Reinforced concrete (RC) structures are used in a wide range of applications, including high-rise buildings, nuclear power plants, oil and gas platforms, bridges, and other infrastructure. However, over time, RC structures can be subject to deterioration and damage, particularly from exposure to weather and environmental conditions, heavy traffic loads, and other factors. Regular inspections, diagnosing the condition, maintenance, and repair can help to mitigate the effects of degradation and extend the life of the structure. The task of this study was to determine the possible causes of the defects of the RC elements based on the identification of external indicators using the ML tools. This study created and compared the performance of four ML models, namely, Support Vector Regression (SVR), decision trees (DTs), random forest (RF), and Artificial Neural Networks (ANNs). The first comparison showed a rather low performance of all models, with a slight advantage of the ANN model. Later, six ANN models were optimized to obtain a higher level of performance. The next step of this study was the training, validation, and testing of ANN models. Analysis of MAPE and R2 metrics showed that the ANN model with an Adaptative Moment (ADAM) loss function and sigmoid activation had the best results (MAPE 3.38%; R² 0.969). The novelty of the study consisted of the development of the ML model, which is based on the use of ANNs, and allows for the establishment of cause-and-effect relationships in the diagnosis of the technical condition of the RC elements. The advantage of using ANN to solve this problem is the possibility to obtain a forecast in the form of continuous values. Moreover, the model can be used further without retraining, and it can make predictions on datasets it has not yet “seen”. The practical use of such a model will allow for the diagnosis of some causes of defects during a visual inspection of structures.

Bridges in Ukraine are one of the most important components of the infrastructure, requiring attention from government agencies and constant funding. The object of the study was the methodology for quantifying the condition of bridge components. The Artificial Neural Network-based (ANN) tool was developed to quantify the technical condition of bridge components. The literature analysis showed that in most cases the datasets were obtained during the inspection of bridges to solve the problems of assessing the current technical condition. The lack of such a database prompted the creation of a dataset on the basis of the Classification Tables of the Operating Conditions of the Bridge Components (CT). Based on CTs, five datasets were formed to assess the condition of the bridge components: bridge span, bridge deck, pier caps beam, piers and abutments, approaches. The next step of this study was creating, training, validating and testing ANN models. The network with ADAM loss function and softmax activation showed the best results. The optimal values of MAPE and R² were achieved at the 100th epoch with 64 neurons in the hidden layer and were equal to 0.1% and 0.99998, respectively. The practical application of the ANN models was carried out on the most common type of bridge in Ukraine, namely, a road beam bridge of small length, made of precast concrete. The novelty of this study consists of the development of a tool based on the use of ANN model, and the proposal to modify the methodology for quantifying the condition of bridge components. This will allow minimizing the uncertainties associated with the subjective judgments of experts, as well as increasing the accuracy of the assessment.

The main task of the operation of engineering structures is to ensure the stability of structures with aggressive external influences, which have a complex probabilistic nature. The reliable functioning of bridge structures requires the development and application of modern systems for inspection and assessment of the technical condition of the structure to take timely measures to ensure the safe operation of the structure in changing operating conditions. With the rapid development of AI, modern approaches are increasingly adopted, offering distinct advantages compared to classical methods. The article aims to develop an AI-based model for quantifying the technical condition of bridge structural components based on data obtained from the survey. To achieve this goal, the authors analyzed existing approaches to the inspection and assessment of bridges and studied the experience of using AI in bridge assessment. Based on the Polish Principles of Bridge Technical Condition Assessment, three datasets were formed to quantify the condition of the bridge components made from reinforced concrete: bridge deck, span structures, and piers and abutments. This study created and compared the performance of five AI-based models: XGBoost, Decision Trees, Random Forest, Support Vector Regression, and Artificial Neural Networks (ANNs). The initial comparison revealed relatively low performance across all models, with the ANN model showing a slight advantage. Subsequently, nine ANN models were optimized to achieve higher performance levels. The performance of models was conducted based on a comparison of mean absolute percentage error (MAPE) and R² metrics. The ANN model with ReLU activation functions for hidden layers and the RMSprop optimizer achieves optimal performance at 100 epochs (MAPE = 3.5%; R² = 0.994). The practical implementation of such a model can considerably reduce uncertainties stemming from subjective expert judgments and enhance the accuracy of assessments.

Crack segmentation in concrete bridge structures is a critical task for ensuring safety and durability. This study focuses on evaluating and improving the performance of various deep learning models for crack segmentation, including U-Net, SegNet, ENet, HRNet, FastFCN, and DeepLab V3+. A novel approach is proposed which integrates a probability map generated by an ensemble of classification models as an additional input channel for segmentation models. This method demonstrated significant improvements in segmentation quality, increasing the IoU by up to 25.91% and F1 score by 15.39% compared with baseline models. These improvements were achieved through the use of additional spatial information provided by the probability map, enabling the models to detect cracks more precisely. Additionally, to evaluate the relevance of this approach, the results were compared with YOLO11x-seg, the latest and largest version for segmentation. These findings highlight that integrating auxiliary data channels into neural network architectures holds promise for enhancing segmentation accuracy in real-world engineering applications. The results of this study provide valuable insights for structural engineers and researchers working on automated crack detection, contributing to the development of reliable tools for structural health monitoring.

Bridges are one of the key elements of the transportation infrastructure of each country, and the reliability of the entire transportation network depends on their functioning. Maintaining bridges in proper technical condition is the main task of bridge management, for which more and more countries use bridge management systems (BMS). This study is devoted to comparing different BMS, reviewing the main functions and modules and determining the perspectives of development and implementation of the latest technologies in BMS. The analysed bridge management systems were compared by functions such as storage of bridge passport data, initial information on the bridge condition, assessment of the bridge condition, forecasting of the bridge condition and consideration of different maintenance strategies. Some systems are distinguished by the fact that they can predict future bridge condition, offer optimal maintenance strategies and take into account losses not only for the operation of structures, but also for transportation. Prospects for the development of BMS were also identified: the use of neural networks, the introduction of building information modelling (BIM) and the Internet of Things (IoT).

Asphalt crack segmentation is essential for preventive maintenance but is sensitive to noise, viewpoint, and illumination. This study evaluates a minimally invasive strategy that augments standard RGB input with an auxiliary fourth channel—a crack-probability map generated by a multi-scale ensemble of classifiers—and injects it into segmentation backbones. Field imagery from unmanned aerial vehicles and action cameras was used to train and compare U-Net, ENet, HRNet, and DeepLabV3+ under unified settings; the probability map was produced by an ensemble of lightweight convolutional neural networks (CNNs). Across models, the four-channel configuration improved performance over three-channel baselines; for DeepLabV3+, the Intersection over Union (IoU) increased by 6.41%. Transformer-based classifiers, despite strong accuracy, proved less effective and slower than lightweight CNNs for probability-map generation; the final ensemble processed images in approximately 0.63 s each. Integrating ensemble-derived probability maps yielded consistent gains, with the best four-channel CNNs surpassing YOLO11x-seg and Transformer baselines while remaining practical. This study presents a systematic evaluation showing that probability maps from classifier ensembles can serve as an auxiliary channel to improve segmentation of asphalt pavement cracks, providing a novel modular complement or alternative to attention mechanisms. The findings demonstrate a practical and effective strategy for enhancing automated pavement monitoring.