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Advanced Diagnostics and Nondestructive Testing Technologies for Civil Structures

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Civil Engineering".

Deadline for manuscript submissions: closed (20 February 2026) | Viewed by 15099

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Dr. Adriana Brancaccio

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Engineering Department, University of Campania “L. Vanvitelli”, Via Roma 29, 81031 Aversa, CE, Italy
Interests: linear and non linear inverse scattering; ground penetrating radar; microwave measurements; microwave tomography; singular values decomposition; detection and localization of defects
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Special Issue Information

Dear Colleagues,

This Special Issue aims to highlight the latest advancements in civil structure-related nondestructive testing (NDT) and diagnostics. It will showcase innovative technologies and applications that facilitate the accurate and efficient monitoring, evaluation, and condition assessment of civil infrastructure systems.

This Special Issue focuses on NDT methods, advanced diagnostics techniques, and their applications in civil structures, with its scope including, but not limited to, the following topics:

Nondestructive testing methods for assessing the condition of concrete, steel, and wooden structures;
Innovative signal processing techniques for enhancing the reliability and accuracy of NDT data;
Integration of NDT data with numerical models for structural health monitoring and prognosis;
Case studies demonstrating the practical application of NDT techniques in civil structures;
Development of automated and semi-automated NDT systems for efficient data collection and analysis;
Use of NDT data for decision-making in maintenance, repair, and replacement of civil infrastructure elements;
Challenges and future trends in NDT for civil structures.

Prof. Dr. Adriana Brancaccio
Guest Editor

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Keywords

non-destructive testing
material characterization
civil structure and infrastructure
experimental techniques
mechanics of materials

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Published Papers (9 papers)

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Research

16 pages, 6261 KB

Open AccessArticle

Polarization Effect in Contactless X-Band Detection of Bars in Reinforced Concrete Structures

by Adriana Brancaccio and Simone Palladino

Appl. Sci. 2026, 16(1), 412; https://doi.org/10.3390/app16010412 - 30 Dec 2025

Cited by 1 | Viewed by 326

Abstract

This study investigates the influence of electromagnetic field polarization in the non-destructive testing of reinforced concrete structures through both theoretical analysis and experimental validation. Theoretical models predict that the orientation of reinforcement bars relative to the incident electric field significantly affects the scattered signal, influencing their detectability. Laboratory experiments on realistic reinforced concrete specimens presenting both vertical bars and horizontal brackets confirm these predictions, demonstrating that polarization can be exploited to enhance measurement accuracy. These findings provide useful insights into the development of microwave-based diagnostic techniques for structural assessment. Full article

(This article belongs to the Special Issue Advanced Diagnostics and Nondestructive Testing Technologies for Civil Structures)

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20 pages, 1689 KB

Open AccessArticle

Prediction of High-Risk Failures in Urban District Heating Pipelines Using KNN-Based Relabeling and AI Models

by Sungyeol Lee, Jaemo Kang, Jinyoung Kim and Myeongsik Kong

Appl. Sci. 2025, 15(20), 11104; https://doi.org/10.3390/app152011104 - 16 Oct 2025

Viewed by 1020

Abstract

This study generated an AI (Artificial Intelligence)-based prediction model for identifying high-risk groups of failures in urban district heating pipelines using pipeline attribute information and historical failure records. A total of 324,495 records from normally operating pipelines and 2293 failure cases were collected. Because the dataset exhibited severe imbalance, a KNN (K Nearest Neighbors)-based similarity selection was applied to reclassify the top 10% of normal data most similar to failure cases as high-risk. Input variables for model development included pipe diameter, purpose, insulation level, year of burial, and burial environment, supplemented with derived variables to enhance predictive capacity. The dataset was trained using XGBoost (eXtreme Gradient Boosting) v3.0.2, LightGBM (Light Gradient-Boosting Machine) v4.5.0, and an ensemble model (XGBoost + LightGBM), and the performance metrics were compared. The XGBoost model (K = 2) achieved the best results, with an F2-score of 0.921 and an AUC of 0.993. Variable importance analysis indicated that year of burial, insulation level, and purpose were the most influential features, highlighting pipeline aging and insulation condition as key determinants of high-risk classification. The proposed approach enables prioritization of failure risk management and identification of vulnerable sections using only attribute data, even in situations where sensor installation and infrared thermography are limited. Future research should consider distance functions suitable for mixed variables, sensitivity to unit length, and SHAP (Shapley Additive exPlanations)-based interpretability analysis to further generalize the model and enhance its field applicability. Full article

