Structural Health Monitoring and Performance Evaluation of Bridges and Structural Elements

Bridges and other structures that support critical services necessary for communities constitute an important part of surface infrastructure. On the other hand, the aging of vast infrastructure constructed during the development of world economies has resulted in a large number of critical structures and systems in need of repair or replacement because of structural or functional deficiencies. It is important to have means and methods for health monitoring and performance evaluation of the existing structures so that their safety can be evaluated and preventive maintenance be performed on time, long before the extent of the damage necessitates drastic actions. This is even more important for the resilience of communities that are prone to natural hazards and need quick recovery after each extreme event. As the many bridge failures over the past few decades have shown, conventional and routine bridge monitoring is insufficient to effectively evaluate the safety and that more effective methods for damage detection and structural monitoring are needed. At the same time, condition assessment and performance evaluation of bridges and structures will allow the decision-makers to allocate their assets on a priority basis for the hardening of inadequate structures for better resiliency.

Research and development activities in recent years have focused on two distinct areas: one targeting improvements in ability for assessment of condition via structural health monitoring (SHM) and non-destructive evaluation (NDE) as well as other methods for addressing deficiencies with repair or rehabilitation, and the other is to learn from the past and to design and construct low-maintenance, economic, smart, and durable alternatives for replacement and/or new structures. In any case, condition assessment and decision-making for prioritizing maintenance programs require the availability of fast, reliable, accurate, cost-effective, and meaningful data with the lowest cost possible.

As it relates to condition assessment and health monitoring of structures, there have been two major approaches: one is the employment of sensors embedded during or installed after construction and the collection of data from sensors continuously or periodically, and the second is the use of non-destructive evaluation (NDE) methods periodically for the collection of data in relation to the current condition of the existing structures. Researchers are attempting to develop the ideal fast, reliable, accurate, and low-cost, on-demand sensing systems and sensors. Such an “ideal” system should be able to combine the advantages introduced by a vast sensor network within the body of the structure with the ease and effectiveness equivalent to or surpassing that of NDE methods, and at the same time to keep the cost at an affordable level. The structural health monitoring and condition assessment have evolved significantly in recent years with the introduction of innovative sensors, data communication, non-destructive evaluation, data processing, and methods of delivery. Innovative approaches to health monitoring and condition assessment, along with new approaches to maintenance, are therefore much in demand.

Despite many advances in instrumentation and structural health monitoring of structures, the existing process with which the sensors are installed and operated and the way reliable data can be collected and interpreted are still cumbersome, slow, and, more importantly, very costly. Several issues still need addressing that include accessibility, disruption of traffic, short service life and initial cost of sensors, costly wiring for wired sensors and power requirements for wireless sensors, high maintenance, disruption in data collection, aging and damage to sensors, and dealing with a large amount of data, making the interpretation and decision-making a cumbersome process.

This Special Issue [1] compiles articles on a wide range of topics related to existing and new non-destructive evaluation (NDE) methods, structural health monitoring (SHM) and damage detection techniques applicable to bridges and other structures, new algorithms for data interpretation, and performance evaluation through load testing and analysis. Methods including, but not limited to, hands-on non-destructive testing (NDT), non-contact or vision-based sensors and instrumentation, load testing, and vibration or modal analysis are also covered. This issue [1] also includes new methods and algorithms of data processing required for condition assessment and performance evaluation methods and maintenance approaches that use the results of NDE and SHM to devise preventive and preservation tactics. Recent interest in machine learning (ML) and deep learning (DL) algorithms for structural damage identification and the use of convolutional neural networks (CNNs) for damage detection have also been highlighted.

1. A Snapshot of Articles and Topics Included in This Issue

The topics included in this issue [1] can be divided into structural health monitoring and damage detection, data processing for damage detection, performance evaluation through load testing and analysis, and damage prevention and remediation.

Around the topic of damage detection, in their article, Hasani and Freddi (Contribution 1) offer a comprehensive and clear overview of the entire process of conducting an operational modal analysis on bridges, including data collection, preprocessing, and the use of various modal identification techniques in both the time and frequency domains. They also introduce advanced methods to address challenges faced in earlier approaches. Their paper stands out for its detailed evaluation of different methodologies, highlighting their pros and cons, and providing practical examples of their application. In their paper, Tabiatnejad et al. (Contribution 2) examine damage detection in the tendons of post-tensioned segmental box girder bridges using a vibration-based technique paired with the Precursor Transformation Matrix (PTM). Their research combines theoretical modeling, FEM simulations, and empirical data to evaluate PTM’s effectiveness. Their findings demonstrate that PTM, combined with vibration analysis, enhances damage detection and localization, offering a faster and more cost-effective method for maintaining bridge safety and longevity. Bakalis et al. (Contribution 3) apply the “M and P” hybrid technique to identify seismic damage in reinforced concrete bridges. Their method combines monitoring with pushover analysis to track changes in the bridge’s eigenfrequency as deck displacement increases. They tested the approach on a four-span bridge, demonstrating its effectiveness in detecting damage in bridge piers.

