Search Results (440)

Periodic visual inspections are currently conducted to maintain the condition of railway bridges. These inspections rely on direct visual assessments by human inspectors, often requiring specialized equipment such as aerial ladders. However, this method is not only time-consuming and costly but also involves significant safety risks. Therefore, there is a growing need for a more efficient and reliable alternative to traditional visual inspections of railway bridges. In this study, we evaluated and compared the performance of damage detection using U-Net-based deep learning models on images captured by unmanned aerial vehicles (UAVs). The target damage types include cracks, concrete spalling and delamination, water leakage, exposed reinforcement, and paint peeling. To enable multi-class segmentation, the U-Net model was trained using three different loss functions: Cross-Entropy Loss, Focal Loss, and Intersection over Union (IoU) Loss. We compared these methods to determine their ability to distinguish actual structural damage from environmental factors and surface contamination, particularly under real-world site conditions. The results showed that the U-Net model trained with IoU Loss outperformed the others in terms of detection accuracy. When applied to field inspection scenarios, this approach demonstrates strong potential for objective and precise damage detection. Furthermore, the use of UAVs in the inspection process is expected to significantly reduce both time and cost in railway infrastructure maintenance. Future research will focus on extending the detection capabilities to additional damage types such as efflorescence and corrosion, aiming to ultimately replace manual visual inspections of railway bridge surfaces with deep-learning-based methods. Full article

Timely and accurate identification of surface damage in hydraulic structures is essential for maintaining structural integrity and ensuring operational safety. Traditional manual inspections are time-consuming, labor-intensive, and prone to subjectivity, especially for large-scale or inaccessible infrastructure. Leveraging advancements in aerial imaging, unmanned aerial vehicles (UAVs) enable efficient acquisition of high-resolution visual data across expansive hydraulic environments. However, existing deep learning (DL) models often lack architectural adaptations for the visual complexities of UAV imagery, including low-texture contrast, noise interference, and irregular crack patterns. To address these challenges, this study proposes a lightweight, robust, and high-precision segmentation framework, called LFPA-EAM-Fast-SCNN, specifically designed for pixel-level damage detection in UAV-captured images of hydraulic concrete surfaces. The developed DL-based model integrates an enhanced Fast-SCNN backbone for efficient feature extraction, a Lightweight Feature Pyramid Attention (LFPA) module for multi-scale context enhancement, and an Edge Attention Module (EAM) for refined boundary localization. The experimental results on a custom UAV-based dataset show that the proposed damage detection method achieves superior performance, with a precision of 0.949, a recall of 0.892, an F1 score of 0.906, and an IoU of 87.92%, outperforming U-Net, Attention U-Net, SegNet, DeepLab v3+, I-ST-UNet, and SegFormer. Additionally, it reaches a real-time inference speed of 56.31 FPS, significantly surpassing other models. The experimental results demonstrate the proposed framework’s strong generalization capability and robustness under varying noise levels and damage scenarios, underscoring its suitability for scalable, automated surface damage assessment in UAV-based remote sensing of civil infrastructure. Full article

Visual inspection of civil infrastructure for structural health assessment, as performed by structural engineers, is expensive and time-consuming. Therefore, automating this process is highly attractive, which has received significant attention in recent years. With the increasing capabilities of computers, deep neural networks have become a standard tool and can be used for structural health inspections. A key challenge, however, is the availability of reliable datasets. In this work, the U-net and DeepLab v3+ convolutional neural networks are trained on a synthetic Tokaido dataset. This dataset comprises images representative of data acquired by unmanned aerial vehicle (UAV) imagery and corresponding ground truth data. The data includes semantic segmentation masks for both categorizing structural elements (slabs, beams, and columns) and assessing structural damage (concrete spalling or exposed rebars). Data augmentation, including both image quality degradation (e.g., brightness modification, added noise) and image transformations (e.g., image flipping), is applied to the synthetic dataset. The selected neural network architectures achieve excellent performance, reaching values of 97% for accuracy and 87% for Mean Intersection over Union (mIoU) on the validation data. It also demonstrates promising results in the semantic segmentation of real-world structures captured in photographs, despite being trained solely on synthetic data. Additionally, based on the obtained results of semantic segmentation, it can be concluded that DeepLabV3+ outperforms U-net in structural component identification. However, this is not the case in the damage identification task. Full article

