## 1 Introduction  
The construction industry has traditionally been slow to embrace new technologies, but in recent years, the use of artificial intelligence (AI) has gained momentum. Despite initial reluctance, the benefits of using artificial intelligence in construction are becoming unequivocal, with the potential to increase efficiency, reduce costs, and decrease time spent on repetitive tasks [1]. Artificial intelligence methods are applied in research in the construction industry in various fields. Different studies, e.g., [2], reveal that artificial intelligence methods can be successfully used to detect and/or evaluate damage to engineering structures, i.e., bridges and viaducts. More specifically, in [3], a neural network to predict the deterioration of bridge structures based on input parameters obtained from previous maintenance records was proposed. Two different fully connected neural networks were used to establish maintenance cost prediction models in [4]. A deep neural network (DNN) model was applied for damage detection in a truss bridge in [5]. In another article [6], the authors used neural networks to analyze bridge structure condition reports to identify the main factors causing damage to structures. Neural networks are also applied to detect irregularities in steel structures during data acquisition by camera [7]. In another study, a neural network model is developed to improve the bridge management system (BMS) by improving the prediction accuracy of the deterioration of bridge decks, superstructures, and substructures [8]. In addition, [PERSON] and co-workers [9] used neural networks to analyze the quality of feeding cable pipes into erection supports, using data from the measured signal that ran through the prestressed cables as input variables. Another article [10] presented a method for selective testing of railroad bridges based on condition assessment using a backpropagation neural network, and fuzzy and dominant analytic hierarchy process methods. Furthermore, [PERSON] et al. [11] proposed the use of neural networks to analyze sensor data to determine the impact on the bridge structure by either a passing train or a vehicle. [PERSON] et al. [12] used a bi-directional recurrent neural network to reconstruct data collected from sensors on bridge structures, considering spatial and temporal correlations between the data. [PERSON] et al. [13] compared different methods (neural networks, the evidential reasoning approach, multiple regression) to evaluate the risk of individual structures based on different criteria such as safety, functionality, durability, and the environment. They concluded that the use of neural networks provided the best results. Recently, some authors, e.g., [14], proposed specific improved artificial intelligence techniques for solving various problems in the construction industry (i.e., sorting of construction and demolition waste, classification of excavator activities, dynamic attitude and position prediction in tunnel construction). Similarly, [PERSON] et al. [15] presents a novel bridge inspection protocol using convolutional neural network-based machine learning and visibility-enhanced images (adjusting brightness, contrast, and sharpness) captured by unmanned aerial vehicles. The summarized literature shows that there are relatively few AI applications in the field of bridge structures, and the majority of them are limited to automation in detecting and/or evaluating damage, detecting structural irregularities, predicting structural deterioration, evaluating the risk of individual structures based on different criteria, etc. Only a few known applications have addressed bridge design at several levels, including conceptual design, aesthetics, analysis, loads, and other subtasks. The first one [16] used the expert system to leverage the ability of experienced engineers to effectively capture and apply heuristic knowledge, thus improving the quality and efficiency of decision-making in the early stages of prestressed concrete bridge design.