Search Results (437)

Marine concrete structures are continuously exposed to harsh marine environments—salt, waves, and biological fouling—that accelerate corrosion and cracking, increasing maintenance costs. Traditional Non-Destructive Testing (NDT) techniques often fail to detect early damage due to signal attenuation and noise in underwater conditions. This study critically reviews recent advances in Artificial Intelligence-integrated NDT (AI-NDT) technologies for marine concrete, focusing on their quantitative performance improvements and practical applicability. To be specific, a systematic comparison of vision-based and signal-based AI-NDT techniques was carried out across reported field cases. It was confirmed that the integration of AI improved detection accuracy by 17–25%, on average, compared with traditional methods. Vision-based AI models such as YOLOX-DG, Cycle GAN, and MSDA increased mean mAP 0.5 by 4%, while signal-based methods using CNN, LSTM, and Random Forest enhanced prediction accuracy by 15–20% in GPR, AE, and ultrasonic data. These results confirm that AI effectively compensates for environmental distortions, corrects noise, and standardizes data interpretation across variable marine conditions. Lastly, the study highlights that AI-enabled NDT not only automates data interpretation but also establishes the foundation for predictive and preventive maintenance frameworks. By linking data acquisition, digital twin-based prediction, and lifecycle monitoring, AI-NDT can transform current reactive maintenance strategies into sustainable, intelligence-driven management for marine infrastructure. Full article

This study presents an advanced signal analysis model for internal defect identification in bridge decks using impact echo field testing data designed to mitigate signal noise and the variability encountered during real-world inspections. Field tests were conducted on a concrete bridge deck utilizing an automated inspection system, systematically capturing impact-echo signals across multiple scanning paths. The large volume of field-acquired data poses significant challenges, particularly in identifying defects and isolating clean signals and suppressing noise under variable environmental conditions. To enhance the accuracy of defect detection, a deep learning framework was designed to refine critical signal parameters, such as signal duration and the starting point in relation to the zero-crossing. A convolutional neural network (CNN)-based classification model was developed to categorize signals into delamination, non-delamination, and insignificant classes. Through systematic parameter tuning, optimal values of 1 ms signal duration and 0.1 ms starting time were identified, resulting in a classification accuracy of 88.8%. Laboratory test results were used to validate the signal behavior trends observed during the parameter optimization process. Comparison of defect maps generated before and after applying the CNN-optimized signal parameters revealed significant enhancements in detection accuracy. The findings highlight the effectiveness of integrating advanced signal analysis and deep learning techniques with impact-echo testing, offering a robust non-destructive evaluation approach for large-scaled infrastructures such as bridge deck condition assessment. Full article

This study introduces a theoretical and computational framework for modeling acoustic wave propagation in defective concrete, with applications to non-destructive testing and structural health monitoring. The formulation is based on a coupled system of evolutionary hyperbolic equations, where internal defects are explicitly represented as localized energetic sources or sinks. A key contribution is the definition of a coercivity coefficient, which quantifies the energetic effect of defects and enables their classification as stabilizing, neutral, or dissipative. The model establishes a rigorous relationship between defect morphology, spatial distribution, and the global energetic stability of the material. Numerical simulations performed with an explicit finite-difference time-domain scheme confirm the theoretical predictions: the normalized total energy remains above $95\%$ for stabilizing defects (${\mu }_i>0$), decreases by about $10\%$ for quasi-neutral cases (${\mu }_i\approx 0$), and drops below $50\%$ within $200\mathsf{\mu }\mathrm{s}$ for dissipative defects (${\mu }_i<0$). The proposed approach reproduces the attenuation and phase behavior of classical Biot-type and Kelvin–Voigt models with deviations below $5\%$ while providing a richer energetic interpretation of local defect dynamics. Although primarily theoretical, this study establishes a physically consistent and quantitatively validated framework that supports the development of predictive ultrasonic indicators for the energetic classification of defects in concrete structures. Full article

This study investigates fiber content and orientation in prefabricated slab elements made of ultra-high-performance fiber-reinforced concrete (UHPFRC), using novel non-destructive measurement using a coil’s quality factor, where the coil is put to one side of the specimen only. This allows the analysis of slab specimens of arbitrary size. That then allows an accurate quality control of elements made in the prefabrication industry. This study presents an experimental campaign designed to evaluate the non-destructive principle’s accuracy and practical feasibility. Twenty-five large slab specimens were made in an industrial prefabrication plant using various casting methods to introduce different flow-induced fiber parameters. The slabs were subjected to this non-destructive testing, then destructive bending tests and CT scanning to tie the results together and validate the non-destructive results. The results showed that the coil’s quality factor values correspond well to the distribution (concentration) and orientation of fibers, and the method reliably reveals potential defects of the material and can predict the element’s mechanical properties. Full article