Non-destructive evaluation of reinforced concrete structures is essential for effective maintenance and safety assessments. This study explores the combined use of active microwave thermography and deep learning to detect and localize steel reinforcement within concrete elements. Numerical simulations were developed to model the thermal response of reinforced concrete subjected to microwave excitation, generating synthetic thermal images representing the surface temperature patterns of reinforced concrete, influenced by subsurface steel reinforcement. These images served as training data for a deep neural network designed to identify and localize rebar positions based on thermal patterns. The model was trained exclusively on simulation data and subsequently validated using experimental measurements obtained from large-format concrete slabs incorporating a structured layout of embedded steel reinforcement bars. Surface temperature distributions obtained through infrared imaging were compared with model predictions to evaluate detection accuracy. The results demonstrate that the proposed method can successfully identify the presence and approximate location of internal reinforcement without damaging the concrete surface. This approach introduces a new pathway for contactless, automated inspection using a combination of physical modeling and data-driven analysis. While the current work focuses on rebar detection and localization, the methodology lays the foundation for broader applications in non-destructive testing of concrete infrastructure. Full article

Post-tensioned (PT) reinforced concrete bridges are particularly vulnerable structures, as the deterioration of internal tendons is often difficult to detect using conventional inspection methods or visual assessments. This paper introduces a practical framework for ranking non-destructive testing (NDT) techniques employed to assess PT systems. The ranking is based on four performance categories: measurement accuracy, ease of use, cost, and impact of disruption to bridge operations on traffic. For each NDT technique, a score is assigned for each evaluation category, and the final ranking is determined using the weighted sum model (WSM). This approach enables the final assessment to reflect the priorities of different decision-making contexts defined by the end-user such as accuracy-oriented, cost-oriented, and impact-oriented scenarios. The proposed method is then applied to an existing bridge in order to practically demonstrate its effectiveness and the flexibility of the proposed criteria. Full article

Permeable pavement is an alternative to conventional impermeable pavement for various applications. However, a common issue with permeable pavement is clogging over time. Permeability is a parameter that reflects the capacity of the pavement to reduce surface runoff; a decline in permeability implies the occurrence of clogging. In this study, permeability data collected on pervious concrete (PC) and JW Eco-Technology (JW) revealed that JW maintained consistent permeability over time. However, PC displayed reduced values, and several locations along the edges had zero permeability, despite no regular vehicular and pedestrian use. Therefore, a portable pressure washer was used to clean the pavements. The cleaning procedure was able to recover the permeability of the areas that showed signs of clogging (0 to 2.69 cm/s) and restore the permeability of PC up to 4.60–5.58 cm/s for corner and center areas, respectively. Moreover, visual inspection using a borescope further revealed the full function of the JW pores (aqueducts), regardless of cleaning. Regardless, it is recommended that periodic cleaning maintenance be performed for both PC and JW using a pressure washer due to its convenience and efficacy, which will be discussed. Full article

Cracks and seepage in dam structures pose a serious risk to their safety, yet traditional inspection methods often fall short when it comes to detecting shallow or early-stage fractures. This study proposes a new approach that uses spectral response analysis to quickly identify signs of seepage in concrete dams. Researchers developed a three-layer model—representing the concrete, a seepage zone, and water—to better understand how cracks affect the way electrical signals behave, thereby inverting the state of the dam based on how electrical signals behave in actual engineering measurements. Through computer simulations and lab experiments, the team explored how changes in the resistivity and thickness of the seepage layer, along with the resistivity of surrounding water, influence key indicators like impedance and signal angle. The results show that the “spectrum-based method” can effectively detect seepage in concrete cracks of dams, and the measurement method of the “spectral quadrupole method” based on the “spectrum-based method” is highly sensitive to these variations, making it a promising tool for spotting early seepage. Field tests backed up the lab findings, confirming that this method is significantly better than traditional techniques at detecting cracks less than a meter deep and identifying early signs of water intrusion. It could provide dam inspectors with a more reliable way to monitor structural health and prevent potential failures. Full article

