐scale infrastructure. The use of contact‐based sensors for acquiring vibration data becomes uneconomical and tedious due to their instrumentation cost, centralized nature, and densification required to collect sufficient data for system identification of modern complex structures. A need to advance and develop alternative methods for efficient sensing system results in next‐generation measurement technology of structural health monitoring. The abundance of handheld smartphones with easily programmable framework has helped in modifying relevant software to acquire vibration data using embedded sensors in the smartphone. The inexpensive cameras have been used to capture images and videos that are utilized to understand the structural behavior with the aid of advanced signal processing techniques. The inaccessible components of structures require noncontact sensors such as unmanned aerial vehicles (UAVs) or so‐called drones and mobile sensors to acquire structural data. To the authors' knowledge, this paper first time presents a comprehensive review of a suite of next‐generation smart sensing technology that has been developed in recent years within the context of structural health monitoring. The state‐of‐the‐art methods have been presented by conducting a detailed literature review of the recent applications of smartphones, UAVs, cameras, and robotic sensors used in acquiring and analyzing the vibration data for structural condition monitoring and maintenance.

Subfields: Civil and Structural Engineering, Mechanics of Materials, Pollution

View Paper: https://www.semanticscholar.org/paper/e0d03127ead1659d877ae961434de8cb90c1d9c3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Civil and Structural Engineering

In Mechanics of Materials

Citations in the corpus

A systematic review of convolutional neural network-based structural condition assessment techniques (2021, methodology): “With the recent development of next-generation 48 sensors (Sony et al. 2019; Dabous and Feroz 2020) such as digital and high-speed cameras, 49 unmanned ground vehicles (UGVs), and mobile sensors, there has been a radical shift to non50 contact sensing techniques in SHM.”
Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights (2021, methodology): “In contrast to the previous DL papers, the recent review by Sony et al. 35 emphasizes on the convolutional neural network (CNN) application in SHM. Owning to its speci ﬁ city, major key points that were overlooked by the previous DL reviews were comprehensively covered.”