This study represents a significant advancement in structural health monitoring by integrating infrared thermography (IRT) with cutting-edge deep learning techniques, specifically through the use of the Mask R-CNN neural network. This approach targets the precise detection and segmentation of hidden defects within the interfacial layers of Fiber-Reinforced Polymer (FRP)-reinforced concrete structures. Employing a dual RGB and thermal camera setup, we captured and meticulously aligned image data, which were then annotated for semantic segmentation to train the deep learning model. The fusion of the RGB and thermal imaging significantly enhanced the model\'s capabilities, achieving an average accuracy of 96.28% across a 5-fold cross-validation. The model demonstrated robust performance, consistently identifying true negatives with an average specificity of 96.78% and maintaining high precision at 96.42% in accurately delineating damaged areas. It also showed a high recall rate of 96.91%, effectively recognizing almost all actual cases of damage, which is crucial for the maintenance of structural integrity. The balanced precision and recall culminated in an average F1-score of 96.78%, highlighting the model\'s effectiveness in comprehensive damage assessment. Overall, this synergistic approach of combining IRT and deep learning provides a powerful tool for the automated inspection and preservation of critical infrastructure components.

The primary cause behind the degradation of reinforced concrete (RC) structures is the propagation of corrosion in the steel-RC structures. Nowadays, numerous retrofitting techniques are available in the construction sector. Fiber-reinforced polymer (FRP) is one of the efficient rehabilitation measures that can be implemented on corroded structures to enhance structural capacities. However, the estimation of axial strength of FRP-strengthened columns affected by corrosion has been a challenging and tedious task in the laboratory as well as on the site. Considering such shortcomings, the prediction of axial capacity can be done using various analytical methods and artificial intelligence (AI) techniques. In this study, a comprehensive dataset of circular columns was extracted from the literature to predict the axial strength of FRP-wrapped and unstrengthened RC corroded columns. The laboratory results from the assembled dataset were compared to corresponding values estimated using relevant design codes provided by American Concrete Institute (ACI 440.2R-17 and ACI 318-19), and Bureau of Indian Standard (IS 456:2000). Five machine learning models were employed on columns to predict the axial load carrying capacity of FRP-strengthened and un-strengthened RC corroded columns. The results discovered that the extreme gradient boosting (XGBoost) model achieves superior accuracy with the least errors and could be used by the scientific community and FRP applicators to forecast the axial performance of corroded columns strengthened with and without FRP. The findings from the design codes revealed that prediction errors were available in high margins. Furthermore, feature importance analysis was conducted using the Shapley Additive exPlanation algorithm to know the contribution and influence of each input parameter on axial capacity. The feature analysis found that unconfined compressive strength of concrete plays an important role in deciding the axial capacity of columns. Moreover, to enhance the precision of axial capacity computation and improving the overall efficacy in engineering practice, a web-based user-friendly interface was developed for FRP applicators and engineers to simplify the process.

With the enormous progress in the field of cardiovascular imaging in recent years, computed tomography (CT) has become readily available to phenotype atherosclerotic coronary artery disease. New analytical methods using artificial intelligence (AI) enable the analysis of complex phenotypic information of atherosclerotic plaques. In particular, deep learning-based approaches using convolutional neural networks (CNNs) facilitate tasks such as lesion detection, segmentation, and classification. New radiotranscriptomic techniques even capture underlying bio-histochemical processes through higher-order structural analysis of voxels on CT images. In the near future, the international large-scale Oxford Risk Factors And Non-invasive Imaging (ORFAN) study will provide a powerful platform for testing and validating prognostic AI-based models. The goal is the transition of these new approaches from research settings into a clinical workflow. In this review, we present an overview of existing AI-based techniques with focus on imaging biomarkers to determine the degree of coronary inflammation, coronary plaques, and the associated risk. Further, current limitations using AI-based approaches as well as the priorities to address these challenges will be discussed. This will pave the way for an AI-enabled risk assessment tool to detect vulnerable atherosclerotic plaques and to guide treatment strategies for patients.

