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Advances in Structural Health Monitoring of the Built Environment

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A special issue of Infrastructures (ISSN 2412-3811).

Deadline for manuscript submissions: 31 March 2025 | Viewed by 20952

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Special Issue Editors

Prof. Dr. Ahmet Emin Aktan

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Guest Editor

College of Engineering, Drexel University, 3141 Chestnut Street, Philadelphia, PA 19104, USA
Interests: infrastructures as large complex systems; condition and performance evaluation of the built environment; infrastructure management

Dr. Branko Glisic

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Guest Editor

Dpt. of Civil and Environmental Eng., Princeton University, Princeton, NJ 08544, USA
Interests: advanced sensing technologies, universal SHM methods, data analysis and management, and prognostics and the decision-making theory; smart kinetic, deployable and adaptable structures; holistic analysis of heritage structures, and engineering arts
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Dr. Carlo Rainieri

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Guest Editor

National Research Council of Italy, Construction Technologies Institute, Corso Nicolangelo Protopisani 70, 80146 Naples, Italy
Interests: operational modal analysis; vibration-based structural health monitoring; self-sensing materials; digital twin of structures and infrastructures
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Prof. Dr. Ivan Bartoli

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Guest Editor

Department of Civil, Architectural and Environmental Engineering, Drexel University, Philadelphia, PA 19104, USA
Interests: infrastructure condition assessment; non-destructive evaluation and structural health monitoring; dynamic identification; stress wave propagation modeling

Special Issue Information

Dear Colleagues,

Interest in the performance and safety of infrastructures and the built environment developed during the early 1980’s as several academics transitioned their research from disaster mitigation to resilience, sustainability and livability of the built environment. There have been significant advancements in sensing, imaging and nondestructive evaluation technologies and their applications to actual operating structures in the field, and a community of researchers experienced in health monitoring for the management of infrastructures has formed world-wide. There is also full awareness of the importance of understanding complex systems such as entire metropolitan areas with human, natural and engineered elements and leveraging cyber–physical systems for enhancing their livability, sustainability and resilience. All engineering and science disciplines must collaborate for convergent, integrative research in the development of the “intelligent city”. The infrastructure health monitoring community plays an indispensable role in “intelligent city” research, as this community has already experienced monitoring the performance of large infrastructures at the heart of complex urban systems by leveraging advanced sensing and imaging, all aspects of data science, digital twins, uncertainty and human factors. At the same time, the meaningful long-term applications of performance and health monitoring of actual infrastructures, especially in the early diagnosis of their deterioration and damage, are not widely known. The Guest Editors are interested in collecting examples of advances and applications of health and performance monitoring in recent years, especially applications to aging infrastructures as well as emerging systems such as Ocean Wind Farms and intelligent (livable, sustainable and resilient) cities. The main objective is to document the actual state-of-the-art practice, future prospects and the potential of this field of research, especially with examples on actual infrastructures by cross-disciplinary researchers.

Prof. Dr. Ahmet Emin Aktan
Prof. Dr. Branko Glisic
Dr. Carlo Rainieri
Prof. Dr. Ivan Bartoli
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. Infrastructures 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 1800 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.

Keywords

structural health monitoring (SHM)
SHM for infrastructure asset management, disaster mitigation and recovery
complex human–natural–engineered systems and their modeling
cyber–physical system applications and potential
intelligent infrastructures and cities
internet of things
data science for the health and performance monitoring of infrastructures
decision science under various types and levels of uncertainty

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Published Papers (9 papers)

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23 pages, 9797 KiB

Open AccessArticle

Enhancing Dam Safety: Statistical Assessment and Kalman Filter for the Geodetic Network of Mosul Dam

by Bashar Alsadik and Hussein Alwan Mahdi

Infrastructures 2024, 9(9), 144; https://doi.org/10.3390/infrastructures9090144 - 26 Aug 2024