Accurate prediction of concrete compressive strength is essential for ensuring structural safety in civil engineering, particularly in road and bridge construction, where inadequate strength can lead to deformation, cracking, or collapse. Traditional non-destructive testing (NDT) methods, such as the Rebound Hammer Test, estimate strength using regression-based formulas fitted with measurement data; however, these formulas, typically optimized via the least squares method, are highly sensitive to initial parameter settings and exhibit low robustness, especially for nonlinear relationships. Meanwhile, AI-based models, such as neural networks, require extensive datasets for training, which poses a significant challenge in real-world engineering scenarios with limited or unevenly distributed data. To address these issues, this study proposes a gradient-boosting artificial bee colony (GB-ABC) algorithm for robust regression curve fitting. The method integrates two novel mechanisms: gradient descent to accelerate convergence and prevent entrapment in local optima, and a point density-weighted strategy using Gaussian Kernel Density Estimation (GKDE) to assign higher weights to sparse data regions, enhancing adaptability to field data irregularities without necessitating large datasets. Following data preprocessing with Local Outlier Factor (LOF) to remove outliers, validation on 600 real-world samples demonstrates that GB-ABC outperforms conventional methods by minimizing mean relative error rate (RER) and achieving precise rebound-strength correlations. These advancements establish GB-ABC as a practical, data-efficient solution for on-site concrete strength estimation. Full article

Premature deterioration of concrete structures in coastal areas requires a careful evaluation based on durability criteria. Electrical Resistivity (ER) serves as a valuable indicator of concrete durability, as it reflects how easily aggressive agents can penetrate its pores. This testing method offers several advantages; it is non-destructive, rapid, and more cost-effective than the chloride permeability test (RCPT). Furthermore, durable concrete typically necessitates larger quantities of cement, which contradicts the goals of sustainable concrete development. Thus, a significant challenge is to create concrete that is both durable and sustainable. This research explores the effects of pozzolanic additives, specifically Volcanic Ash (VA) and Sugarcane Bagasse Ash (SCBA), on the electrical resistivity of eco-friendly concretes exposed to the coastal conditions of the Gulf of Mexico. The electrical resistivity (ER) was measured at intervals of 3, 7, 14, 21, 28, 45, 56, 90, and 180 days across 180 cylinders, each with dimensions of 10 cm × 20 cm. The sustainability of the concrete was evaluated based on its energy efficiency. Three types of mixtures were developed using the ACI 211.1 method, maintaining a water-to-cement (w/c) ratio of 0.57 with CPC 30 R RS cement and incorporating various additions: (1) varying percentages of VA (2.5%, 5%, and 7.5%), (2) SCBA at rates of 5%, 10%, and 15%, and (3) ternary mixtures featuring VA-SCBA ratios of 1:1, 1:2, and 1:3. The findings indicated an increase in ER of up to 37% and a reduction in CO₂ emissions ranging from 4.2% to 16.8% when compared to the control mixture, highlighting its potential for application in structures situated in aggressive environments. Full article

This study presents an amplitude-based non-destructive testing (NDT) approach for estimating reinforcement bar diameter in reinforced concrete members using ground-penetrating radar (GPR). The novelty of this work lies in the use of normalized amplitude-diameter-depth (NADD) relationships, which link the reflected electromagnetic wave amplitude to both rebar diameter and cover depth through an exponential attenuation model. Normalization was applied to remove the influence of varying signal energy and antenna coupling, thereby allowing consistent comparison of amplitudes across different depths and improving the reliability of amplitude-depth interpretation. The NADD equation was developed from GPR measurements obtained on a reinforced concrete slab containing bars with diameters ranging from 9.5 mm (#3 bar) to 25.4 mm (#8 bar) and then validated using data from three prestressed concrete box beams recovered from a decommissioned bridge managed by the West Virginia Department of Highways. The normalized amplitude prediction error (E_a) was calculated to quantify model performance. The minimum mean error of approximately 4.7% corresponded to the 12.7 mm (#4 bar), which matched the actual reinforcement used in the beams. The results demonstrate that the proposed normalization-based approach effectively captures the amplitude-depth-diameter relationship, offering a quantitative framework for interpreting GPR data and improving the evaluation of reinforcement characteristics in existing concrete structures. Full article

