Structural Health Monitoring in Civil Engineering Using Artificial Intelligence, Machine Learning and Novel Sensor Technologies

Dear Colleagues,

Many countries throughout the world are seriously struggling to quantify and ensure the security of their built heritages and aging infrastructure networks. These buildings are frequently exposed to hazardous environmental conditions, which decreases population safety. Therefore, engineers must be able to monitor the performance of existing structures and monuments throughout their lifetime for socioeconomic reasons related to the fact that construction and rehabilitation are very expensive processes. The technology of the Internet of Things (IoT) will make it possible for industrial and civil engineering to optimize maintenance operations, reduce operational costs, and (continuously) monitor parts and structures in order to assess their structural health, predict failures, and increase user safety. There are two alternative monitoring approaches in civil engineering: periodic on-demand measurements or the ongoing monitoring of relevant parameters. The work of continuous monitoring is quite difficult and costly, particularly when there are a lot of measurement nodes. Although sporadic monitoring is simpler to set up, real-time structural monitoring is not possible with this technique. Furthermore, the measurements must be taken by an operator on-location, which is costly. Recent advancements in sensor technologies have led to many low-cost but efficient solutions for procuring long-term monitoring data from instrumented structural systems.

This Special Issue’s aim is to investigate creative techniques and intelligent practices for structural health monitoring (SHM). It will cover a wide array of subjects, from dealing with SMH to artificial intelligence (AI) in civil engineering. Special attention will be paid to the development of novel sensor technologies, for continuous SHM and smart concrete applications, including  SAW-integrated sensors and distributed optical fiber sensors. The impact of climate extremes on the performance and long-term viability of built heritage will also be a focus.

Manuscript Submission Information

Authors are invited to send their original research findings regarding SHM technologies, the development and use of advanced monitoring and sensor technologies, and civil engineering applications of advanced AI, such as machine learning, deep learning, and computer vision.

Papers that promote knowledge are of relevance and interest, provided that they include an analysis of the experimental data and proper conclusions.

Dr. Firas Al Mahmoud
Guest Editor

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. Materials is an international peer-reviewed open access semimonthly 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.

• advanced SHM algorithms
• artificial intelligence
• machine learning
• infrastructure safety
• smart and mobile sensor systems
• strain monitoring
• control systems
• data acquisition
• transmission system
• diagnosis
• damage detection
• maintenance systems