Considering the different structural strength requirements of different parts of fiberglass yachts, carbon fiber/glass fiber hybrid reinforcement can be applied to the skins of sandwich panels in special areas. This paper designs and prepares 12 foam sandwich panel samples composed of pure carbon fiber, a carbon fiber/glass fiber hybrid, pure glass fiber skin, and PVC and SAN foam sandwich, with reference to the layup structure of the outer panel of a fiberglass yacht. Through a comparative analysis of low-speed impact experiments, edge compression experiments, and short beam three-point bending experiments, we seek the optimal carbon fiber/glass fiber hybrid layup design scheme for local structures to guide production. The results show that a reasonable hybrid carbon fiber layup in fiberglass skin can effectively reduce the low-speed impact damage of the sandwich structure, reduce edge compression damage, and improve the bending and compression resistance of sandwich structure. The impact resistance, compression resistance, and shear resistance of the SAN sandwich structure are stronger than the PVC sandwich structure. The carbon fiber/glass fiber hybrid SAN foam sandwich structure can be used for the local structural reinforcement of special parts such as the bow, side, and main deck of fiberglass yachts.

In the pursuit of creating more sustainable and resilient structures, the exploration of construction materials and strengthening methodologies is imperative. Traditional methods of relying on steel for strengthening proved to be uneconomical and unsustainable, prompting the investigation of innovative composites. Fiber-reinforced polymers (FRPs), known for their lightweight and high-strength properties, gained prominence among structural engineers in the 1980s. This period saw the development of novel approaches, such as near-surface mounted and externally bonded reinforcement, for strengthening of concrete structures using FRPs. In recent decades, additional methods, including surface curvilinearization and external prestressing, have been discovered, demonstrating significant additional benefits. While these techniques have shown the enhanced performance, their full potential remains untapped. This article presents a comprehensive review of current approaches employed in the fortification of reinforced cement concrete structures using FRPs. It concludes by identifying key areas that warrant in-depth research to establish a sustainable methodology for structural strengthening, positioning FRPs as an effective replacement for conventional retrofitting materials. This review aims to contribute to the ongoing discourse on modern structural strengthening strategies, highlight the properties of FRPs, and propose avenues for future research in this dynamic field.

Composites made from fiber-reinforced polymers (FRPs) are a crucial and highly adaptable category of materials widely utilized in numerous fields. Their flexibility and the range of criteria for classification enable the creation of tailored solutions to address distinct requirements in sectors such as civil engineering, aerospace, automotive, and marine, among others. The distinguishing characteristics of FRP composites include the type of reinforcing fiber used, the composition of the matrix material, the employed manufacturing process, the orientation of the fibers, and the specific end-use application. These classification variables offer engineers a versatile structure to determine and select the most appropriate materials and production techniques for their specific needs. Furthermore, the present study aims to reunite the criteria of classification for FRPs and specific manufacturing technologies of FRPs, such as conventional ones (matched die molding, contact molding), automated ones (filament winding, tape lay-up, and fiber placement), and advanced ones (electrospinning and additive manufacturing),with the chronological development of FRPs, insights on material characteristics, and comprehensive design guidelines based on their behavior in different environments of use.

This paper conducts an experimental study on the axial compressive performance of FRP-steel-concrete composite columns. Nine short columns were produced and evaluated in the study, comprising of three concrete-filled steel tube reference columns and six FRP-steel-concrete composite columns, respectively denoted as \"reference columns\" and \"composite columns\". Two categories of failure modes, including shear failure and waist drum, were observed from the experiments. The failure mode may trend toward waist drum from shear failure as more FRP layers were used. The number of FRP layers had a direct effect on the level of compressive strength attained, with a greater number of layers resulting in a greater increase in compressive strength. Moreover, a greater tensile strength and higher elastic modulus of CFRP sheets are more effective at improving the compressive stiffness of the columns. Finally, a four-stage confinement mechanism for FRP-wrapped steel tube concrete composite columns is proposed and discussed, through which the damage mechanisms of the composite structures are more rationally characterized.