(This article belongs to the Special Issue Advanced Diagnostics and Nondestructive Testing Technologies for Civil Structures)

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12 pages, 2357 KB

Open AccessArticle

D-Band THz A-Scanner for Grout Void Inspection of External Bridge Tendons

by Dae-Su Yee, Ji Sang Yahng and Seung Hyun Cho

Appl. Sci. 2025, 15(19), 10859; https://doi.org/10.3390/app151910859 - 9 Oct 2025

Cited by 1 | Viewed by 702

Abstract

Grout voids in external tendons of post-tensioned bridges are a critical issue, as they may result in the corrosion of the steel strands and significantly reduce tendon strength. Therefore, preventing tendon failure necessitates thorough inspection for these voids during both construction and operation. Terahertz electromagnetic wave testing is an effective method for detecting voids between the protective duct and the grout in external tendons, as terahertz waves can penetrate through the protective duct. This study introduces a D-band electronic frequency-modulated continuous-wave terahertz A-scanner for enhanced real-time inspection. The proposed method offers key advantages such as miniaturization, cost-effectiveness, and robustness, while providing effective detection of voids beneath the duct in external tendons. It is indicated that voids with a thickness of approximately 2.5 mm or greater can be detected using the D-band THz A-scanner. Full article

(This article belongs to the Special Issue Advanced Diagnostics and Nondestructive Testing Technologies for Civil Structures)

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12 pages, 2090 KB

Open AccessArticle

Predicting the Mechanical Strength of Caliche Using Nanoindentation to Preserve an Archaeological Site

by Carmen Salazar-Hernández, Jorge Cervantes, Mercedes Salazar-Hernández, Juan Manuel Mendoza-Miranda, Antonio Guerra-Contreras, Omar Cruces-Cervantes and María Jesús Puy-Alquiza

Appl. Sci. 2025, 15(17), 9355; https://doi.org/10.3390/app15179355 - 26 Aug 2025

Viewed by 982

Abstract

During the processes of excavation, restoration, and conservation of archaeological sites, it is common practice to perform physical and chemical characterization of the site materials. This is carried out to determine the best methods and materials for conserving and preserving the site. For this reason, techniques such as infrared spectroscopy and elemental analysis by X-ray fluorescence (XRF) are primarily used for chemical characterization, while mechanical tests such as the uniaxial compression test and hardness tests are used for physical and mechanical characterization. However, a common limitation is obtaining samples for destructive physical tests, such as compression tests, due to their invaluable cultural value. To address this problem, this work proposes the mechanical characterization of the material through nanoindentation. This technique requires a smaller sample size and can be performed in a timely manner by observing the resistance of each mineralogical phase present in the material. Thus, a preliminary predictive model of mechanical resistance is proposed based on the composition observed in the samples from the archaeological site of Cerro de los Remedios, located in the municipality of Comonfort, Guanajuato, Mexico. The samples were characterized using infrared spectroscopy, XRF, XRD, and SEM-EDS. The results indicate that the stone (caliche) is formed from 95.6–93% micrite calcite; 2.51–0.42% aluminosilicate; 3.14–1.89% high-calcium aluminosilicate; and 3.43–2.39 quartz or amorphous SiO₂. The proposed correlation models were adjusted to a linear function, a second-order polynomial, and a logarithmic function. In the M2–linear model, the non-linear effects generated by variables such as texture, porosity, phase adhesion, cement type, and cracks or discontinuities were not considered. In this model the best prediction of the experimental data was obtained within a variation of ±15%. Full article

(This article belongs to the Special Issue Advanced Diagnostics and Nondestructive Testing Technologies for Civil Structures)

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44 pages, 8956 KB

Open AccessArticle

Gray Brick Wall Surface Damage Detection of Traditional Chinese Buildings in Macau: Damage Quantification and Thermodynamic Analysis Method via YOLOv8 Technology

by Liang Zheng, Jianyi Zheng, Yile Chen, Yuchan Zheng, Wei Lao and Shuaipeng Chen