Cited by 1 | Viewed by 784

Abstract

Dams play a pivotal role in providing essential services such as energy generation, water supply, and flood control. However, their stability is crucial, and continuous monitoring is vital to mitigate potential risks. The Mosul Dam is one of the most interesting infrastructures in Iraq because it was constructed on alternating beds of karstified and gypsum which required continuous grouting due to water seepage. Therefore, the ongoing maintenance issues raised international concerns about its stability. For several years the dam indicated a potential for disastrous failure that could cause massive flooding downstream and pose a serious threat to millions of people. This research focuses on comprehensive statistical assessments of the dam geodetic network points across multiple epochs of long duration. Through the systematic application of three statistical tests and the predictive capabilities of the Kalman filter, safety and long-term stability are aimed to be enhanced. The analysis of the dam’s geodetic network points shows a consistent trend of upstream-to-downstream movement. The Kalman filter demonstrates promising outcomes for displacement prediction compared to least squares adjustment. This research provides valuable insights into dam stability assessment, aligns with established procedures, and contributes to the resilience and safety of critical infrastructure. The outcome of this paper can encourage future studies to build upon the foundation presented. Full article

(This article belongs to the Special Issue Advances in Structural Health Monitoring of the Built Environment)

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18 pages, 12774 KiB

Open AccessArticle

Wolf Rock Lighthouse Long-Term Monitoring

by James Brownjohn, Alison Raby, James Bassitt, Alessandro Antonini, Zuo Zhu and Peter Dobson

Infrastructures 2024, 9(4), 77; https://doi.org/10.3390/infrastructures9040077 - 22 Apr 2024

Viewed by 1310

Abstract

Wolf Rock Lighthouse is a Victorian era masonry structure located in an extreme environment facing the fiercest Atlantic storms off the southwest coast of England whose dynamic behaviour has been studied since 2016. Initially, a modal test was used to determine modal parameters; then, in 2017, a monitoring system was installed that has operated intermittently providing response data for a number of characteristic loading events. These events have included wave loads due to storms, a small UK earthquake, helicopters landing on the helideck, and the grounding of a ship on the reef. This is believed to be the most extensive experimental campaign on any structure of this type. This paper briefly describes a unique project involving the characterisation and measurement of dynamic behaviour due to different forms of dynamic loading. Full article

(This article belongs to the Special Issue Advances in Structural Health Monitoring of the Built Environment)

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24 pages, 27653 KiB

Open AccessArticle

Enhancing the Damage Detection and Classification of Unknown Classes with a Hybrid Supervised–Unsupervised Approach

by Lorenzo Stagi, Lorenzo Sclafani, Eleonora M. Tronci, Raimondo Betti, Silvia Milana, Antonio Culla, Nicola Roveri and Antonio Carcaterra

Infrastructures 2024, 9(3), 40; https://doi.org/10.3390/infrastructures9030040 - 24 Feb 2024

Cited by 1 | Viewed by 1793

Abstract

Most damage-assessment strategies for dynamic systems only distinguish between undamaged and damaged conditions without recognizing the level or type of damage or considering unseen conditions. This paper proposes a novel framework for structural health monitoring (SHM) that combines supervised and unsupervised learning techniques to assess damage using a system’s structural response (e.g., the acceleration response of big infrastructures). The objective is to enhance the benefits of a supervised learning framework while addressing the challenges of working in an SHM context. The proposed framework uses a Linear Discriminant Analysis (LDA)/Probabilistic Linear Discriminant Analysis (PLDA) strategy that enables learning the distributions of known classes and the performance of probabilistic estimations on new incoming data. The methodology is developed and proposed in two versions. The first version is used in the context of controlled, conditioned monitoring or for post-damage assessment, while the second analyzes the single observational data. Both strategies are built in an automatic framework able to classify known conditions and recognize unseen damage classes, which are then used to update the classification algorithm. The proposed framework’s effectiveness is first tested considering the acceleration response of a numerically simulated 12-degree-of-freedom system. Then, the methodology’s practicality is validated further by adopting the experimental monitoring data of the benchmark study case of the Z24 bridge. Full article

(This article belongs to the Special Issue Advances in Structural Health Monitoring of the Built Environment)

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16 pages, 766 KiB

Open AccessArticle

Lessons from Bridge Structural Health Monitoring (SHM) and Their Implications for the Development of Cyber-Physical Systems

by Emin Aktan, Ivan Bartoli, Branko Glišić and Carlo Rainieri

Infrastructures 2024, 9(2), 30; https://doi.org/10.3390/infrastructures9020030 - 7 Feb 2024

