Recent Advances in Structural Health Monitoring of Buildings and Infrastructures

Dear Colleagues,

Due to the rapid development of sensing technologies and data analysis methods in recent years, the structural health monitoring (SHM) of buildings and infrastructures has become exceedingly popular in the last decade.

In this regard, this Special Issue “Recent advances in structural health monitoring of buildings and infrastructures” aims to present novel technologies and methodologies used to study buildings and infrastructures via SHM, such as sensing techniques, data processing, the machine learning of high-rise buildings, bridges, tunnels, and dams, etc. Both new methodologies and technological advancements are welcome, as well as specific laboratory or in situ experimental studies or validations.

This Special Issue provides an integrated view of the problems associated with the health monitoring of buildings under construction and the structural damage identification of in-service buildings and infrastructures.

Prof. Minshui Huang warmly invites authors to submit their papers for potential inclusion in this Special Issue on structural health monitoring of buildings and infrastructures under construction and in operation.

Prof. Dr. Minshui Huang
Dr. Jianfeng Gu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Buildings is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• damage identification
• inverse problems in structural engineering
• deep learning in SHM
• smartphone sensing, sensor network, optimal sensor placement and instrumentation design
• environmental and operational factors in SHM
• static load testing and vibration testing of structure
• swarm intelligence algorithms
• sustainable developments in SHM
• other related topics in SHM

• Construction Process Monitoring and Structural Damage Identification for Buildings and Bridges in Buildings (18 articles)

Title: Deep Learning-Based Strong Ground Motion Signal Prediction for Real-Time Structural Control
Authors: Mohammad AlHamaydeh; Sara Tellab; Usman Tariq
Affiliation: Department of Civil Engineering, College of Engineering, American University of Sharjah, Sharjah, P.O. Box 26666, UAE, ORCID ID: 0000-0002-5004-0778
Abstract: Processing ground motion signals at early stages can be advantageous for issuing public warnings, deploying first-responders’ teams, and other time-sensitive measures. Multiple Deep Learning (DL) models are presented herein, which can predict triaxial ground motion accelerations upon processing the first arriving 0.5 sec of recorded acceleration measurements. Principal Component Analysis (PCA) and the k-means clustering algorithm were utilized to cluster 17,602 accelerograms into three clusters using their metadata. The accelerograms were divided into 1 million input-output pairs for training, 100,000 pairs for validation, and 420,000 pairs for testing. Several non-overlapping forecast horizons were explored (1, 10, 50, 100, and 200 points). Various architectures of Artificial Neural Networks (ANNs) were trained and tested, like Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) networks, and CNN-LSTMs. The utilized training methodology applied different aspects of supervised and unsupervised learning. The LSTM model demonstrated superior performance in terms of short-term prediction. A prediction horizon of 10 timesteps in the future with a Root Mean Squared Error (RMSE) value of 8.43 x10-6 g was achieved. In other words, the LSTM model exhibited a performance improvement of 95% compared to the baseline benchmark, i.e., ANN. It is worth noting that all the considered models exhibited acceptable real-time performance (0.01 s) when running in testing mode. The CNN model demonstrated the fastest computational performance among all models. It predicts ground accelerations in under 0.5 ms on an Intel Core i9-10900X CPU (10 cores). The models allow for the implementation of real-time structural control responses via intelligent seismic protection systems (e.g., Magneto-rheological (MR) dampers).

Title: The Sustainable Development of Bridges in China: Collapses Cause Analysis, Existing Management Dilemmas and Potential Solutions
Authors: Dina Tang
Affiliation: College of Post and Telecommunication of WIT, Wuhan 430073, China
Abstract: The construction of sustainable bridge projects is a global mission and challenge in the 21st century. However, in recent years, there have been occurrences of bridge collapses due to various factors during the construction phase and the service phase. These incidents have resulted in significant loss of lives and property damages, which exacerbated the five sustainable development issues that bridge engineering is facing, namely, natural, resource, environmental, social, and economic factors. Preventing and addressing bridge collapse accidents have attracted the attention of many professionals, research institutions, and government departments, and it is a prominent research area. Based on the concept of sustainable development of bridge engineering, this article categorizes the causes of bridge collapses into two types: the causes during construction phase and the cause during service phase (including lack of inspection, maintenance and management, external natural factors and human factors). Then, from the perspective of regulation, the dilemmas in existing national management framework that may hinder its ability to effectively are investigated in detail. Finally, several effective suggestions for the prevention of bridge collapse are proposed, which can effectively motivate governments, owners, designers, constructors, managers, and users to actively develop and promote high-quality sustainable bridges.