Fiber reinforced plastics (FRP) offer huge potentials for energy efficient applications. Special care must be taken during both FRP fabrication and usage to ensure intended material properties and behavior. This paper presents a novel approach for the monitoring of the strain and temperature of glass fibre reinforced polymer (GFRP) materials in the context of both production process monitoring and structural health monitoring (SHM) applications. The sensor is designed to be integrated into GFRPs during the production process, and the sensor concept includes possibilities of automated placement during textile layup. To minimize sensor impact on GFRP integrity and to simplify vacuum setup and part handling, the sensor operates without the need for either wires or a battery. In the first sections of this work, sensor concept, design and prototype fabrication are presented. Subsequently, it is shown how the sensors can be used for flow front monitoring and cure estimation during GFRP production by measuring local resin temperature. The resulting specimens are then characterized regarding strain measurement capabilities, mechanical influence on the host component and overall system limitations. Average strain sensor accuracy is found to be ≤0.06 mm/m, while a maximum operation temperature of 126.9 °C and a maximum reading distance of 38 mm are measured. Based on a limited number of bending tests, no negative influence of sensor presence on breaking strength could be found. Possible applications include structural components, e.g., wind turbine blades or boat hulls.

The properties of fiber reinforced polymers are strongly related to the length and orientation of the fibers within the polymer matrix, the latter of which can be studied using X-ray computed tomography (XCT). Unfortunately, resolving individual fibers is challenging because they are small compared to the XCT voxel resolution and because of the low attenuation contrast between the fibers and the surrounding resin. To alleviate both problems, anisotropic dark field tomography via grating based interferometry (GBI) has been proposed. Here, the fiber orientations are extracted by applying a Funk-Radon transform (FRT) to the local scatter function. However, the FRT suffers from a low angular resolution, which complicates estimating fiber orientations for small fiber crossing angles. We propose constrained spherical deconvolution (CSD) as an alternative to the FRT to resolve fiber orientations. Instead of GBI, edge illumination phase contrast imaging is used because estimating fiber orientations with this technique has not yet been explored. Dark field images are generated by a Monte Carlo simulation framework. It is shown that the FRT cannot estimate the fiber orientation accurately for crossing angles smaller than 70∘, while CSD performs well down to a crossing angle of 50∘. In general, CSD outperforms the FRT in estimating fiber orientations.

The shear strength prediction of concrete beams reinforced with FRP rebars and stirrups is one of the most complicated issues in structural engineering applications. Numerous experimental and theoretical studies have been conducted to establish a relationship between the shear capacity and the design variables. However, existing semi-empirical models fail to deliver precise predictions due to the intricate nature of shear mechanisms. To provide a more accurate and reliable model, machine learning (ML) techniques are adopted to study the shear behavior of concrete beams reinforced with FRP rebars and stirrups. A database consisting of 120 tested specimens is compiled from the reported literature. An artificial neural network (ANN) and a combination of ANN with a genetic optimization algorithm (GA-ANN) are implemented for the development of an ML model. Through neural interpretation diagrams (NID), the critical design factors, i.e., beam width and effective depth, shear span-to-depth ratio, compressive strength of concrete, FRP longitudinal reinforcement ratio, FRP shear reinforcement ratio, and elastic modulus of FRP longitudinal reinforcement rebars and FRP stirrups, are identified and determined as input parameters of the models. The accuracy of the proposed models has been verified by comparing the model predictions with the available test results. The application of the GA-ANN model provides better statistical results (mean value Vexp/Vpre equal to 0.99, R2 of 0.91, and RMSE of 22.6 kN) and outperforms CSA S806-12 predictions by improving the R2 value by 18.2% and the RMSE value by 52.5%. Furthermore, special attention is paid to the coupling effects of design parameters on shear capacity, which has not been reasonably considered in the models in the literature and available design guidelines. Finally, an ML-regression equation considering the coupling effects is developed based on the data-driven regression analysis method. The analytical results revealed that the prediction agrees with the test results with reasonable accuracy, and the model can be effectively applied in the prediction of shear capacity of concrete beams reinforced with FRP bars and stirrups.