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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 3521

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Special Issue Editor

Dr. Chengyu Hong

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Guest Editor

College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China
Interests: geotechnical monitoring, development of innovative sensing technologies
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The increasing frequency of harsh environmental and climatic conditions and long design reference periods have had an adverse impact on civil infrastructures, including buildings, railways, bridges, tunnels, highways, airports, pipelines, roads, power plants, industrial facilities, geotechnical and hydraulic structures, and others. Such events can cause several geotechnical health problems, leading to settlement, cracking, slope instability, deformations, and many more issues. Accordingly, it is critical to accurately and precisely monitor the mechanical behavior of civil infrastructures. Civil infrastructure monitoring with the aim of early damage detection and acquiring the data required for engineering construction not only prevents sudden infrastructure collapse but also increases service life and sustainability. Currently, various sensing technologies are being rapidly developed in line with the deeper and more extensive research on new principles, new materials and new technologies. In view of its higher accuracy, smaller volume and stronger adaptability, novel sensor technology has a significant role in satisfying the high requirements for resilient and sustainable civil infrastructures. Various types of novel sensors with new principles, new materials or new technologies, including the following, would provide new ways to assist in the green and high-quality development of civil engineering infrastructures:

Graphene-based sensors;
Piezoceramic sensors;
Semi-distributed/distributed optical fiber sensors;
FBG-based 3D-printed/FBG sensors;
Image-/video-/laser-based sensors;
Smart/intelligent sensing methods;
Multi-sensor fusion methods;
IoT-based monitoring technology.

Dr. Chengyu Hong
Guest Editor

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Keywords

civil infrastructure monitoring
FBG sensors
multi-sensor fusion

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Published Papers (4 papers)

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Research

20 pages, 3004 KiB

Open AccessArticle

An Evaluation of the Acoustic Activity Emitted in Fiber-Reinforced Concrete Under Flexure at Low Temperature

by Omar A. Kamel, Ahmed A. Abouhussien, Assem A. A. Hassan and Basem H. AbdelAleem

Sensors 2025, 25(9), 2703; https://doi.org/10.3390/s25092703 - 24 Apr 2025

Viewed by 199

Abstract

This study investigated the changes in the acoustic emission (AE) activity emitted in fiber-reinforced concrete (FRC) under flexure at two temperatures (25 °C and −20 °C). Seven concrete mixtures were developed with different water-binder ratios (w/b) (0.4 and 0.55), different fiber materials (steel fiber (SF) and synthetic polypropylene fiber (Syn-PF)), different fiber lengths (19 mm and 38 mm), and various Syn-PF contents (0%, 0.2%, and 1%). Prisms with dimensions of 100 × 100 × 400 mm from each mixture underwent a four-point monotonic flexure load while collecting the emitted acoustic waves via attached AE sensors. AE parameter-based analyses, including b-value, improved b-value (Ib-value), intensity, and rise time/average signal amplitude (RA) analyses, were performed using the raw AE data to highlight the change in the AE activity associated with different stages of damage (micro- and macro-cracking). The results showed that the number of hits, average frequency, cumulative signal strength (CSS), and energy were higher for the waves released at −20 °C compared to those obtained at 25 °C. The onset of the first visible micro- and macro-cracks was noticed to be associated with a significant spike in CSS, historic index (H (t)), severity (S_r) curves, a noticeable dip in the b-value curve, and a compression in bellows/fluctuations of the Ib-value curve for both testing temperatures. In addition, time and load thresholds of micro- and macro-cracks increased when samples were cooled down and tested at −20 °C, especially in the mixtures with higher w/b, longer fibers, and lower fiber content. This improvement in mechanical performance and cracking threshold limits was associated with higher AE activity in terms of an overall increase in CSS, S_r, and H (t) values and an overall reduction in b-values. In addition, varying the concrete mixture design parameters, including the w/b ratio as well as fiber type, content, and length, showed a significant impact on the flexural behavior and the AE activity of the tested mixtures at both temperatures (25 °C and −20 °C). Intensity and RA analysis parameters allowed the development of two charts to characterize the detected AE events, whether associated with micro- and macro-cracks considering the temperature effect. Full article

(This article belongs to the Special Issue Novel Sensor Technologies for Civil Infrastructure Monitoring)