In relation to data processing for damage detection, Rodrigues-Quinonez et al. (Contribution 4) introduce in their paper a non-invasive structural health monitoring (SHM) method using inertial sensors and signal conditioning techniques. Their approach employs an IMU (inertial measurement unit) with a tri-axial accelerometer and gyroscope to continuously measure structural displacements caused by seismic vibrations. They implemented the “Inertial Displacement Monitoring System” (IDMS) using a Kalman filter to reduce signal noise, a Chebyshev filter to isolate seismic frequencies, and a zero-velocity observation update (ZVOB) algorithm to detect vibrations and minimize external disturbances. They show that IDMS effectively measured displacements during seismic events, providing timely information for detecting structural failures. A study by Valtierra-Rodriguez et al. (Contribution 5) introduces a method that combines vibration signal fractality with autoencoders to detect corrosion in truss-type bridges. In this method, vibration signals are analyzed using fractal dimension algorithms, and the results are processed by autoencoders to identify damage. Tested on a truss bridge model, the method accurately detected corrosion at various stages, achieving 99.8% accuracy. Ngeljaratan et al. (Contribution 6) in their article evaluate and implement feature detection and tracking algorithms for vision-based structural health monitoring of bridges, and test on a two-span bridge during a large-scale shake table test. They compare blob-based detectors (SIFT, SURF, KAZE) and model-fitting algorithms (LMEDS, LTS, RANSAC, MSAC) for accuracy and outlier removal. The seismic responses were analyzed and validated with mechanical sensors, showing strong agreement with the proposed methods and confirming their effectiveness. The paper by Arnold and Keller (Contribution 7) introduces a new bridge weigh-in-motion (BWIM) method using deep learning and ground-based radar (GBR) data. Their study used GBR and UAV data to monitor a bridge in Germany, identifying vehicle characteristics like axle count and spacing. They applied machine learning techniques to displacement data, achieving 76.7% accuracy in truck axle count classification and suggesting that GBR is a viable alternative to conventional BWIM systems. In the same direction, the paper by Ameli et al. (Contribution 8) focuses on improving corrosion detection in steel bridges using deep learning. They created a new dataset of 514 annotated images and trained two algorithms, Mask RCNN and YOLOv8, to segment and rate corrosion more accurately than previous methods. The models performed well, showing promise for practical applications in infrastructure assessment. Taking a new approach to data processing, Gui et al. (Contribution 9) introduce a new algorithm that enhances bridge performance assessment by integrating advanced clustering techniques with structural health monitoring (SHM). It effectively analyzes large datasets to monitor safety and serviceability, particularly for double-layer truss arch bridges. The algorithm’s accuracy and reliability were validated through case studies, showing its potential to improve maintenance decisions and support bridge preservation.

In the area of performance evaluation through testing and analysis, Brinissat et al. (Contribution 10) report in their paper a series of field tests conducted on the Szapáry motorway bridge in Hungary before its opening. Static and dynamic load tests assessed the deflections, stresses, and dynamic mode shapes of the bridge. Dynamic tests revealed how vehicle speed affected the deflections, and ambient vibration testing identified vibration modes and frequencies. They used a 3D finite-element model to simulate the bridge’s performance within design limits. Their results will be instrumental for future condition-based maintenance plans. In their article, Sayhood et al. (Contribution 11) introduce new empirical equations for predicting the shear strength of reinforced concrete deep beams, improving on existing codes. Analyzing 198 beams, their proposed model showed better accuracy and reduced variability. Their research offers a valuable tool for structural engineers, enhancing design methods and safety. Xiao et al. (Contribution 12) in their study analyze the stiffness of large-span cable-stayed suspension bridges using a wind–vehicle–bridge coupling vibration method. They developed a model to assess driving comfort and safety under different stiffness conditions and proposed a vertical stiffness limit and deflection-to-span ratio for these bridges based on engineering experience and numerical analysis. In their paper, Javed et al. (Contribution 13) investigate the use of a low-cost method with micro-concrete to study the seismic impact of improper lap splices in RC frames. They tested scaled-down models on a shake table and found that improper splices can significantly increase the risk of structural failure during earthquakes.

On damage prevention and remediation, Song and Xiao (Contribution 14) address the safety challenges when high-speed railways intersect with 1000 kV ultra-high voltage transmission lines, both key components of China’s “new infrastructure.” To ensure safety, they designed a protection scheme involving a concrete shed structure with a straight wall and flat roof to provide line-break protection, allowing for the safe and efficient construction of high-speed railway. Their approach has proven effective and can serve as a reference for similar infrastructure projects.

The Special Issue [1] has been successful in bringing together the valuable work of fourteen distinguished research groups from around the world with state-of-the-art information on structural health monitoring and performance evaluation. This collection also hints at the future direction of the SHM, NDT, damage detection, and performance evaluation. MDPI and the editors plan to embark on a sequel issue [2] to expand on SHM, NDT, and damage detection along with research on remedial and rehabilitation measures toward enabling the resiliency for the existing infrastructure as well as for future construction through the lessons learned.