Appl. Sci. 2025, 15(12), 6665; https://doi.org/10.3390/app15126665 - 13 Jun 2025

Viewed by 1581

Abstract

The historical Lingnan gray brick buildings in Macau, a World Heritage Site, are facing severe deterioration due to prolonged disrepair, manifesting as cracks, breakages, moss adhesion, and other types of surface damage. These issues threaten not only the structural stability of the buildings but also the conservation of cultural heritage. To address the inefficiencies and low accuracy of traditional manual inspections, this study proposes an automated recognition and quantitative detection method for wall surface damage based on the YOLOv8 deep learning object detection model. A dataset comprising 375 annotated images collected from 162 gray brick historical buildings in Macau was constructed, covering eight damage categories: crack, damage, missing, vandalism, moss, stain, plant, and intact. The model was trained and validated using a stratified sampling approach to maintain a balanced class distribution, and its performance was comprehensively evaluated through metrics such as the mean average precision (mAP), F1 score, and confusion matrices. The results indicate that the best-performing model (Model 3 at the 297th epoch) achieved a mAP of 61.51% and an F1 score up to 0.74 on the test set, with superior detection accuracy and stability. Heatmap analysis demonstrated the model’s ability to accurately focus on damaged regions in close-range images, while damage quantification tests showed high consistency with manual assessments, confirming the model’s practical viability. Furthermore, a portable, integrated device embedding the trained YOLOv8 model was developed and successfully deployed in real-world scenarios, enabling real-time damage detection and reporting. This study highlights the potential of deep learning technology for enhancing the efficiency and reliability of architectural heritage protection and provides a foundation for future research involving larger datasets and more refined classification strategies. Full article

(This article belongs to the Special Issue Advanced Diagnostics and Nondestructive Testing Technologies for Civil Structures)

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21 pages, 4591 KB

Open AccessArticle

Research on Multi-Step Prediction of Pipeline Corrosion Rate Based on Adaptive MTGNN Spatio-Temporal Correlation Analysis

by Mingyang Sun and Shiwei Qin

Appl. Sci. 2025, 15(10), 5686; https://doi.org/10.3390/app15105686 - 20 May 2025

Cited by 3 | Viewed by 1595

Abstract

In order to comprehensively investigate the spatio-temporal dynamics of corrosion evolution under complex pipeline environments and improve the corrosion rate prediction accuracy, a novel framework for corrosion rate prediction based on adaptive multivariate time series graph neural network (MTGNN) multi-feature spatio-temporal correlation analysis is proposed. First, pipeline monitoring points are modeled as graph nodes to construct the pipeline corrosion spatio-temporal information graph, with corrosion rate and auxiliary features (selected through feature correlation analysis) forming node attributes. Then, a dynamic adjacency matrix is adaptively learned to capture hidden spatial dependencies, while temporal convolution modules extract multi-scale temporal patterns, and the node sequences with integrated corrosion features are input into the adaptive MTGNN for prediction. To reduce the accumulation of errors in multi-step prediction, a “chunked progressive” training strategy is adopted, incrementally expanding prediction horizons. Finally, experiments based on real urban drainage pipeline data show that in six-step predictions, the model reduces MAE, RMSE, and MAPE by 6.59–32.16%, 4.38–27.95%, and 5.01–22.22%, respectively, compared to traditional time series methods such as Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and non-adaptive MTGNN. The results indicate that the adaptive MTGNN, which integrates multi-source node features, has higher prediction accuracy across the three evaluation metrics, highlighting its capability to leverage spatio-temporal synergies for accurate short-term corrosion rate prediction. Full article

(This article belongs to the Special Issue Advanced Diagnostics and Nondestructive Testing Technologies for Civil Structures)

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26 pages, 21449 KB

Open AccessArticle

Automated Multi-Type Pavement Distress Segmentation and Quantification Using Transformer Networks for Pavement Condition Index Prediction

by Zaiyan Zhang, Weidong Song, Yangyang Zhuang, Bing Zhang and Jiachen Wu

Appl. Sci. 2024, 14(11), 4709; https://doi.org/10.3390/app14114709 - 30 May 2024

Cited by 11 | Viewed by 3185

Abstract

Pavement distress detection is a crucial task when assessing pavement performance conditions. Here, a novel deep-learning method based on a transformer network, referred to as ISTD-DisNet, is proposed for multi-type pavement distress semantic segmentation. In this methodology, a mix transformer (MiT) based on a hierarchical transformer structure is chosen as the backbone to obtain multi-scale feature information on pavement distress, and a mixed attention module (MAM) is introduced at the decoding stage to capture the pavement distress features across different channels and spatial locations. A learnable transposed convolution upsampling module (TCUM) enhances the model’s ability to restore multi-scale distress details. Subsequently, a novel parameter—the distress pixel density ratio (PDR)—is introduced based on the segmentation results. Analyzing the intrinsic correlation between the PDR and the pavement condition index (PCI), a new pavement damage index prediction model is proposed. Finally, the experimental results reveal that the F1 and mIOU of the proposed method are 95.51% and 91.67%, respectively, and the segmentation performance is better than that of the other seven mainstream segmentation models. Further PCI prediction model validation experimental results also indicate that utilizing the PDR enables the quantitative evaluation of the pavement damage conditions for each assessment unit, holding promising engineering application potential. Full article