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22 pages, 6678 KiB

Open AccessArticle

Monitoring Excavation-Induced Deformation of a Secant Pile Wall Using Distributed Fiber Optic Sensors

by Chengyu Hong, Chengkai Xu, Weibin Chen, Jianwei Liu and Junkun Tan

Sensors 2025, 25(1), 254; https://doi.org/10.3390/s25010254 - 4 Jan 2025

Viewed by 855

Abstract

This paper investigates the use of the BOTDA (Brillouin Optical Time-Domain Analysis) technology to monitor a large-scale bored pile wall in the field. Distributed fiber optic sensors (DFOSs) were deployed to measure internal temperature and strain changes during cement grouting, hardening, and excavation-induced deformation of a secant pile wall. The study details the geological conditions and DFOS installation process. During grouting, the temperature increased by approximately 69 °C due to cement hydration 30 min post-grouting, while the strain decreased by 0.5% on average due to cement slurry shrinkage. During excavation, the temperature changes were minimal, but the excavation depth significantly influenced the strain distribution, with continuous compressive deformation observed in two monitored boreholes. Two analytical methods, the numerical integration method (NIM) and the finite difference method (FDM), were used to calculate the lateral pile displacement based on the monitored strain data. The results were compared with previous monitoring data, showing that the lateral displacement of the pile was minimal after excavation and was attributed to the high stiffness of the secant pile wall. This study demonstrates the effectiveness of DFOSs and BOTDA technology for monitoring complex pile wall behaviors during construction. Full article

(This article belongs to the Special Issue Novel Sensor Technologies for Civil Infrastructure Monitoring)

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15 pages, 2784 KiB

Open AccessArticle

Analytical Equations for the Prediction of the Failure Mode of Reinforced Concrete Beam–Column Joints Based on Interpretable Machine Learning and SHAP Values

by Ioannis Karampinis, Martha Karabini, Theodoros Rousakis, Lazaros Iliadis and Athanasios Karabinis

Sensors 2024, 24(24), 7955; https://doi.org/10.3390/s24247955 - 12 Dec 2024

Viewed by 813

Abstract

One of the most critical components of reinforced concrete structures are beam–column joint systems, which greatly affect the overall behavior of a structure during a major seismic event. According to modern design codes, if the system fails, it should fail due to the flexural yielding of the beam and not due to the shear failure of the joint. Thus, a reliable tool is required for the prediction of the failure mode of the joints in a preexisting population of structures. In the present paper, a novel methodology for the derivation of analytical equations for this task is presented. The formulation is based on SHapley Additive exPlanations values, which are commonly employed as an explainability tool in machine learning. Instead, in the present paper, they were also utilized as a transformed target variable to which the analytical curves were fitted, which approximated the predictions of an underlying machine learning model. A dataset comprising 478 experimental results was utilized and the eXtreme Gradient Boosting algorithm was initially fitted. This achieved an overall accuracy of ≈

84 %

. The derived analytical equations achieved an accuracy of ≈

78 %

. The corresponding metrics of precision, recall, and the F1-score ranged from ≈

76 %

to ≈

80 %

and were close across the two modes, indicating an unbiased model. Full article

(This article belongs to the Special Issue Novel Sensor Technologies for Civil Infrastructure Monitoring)

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26 pages, 24158 KiB

Open AccessArticle

Onsite Seismic Monitoring Behavior of Undamaged Dams During the 2023 Kahramanmaraş Earthquakes (M7.7 and M7.6)

by Alemdar Bayraktar, Mehmet Akköse, Carlos E. Ventura, Tony Y. Yang and Emin Hökelekli

Sensors 2024, 24(21), 6856; https://doi.org/10.3390/s24216856 - 25 Oct 2024

Viewed by 991

Abstract

On 6 February 2023, two major earthquakes struck Türkiye, with their epicenters in the Pazarcık (M7.7; focal depth: 8.6 km) and Elbistan (M7.6; focal depth: 7 km) districts of Kahramanmaraş city. Most of the dams in the earthquake region remained structurally safe and stable. However, 17 dams in Türkiye and 1 dam in Syria were damaged during the 2023 Kahramanmaraş earthquakes. The main objective of this study was to better understand the real seismic behaviors of the dams during the two mainshocks and significant aftershocks. An earthfill dam, a concrete-faced rockfill dam (CFRD), and a roller-compacted concrete (RCC) dam constructed in the disaster area were selected to identify the real seismic behaviors of different types of dams during strong earthquakes. Acceleration records measured at the crest, right and left abutments, and foundations of the selected dams during the 2023 Kahramanmaraş earthquakes were taken into account to determine the real seismic behavior of the dams before, during, and after the earthquakes. The results of this investigation provide valuable insights into the real seismic behaviors of different types of dams in the vicinity of fault lines during strong earthquakes. Full article

(This article belongs to the Special Issue Novel Sensor Technologies for Civil Infrastructure Monitoring)

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