Structural health monitoring in resource-constrained environments demands crack segmentation models that match the accuracy of heavyweight convolutional networks while conforming to the power, memory, and latency limits of watt-level edge devices. This study presents a lightweight dual-attention network, which is a four-stage U-Net compressed to one-quarter of the channel depth and augmented—exclusively at the deepest layer—with a compact dual-attention block that couples channel excitation with spatial self-attention. The added mechanism increases computation by only 19%, limits the weight budget to 7.4 MB, and remains fully compatible with post-training INT8 quantization. On a pixel-labelled concrete crack benchmark, the proposed network achieves an intersection over union of 0.827 and an F1 score of 0.905, thus outperforming CrackTree, Hybrid 2020, MobileNetV3, and ESPNetv2. While refined weight initialization and Dice-augmented loss provide slight improvements, ablation experiments show that the dual-attention module is the main factor influencing accuracy. With 110 frames per second on a 10 W Jetson Nano and 220 frames per second on a 5 W Coral TPU achieved without observable accuracy loss, hardware-in-the-loop tests validate real-time viability. Thus, the proposed network offers cutting-edge crack segmentation at the kiloflop scale, thus facilitating ongoing, on-device civil infrastructure inspection. Full article

Precise grading of manufactured sand is vital to concrete performance, yet standard sieve tests, though accurate, are too slow for online quality control. Thus, we devised an image-based inspection method combining a dual-camera module with a Temporal Interval Sampling Strategy (TISS) to enhance throughput while maintaining precision. In this design, a global wide-angle camera captures the entire particle field, whereas a local high-magnification camera focuses on fine fractions. TISS selects only statistically representative frames, effectively eliminating redundant data. A lightweight segmentation algorithm based on geometric rules cleanly separates overlapping particles and assigns size classes using a normal-distribution classifier. In tests on ten 500 g batches of manufactured sand spanning fine, medium, and coarse gradations, the system processed each batch in an average of 7.8 min using only 34 image groups. It kept the total gradation error within 12% and the fineness-modulus deviation within ±0.06 compared to reference sieving. These results demonstrate that the combination of complementary optics and targeted sampling can provide a scalable, real-time solution. Full article

Compton backscattering is a versatile non-destructive technique for material characterization and structural evaluation in reinforced concrete. This methodology enables a single-sided inspection of large structures—which is particularly useful where only one side of the material is accessible for examination—is relatively inexpensive, and can be made portable for field applications. This study aims to assess the influence of basaltic coarse aggregates on the accurate localization and dimensioning of rebar in reinforced concrete using the gamma-ray Compton backscattering technique at two distinct incident photon energies—59.5 keV and 1170 keV. The analysis was performed through Monte Carlo simulations using the FLUKA code, providing insights into the feasibility and limitations of this non-destructive method for structural evaluation. Both photon energies successfully detected the rebar embedded at a 3 cm depth in mortar, achieving a good spatial resolution and contrast, despite the presence of a significant amount of iron oxide within the aggregate. Among the evaluated sources, ⁶⁰Co yielded the highest contrast and count values, demonstrating its potential for rebar detection at greater depths within concrete structures. The single-sided Compton scattering technique proved to be effective for the investigated application and presents a promising alternative for the non-destructive assessment of real-world reinforced concrete structures. Full article