Cited by 5 | Viewed by 4534

Abstract

This paper summarizes the lessons learned after several decades of exploring and applying Structural Health Monitoring (SHM) in operating bridge structures. The challenges in real-time imaging and processing of large amounts of sensor data at various bandwidths, synchronization, quality check and archival, and most importantly, the interpretation of the structural condition, performance, and health are necessary for effective applications of SHM to major bridges and other infrastructures. Writers note that such SHM applications have served as the forerunners of cyber infrastructures, which are now recognized as the key to smart infrastructures and smart cities. Continued explorations of SHM in conjunction with control, therefore, remain vital for assuring satisfactory infrastructure system performance at the operational, damageability, and safety limit-states in the future. Researchers in the SHM of actually constructed systems, given their experience in monitoring major structures in the field, are well positioned to contribute to these vital needs. Especially, SHM researchers who have learned how to integrate the contributions from various disciplines such as civil, electrical, mechanical, and materials engineering; computer and social sciences; and architecture and urban planning would appear to be well equipped and could become instrumental in assessing the health and performance of urban regions, which today must function by optimizing and balancing the needs of Livability, Sustainability, and Resilience (LSR). Full article

(This article belongs to the Special Issue Advances in Structural Health Monitoring of the Built Environment)

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16 pages, 3466 KiB

Open AccessArticle

Characterizing Bridge Thermal Response for Bridge Load Rating and Condition Assessment: A Parametric Study

by Artem Marchenko, Rolands Kromanis and André G. Dorée

Infrastructures 2024, 9(2), 20; https://doi.org/10.3390/infrastructures9020020 - 26 Jan 2024

Cited by 3 | Viewed by 2371

Abstract

Temperature is the main driver of bridge response. It is continuously applied and may have complex distributions across the bridge. Daily temperature loads force bridges to undergo deformations that are larger than or equal to peak-to-peak traffic loads. Bridge thermal response must therefore be accounted for when performing load rating and condition assessment. This study assesses the importance of characterizing bridge thermal response and separating it from traffic-induced response. Numerical replicas (i.e., fine element models) of a steel girder bridge are generated to validate the proposed methodology. Firstly, a variety of temperature distribution scenarios, such as those resulting from extreme weather conditions due to climate change, are modelled. Then, nominal traffic load scenarios are simulated, and bridge response is characterized. Finally, damage is modelled as a reduction in material stiffness due to corrosion. Bridge response to applied traffic load is different before and after the introduction of damage; however, it can only be correctly quantified when the bridge thermal response is accurately accounted for. The study emphasizes the importance of accounting for distributed temperature loads and characterizing bridge thermal response, which are important factors to consider both in bridge design and condition assessment. Full article

(This article belongs to the Special Issue Advances in Structural Health Monitoring of the Built Environment)

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21 pages, 7594 KiB

Open AccessArticle

On the Generation of Digital Data and Models from Point Clouds: Application to a Pedestrian Bridge Structure

by F. Necati Catbas, Jacob Anthony Cano, Furkan Luleci, Lori C. Walters and Robert Michlowitz

Infrastructures 2024, 9(1), 6; https://doi.org/10.3390/infrastructures9010006 - 25 Dec 2023

Cited by 5 | Viewed by 2655

Abstract

This study investigates the capture of digital data and the development of models for structures with incomplete documentation and plans. LiDAR technology is utilized to obtain the point clouds of a pedestrian bridge structure. Two different point clouds with varying densities, (i) fine (11 collection locations) and (ii) coarse (4 collection locations), collected via terrestrial LiDAR, are analyzed to generate geometry and structural sections. This geometry is compared to the structural plans, which are then converted into numerical models (finite element—FE model) based on the point cloud data. Point cloud-based FE models (based on fine and coarse data) are compared with the structural plan-based FE model. It is observed that the static and dynamic responses are comparable within an acceptable range of a maximum difference of 5.5% for static deformation and an 8.23% frequency difference, with an average difference of less than 5%. Additionally, the dynamic properties of the fine and coarse point cloud FE models are compared with the operational modal analysis data obtained from the bridge. The fine and course point-cloud-based FE models, without model calibration, achieve an average accuracy of 8.76% and 9.94% for natural frequencies and a 0.89 modal assurance criterion value. The research found that the digital data generation yields promising results in this case for a bridge if documentation or plans are unavailable. With recent technologies and approaches such as digital twins, the connection between physical and virtual entities needs to be established by fusing digital models, sensorial information, and other data forms for better infrastructure management. Models such as those investigated and discussed in this paper can assist engineers with structural preservation in conjunction with monitoring data and utilization for digital twins. Full article