Concrete pavements frequently develop subsurface voids between surface and base layers during long-term service due to cyclic loading, environmental effects, and subgrade instability, which compromise structural integrity and traffic safety. Ground-penetrating radar (GPR) has been widely used as a non-destructive method for detecting such voids. However, the presence of coarse aggregates with strong electromagnetic scattering properties often introduces pseudo-reflection signals in radar images, hindering accurate void identification. To address this challenge, this study develops a high-fidelity three-phase concrete model incorporating aggregates, mortar, and the interfacial transition zone (ITZ). The Finite-Difference Time-Domain (FDTD) method is used to simulate electromagnetic wave propagation in both voided and intact structures. Simulation results reveal that aggregate-induced scattering can blur or distort reflection interfaces, generating pseudo-hyperbolic anomalies even in the absence of voids. In cases of thin-layer voids, real echo signals may be masked by aggregate scattering, leading to missed detections. GPR systems can be broadly classified into impulse, continuous-wave, and multi-frequency types. To validate the simulations, field tests using multi-frequency 2D/3D GPR systems and borehole verification were conducted. The results confirm the consistency between simulated and actual radar anomalies and validate the proposed model. This work provides theoretical insight and modeling strategies to enhance the interpretation accuracy of GPR data for subsurface void detection in concrete pavements. Full article

This paper presents an experimental study using test specimens to demonstrate that the filling status of concrete on the inner surface of a thick wall’s steel plates can be monitored on the outside using infrared cameras. In previous research, it was feasible to monitor the filling status of concrete on the wall with a steel plate thickness of 10 mm or less on the outside using an infrared camera. In this study, the steel plate thickness is increased from 10 mm to 22 mm and 32 mm. Full article

Cracks in concrete are a persistent issue that compromises structural durability, increases maintenance costs, and poses environmental challenges. Self-healing concrete has emerged as a promising innovation to address these concerns by autonomously sealing cracks and restoring integrity. This review focuses on two primary healing mechanisms: autogenous healing and microbial-induced calcite precipitation (MICP), the latter involving the biomineralization activity of bacteria, such as Bacillus subtilis and Sporosarcina pasteurii (formerly known as B. pasteurii). This review explores the selection, survivability, and activity of these microbes within the alkaline concrete environment. Additionally, the review highlights the role of fiber-reinforced cementitious composites (FRCCs), including high-performance fiber-reinforced cement composites (HPFRCCs) and engineered cement composites (ECCs), in enhancing crack control and enabling more effective microbial healing. The hybridization of natural and synthetic fibers contributes to both improved mechanical properties and crack width regulation, key factors in facilitating bacterial calcite precipitation. This review synthesizes current findings on self-healing efficiency, fiber compatibility, and the scalability of bacterial healing in concrete. It also evaluates critical parameters, such as healing agent integration, long-term performance, and testing methodologies, including both destructive and non-destructive techniques. By identifying existing knowledge gaps and performance barriers, this review offers insights for advancing sustainable, fiber-assisted microbial self-healing concrete for resilient infrastructure applications. Full article

Condition assessment of reinforced concrete structures presents a significant challenge worldwide as structures built in the post-war construction period (1950s–1970s) reach end of service life. The alkali-silica reaction (ASR) is one of several damage mechanisms which commonly affect infrastructure in Canada. Frequent freeze-thaw cycles and heavy use of de-icing salts in winter as well as high heat and humidity in summer are expected to have intensified ASR-induced damage. This work investigates five segments of a pier cap—PC, which had undergone encapsulation repair, and four segments of a pier shaft—PS, which represented dry and semi-submerged conditions, removed from a highway bridge constructed starting in 1957. Preliminary evaluation through visual inspection (conventional, qualitative and quantitative using the cracking index—CI) and non-destructive techniques (rebound hammer—RBH, ultrasonic pulse velocity—UPV and surface resistivity) was conducted on both internal (i.e., cut during decommissioning) and external (i.e., exposed while in service) surfaces of five PC segments and four PS segments. Differences in geometry, exposure conditions and repair history from the two members were found to have limited impact on the results of quantitative tests (i.e., CI, RBH and UPV results with average values of 1.6 mm/m, 37 MPa and 2.4 Km/s, respectively) while still exhibiting qualitative differences in visual determination (i.e., crack patterns, surface appearance and crack widths). Full article