Concrete printing technologies play a key role in the modernization of construction practices. One factor that mitigates their progress is the development of standards and characterization tools for concrete during printing. The aim of this work is to point out correlations between some printability descriptors of mortars and the data obtained from low-field nuclear magnetic resonance (NMR) relaxometry techniques. In this context, the superposed effects of an acrylic-based superplasticizer and calcium nitrate accelerator were investigated. The mortars under study are based on white Portland cement, fine aggregates, and silica fume at fixed ratios. Extrusion tests and visual inspection of the filaments evaluate the extrudability and the printing window. The selected compositions were also investigated via transverse $\left(T_2\right)$ and longitudinal $\left(T_1\right)$ NMR relaxation times. The results indicate that both additives increase the printing window of the mortar, while the accelerator induces a faster increase in specific surface area of capillary pores $\left(S/V\right)$ only after 30–60 min of hydration. Some correlations were found between the printing window and the range where the transverse relaxation rates $\left(1/T_2\right)$ and the pore surface-to-volume ratios $\left(S/V\right)$ increase linearly. This suggests some promising connections between NMR techniques and the study of structural buildup of cementitious materials. Full article

Concrete bridge cracks are critical indicators for maintenance planning. Traditional visual inspections are often subjective, labor-intensive, and time-consuming, requiring close-range access by inspectors. In contrast, UAV-based remote sensing, combined with advanced image processing, offers a more efficient and accurate solution. This study proposes an enhanced crack detection method combining Laplacian of Gaussian (LoG) filtering and Otsu thresholding to improve segmentation accuracy through background noise suppression. The proposed approach extracts key crack characteristics—including area, length, centroid, and main direction—enabling precise damage assessment. Experimental validation on a real bridge dataset demonstrates significant improvements in detection accuracy. The method provides a reliable tool for automated structural health monitoring, supporting data-driven maintenance decisions. Full article

This study explores the use of Polypropylene (PP) as a cost-effective matrix in composite materials, employing marble dust (MD) as a readily available filler. PP’s affordability and suitable strength make it ideal for various applications. MD, composed of CaCO₃, alumina, and silica, enhances mechanical strength and is commonly used in construction applications like concrete. Composite specimens were fabricated using the injection molding technique, and their mechanical properties (tensile, flexural, and compressive strength) were analyzed following ASTM standards. Tribological properties were assessed through a pin-on-disc apparatus with varying MD proportions. SEM and EDS analyses visually inspected the fracture types and filler distribution in the composites. Full article

Limited battery life compromises the usability of inspection and operation robots in hazardous environments such as nuclear sites under decommissioning. Both manually replacing the batteries and installing charging bays may be infeasible. Inductive wireless power transfer is a possible solution to deliver power through barriers such as reinforced concrete walls without physical contact. However, when requiring decent power (e.g., 100 W) to be transmitted over longer distances, the exaggerated dimensions of transmitting and receiving coils restrain the integrations with mobile robots. In this paper, a novel retractable design of the coil used in an inductive wireless power charging system is proposed, proving the minor deformation of the winding shape does not affect the transmission efficiency. A prototype with 5× size compression is implemented and tested. It successfully transmits 116.5 W over a distance of 1 m with 68.72% energy efficiency. The principle can be applied to a wide range of mobile platforms with a limited payload area where remote power is needed. Full article

Guaranteeing tunnel integrity is an important issue for several countries worldwide. Due to the continuous increase in the number of tunnels, as well as the aging of old tunnels, several countries and companies have created manuals to standardize tunnel inspection and assessment. Most manuals still specify just visual procedures for tunnel inspection; however, because of underground conditions, the structural system of tunnels is often accessible only by one side, thus posing difficulties to the accurate evaluation of the structural conditions of the tunnel using only visual inspection. A possibility to improve the effectiveness of tunnel inspection is the use of non-destructive testing (NDT), which will assist in obtaining information about the inner condition behind the tunnel wall. The current advancements in the NDT methods allow them to be employed in all the different kinds of inspections (initial, routine, special inspection) suggested by the manuals. Therefore, in an attempt to help in the decision about the application of each method, this work provides an overview of some international practices for tunnel inspections and shows a review of different NDT methods (traditional and new methods) applied to tunnel inspections. Furthermore, this study describes their workability, advantages, and capability, and classifies the best fitting of each in the inspection procedures. Full article