(This article belongs to the Special Issue Advanced Diagnostics and Nondestructive Testing Technologies for Civil Structures)

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20 pages, 7737 KB

Open AccessArticle

Investigation of Carbon Fiber Reinforced Polymer Concrete Reinforcement Ageing Using Microwave Infrared Thermography Method

by Barbara Szymanik, Sam Ang Keo, Franck Brachelet and Didier Defer

Appl. Sci. 2024, 14(10), 4331; https://doi.org/10.3390/app14104331 - 20 May 2024

Cited by 5 | Viewed by 2643

Abstract

This study presents the utilization of the microwave infrared thermography (MIRT) technique to identify and analyze the defects in the carbon-fiber-reinforced polymer (CFRP) composite reinforcement of concrete specimens. At first, a set of numerical models was created, comprising the broadband pyramidal horn antenna and the analyzed specimen. The utilization of the system operating at a power of 1000 W in a continuous mode, operating at frequency of 2.45 GHz, was analyzed. The specimen under examination comprised a compact concrete slab that was covered with an adhesive layer and, thereafter, topped with a layer of CFRP. An air gap represented a defect at the interface between the concrete and the CFRP within the adhesive layer. In the modeling stage, the study investigated three separate scenarios—a sample with no defects, a sample with a defect located at the center, and a sample with a numerous additional random defects located at the rim of the CFRP matte—to analyze the effect of the natural reinforcement degradation in this area. The next phase of the study involved conducting experiments to confirm the results obtained from numerical modeling. In the experiments, the concrete sample aged for 10 years with the defect in the center and naturally developed defects at the CFRP rim was used. The study employed numerical modeling to explore the phenomenon of microwave heating in complex structures. The aim was to assess the chosen antenna design and identify the most effective experimental setup. These conclusions were subsequently confirmed through experimentation. The observations made during the heating process were particularly remarkable since they deviated from earlier studies that solely conducted measurements of the sample post-heating phase. The findings demonstrate that MIRT has the capacity to be employed as a technique for detecting flaws in concrete structures reinforced with CFRP. Full article

(This article belongs to the Special Issue Advanced Diagnostics and Nondestructive Testing Technologies for Civil Structures)

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14 pages, 4796 KB

Open AccessArticle

A PZT-Based Smart Anchor Washer for Monitoring Prestressing Force Based on the Wavelet Packet Analysis Method

by Long Wang, Liuyu Zhang, Di Mo and Xiaoguang Wu

Appl. Sci. 2024, 14(2), 641; https://doi.org/10.3390/app14020641 - 12 Jan 2024

Cited by 2 | Viewed by 1941

Abstract

Prestressed steel strands in prestressed structures offset or reduce the tensile stress caused by external loads, making them the primary load-bearing components. Great concerns have been raised about prestress monitoring due to the growing use of structural health monitoring (SHM). Piezoceramic (PZT) active sensing methods are commonly used in this field. However, there appears to be a problem of “energy saturation” in the utilization of piezoceramic active sensing methods. In this study, a smart anchor washer with semi-cylinders was developed to alleviate the saturation problem. An intelligent monitoring system is formed by combining the upper and lower annular cylinders with two piezoelectric patches. The piezoelectric patch on the upper annular cylinder is used as an actuator to emit signals through the contact interface of the smart anchor washer, which are then received by the piezoelectric patch on the lower annular cylinder. Based on wavelet packet decomposition, we investigate the correlation between the energy of the received signal and the applied tension force. Finally, a prestressing force index is developed for monitoring prestressing force using Shannon entropy. It is found that the index decreases with the increase in tension. The proposed design and index are also sensitive to early monitoring of prestressing force and can be used to monitor the entire prestressing process of steel strands. Full article

(This article belongs to the Special Issue Advanced Diagnostics and Nondestructive Testing Technologies for Civil Structures)

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