This study explores the influence of various nano-inclusions on the electrical and thermal properties of cement-based materials. Specifically, it investigates the incorporation of Multi-Walled Carbon Nanotubes (MWCNTs) and Graphene Nanoplatelets (GNPs) as reinforcement materials in cement composites. These advanced nanomaterials enhance the mechanical strength, durability, and functional properties of cementitious matrices. A series of experimental tests was conducted to evaluate the thermal and electrical behavior of nano-reinforced concrete, employing nondestructive evaluation techniques, such as Infrared Thermography (IRT) and Electrical Resistivity measurements. The results indicate that increasing the concentration of nanomaterials significantly improves both the thermal and electrical conductivity of the composites. Optimum performance was observed at a CNT dosage of 0.6% and a GNP dosage of 1.2% by weight of cement in cement paste, while in concrete, both nanomaterials showed a significant decrease in resistivity beginning at 1.0%, with optimal performance at 1.2%. The study also emphasizes the critical role of proper dispersion techniques, such as ultrasonication, in achieving a homogeneous distribution of nanomaterials within the cement matrix. These findings highlight the potential of carbon nanotubes (CNTs) and GNPs to enhance the multifunctional properties of cement-based materials, paving the way for their application in smart and energy-efficient construction applications. Full article

Ground penetrating radar (GPR) is a non-destructive testing (NDT) method increasingly used for evaluating concrete structures by identifying internal flaws and embedded objects. This study presents a structured image-based methodology for interpreting GPR B-scan data using a practical flowchart designed to aid in distinguishing common subsurface anomalies. The methodology was validated through a laboratory experiment involving four concrete slabs embedded with simulated defects, including corroded rebar, hollow pipes, polystyrene sheets (to represent delamination), and hollow containers (to represent voids). Scans were performed using a commercially available device, and the resulting radargrams were analyzed based on signal reflection patterns. The proposed approach successfully identified rebar positions, spacing, and depths, as well as low-dielectric anomalies such as voids and polystyrene inclusions. Some limitations were noted in detecting non-metallic materials with weak dielectric contrast, such as hollow pipes. Overall, the findings demonstrate the reliability and adaptability of the proposed method in improving the interpretation of GPR data for structural diagnostics. The proposed methodology achieved a detection accuracy of approximately 90% across all embedded features, which demonstrates improved interpretability compared to traditional manual GPR assessments, typically ranging between 70 and 80% in similar laboratory conditions. Full article

Concrete sleepers are essential components of railroad infrastructure, yet their service life has been reduced by one-third due to deterioration caused by internal swelling reactions (ISR), leading a major Brazilian railroad to replace millions of sleepers within a decade. This study investigates the reliability of various non-destructive testing (NDT) techniques to estimate damage levels in concrete sleepers. The methods evaluated include surface hardness testing, stress wave propagation, electromagnetic wave propagation using ground-penetrating radar (GPR), electrical resistivity, and resonant frequency. These techniques were applied to assess sleepers diagnosed as affected by alkali-silica reaction (ASR) and delayed ettringite formation (DEF) at different deterioration degrees. Although findings indicate that most NDT methods are limited and unreliable for quantifying ISR-induced damage, resonant frequency testing combined with energy dissipation analysis provided the highest accuracy across all damage stages and was able to capture microstructural changes before significant expansion occurred. These results support the use of vibration-based screening tools to enhance early detection and guide condition assessment of railroad infrastructure, helping to reduce the premature replacement of ISR-affected concrete sleepers. Full article

Concrete compressive strength is a critical property for structural performance and construction scheduling. Traditional non-destructive testing (NDT) methods, such as rebound hammer and ultrasonic pulse velocity, offer limited reliability and resolution, particularly at early ages. This study presents an AI-powered structural health monitoring (SHM) framework that integrates multi-type and multi-position piezoelectric (PZT) sensor networks with machine learning for in situ prediction of concrete compressive strength. Signals were collected from various PZT types positioned on the top, middle, bottom, and surface sides of concrete cubes during curing. A series of machine learning models were trained and evaluated using both the full and selected feature sets. Results showed that combining multiple PZT types and locations significantly improved prediction accuracy, with the best models achieving up to 95% classification accuracy using only the top 200 features. Feature importance and PCA analyses confirmed the added value of sensor heterogeneity. This study demonstrates that multi-sensor AI-enhanced SHM systems can offer a practical, non-destructive solution for real-time strength estimation, enabling earlier and more reliable construction decisions in line with industry standards. Full article