(This article belongs to the Special Issue Advances in Structural Health Monitoring of the Built Environment)

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20 pages, 4350 KiB

Open AccessArticle

Topographic Measurements and Statistical Analysis in Static Load Testing of Railway Bridge Piers

by Massimiliano Pepe, Domenica Costantino and Vincenzo Saverio Alfio

Infrastructures 2024, 9(1), 4; https://doi.org/10.3390/infrastructures9010004 - 22 Dec 2023

Cited by 3 | Viewed by 2034

Abstract

The aim of the paper is to identify a suitable method for assessing the deformation of structures (buildings, bridges, walls, etc.) by means of topographic measurements of significant targets positioned on the infrastructure under consideration. In particular, the paper describes an approach to testing a bridge in a mixed structure (concrete and steel). The methodological approach developed can be schematised into the following main phases: (i) surveying using total stations (TSs) in order to obtain the spatial coordinates of the targets by means of the three-dimensional intersection technique (planimetric and altimetric measurements); (ii) least-squares compensation for the measurements performed; (iii) displacement analysis; and (iv) statistical evaluation of the reliability of the results. This method was evaluated on a case study of a newly built double-track railway bridge, located near the metropolitan area of the city of Bari, Italy, during various loading and unloading activities. The results obtained, evaluated by means of certain statistical tests, made it possible to verify the structural suitability of the bridge. Full article

(This article belongs to the Special Issue Advances in Structural Health Monitoring of the Built Environment)

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18 pages, 2342 KiB

Open AccessArticle

Structural Health Monitoring-Based Bridge Lifecycle Extension: Survival Analysis and Monte Carlo-Based Quantification of Value of Information

by Antti Valkonen and Branko Glisic

Infrastructures 2023, 8(11), 158; https://doi.org/10.3390/infrastructures8110158 - 5 Nov 2023

Cited by 1 | Viewed by 2313

Abstract

A key goal of structural health monitoring (SHM) systems applied to infrastructure is to improve asset management. SHM systems yield benefits by providing information that allows improved asset management decisions. Often, improvement is measured in monetary terms, whereby lower expenses are sought. The value of information (VoI) is often evaluated through the quantification of the incremental benefit, resulting from the information provided by the SHM system. The VoI can be considered as having two components: value derived from the improved operation of the infrastructure and value derived from increased useful life. This work focuses on the latter source of value in the context of concrete decks in US highway bridges. To estimate the lifecycle extension potential and the connected VoI, we need to simulate bridge deck condition degradation over time to support a discounted cash flow analysis of bridge replacement cost. We accomplish this by utilizing a neural network-based survival analysis combined with Monte Carlo simulation. We present a case study using the developed methods. We have chosen to study the southbound portion of the bridge on the US Highway 202, located in Wayne, NJ. The selected bridge is a representative concrete highway overpass, the type of which there are large numbers in the US. The case study demonstrates the applicability of the methods developed for the general evaluation of the VoI obtained via SHM. The results are encouraging for the widespread use of SHM for lifecycle extension purposes; the potential value in such applications is large. Full article

(This article belongs to the Special Issue Advances in Structural Health Monitoring of the Built Environment)

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7 pages, 1191 KiB

Open AccessOpinion

Role of Civionics in the Civil Structural Health Monitoring System

by Aftab A. Mufti and Douglas J. Thomson

Infrastructures 2024, 9(3), 57; https://doi.org/10.3390/infrastructures9030057 - 11 Mar 2024

Viewed by 1995

Abstract

Civil structural health monitoring (CSHM) tracks different aspects of an infrastructure system’s service and safety condition by utilizing reliably measured data and physics-based model simulations. Data and physical models are coupled with heuristic experience to proactively represent current and expected future performance. In the past two decades, more bridges and dams have been instrumented and monitored during and after construction to determine their performances and responses to various loading, material, boundary, and environmental conditions. Furthermore, bridge and dam owners increasingly utilize civionics systems to obtain essential data for developing data-driven asset management programs and addressing the state of good repair requirements. Full article

(This article belongs to the Special Issue Advances in Structural Health Monitoring of the Built Environment)

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