Offer preparation has always been a specific part of a building process which has significant impact on company business. Due to the fact that income greatly depends on offer’s precision and the balance between planned costs, both direct and overheads, and wished profit, it is necessary to prepare a precise offer within required time and available resources which are always insufficient. The paper presents a research of precision that can be achieved while using artificial intelligence for estimation of cost and duration in construction projects. Both artificial neural networks (ANNs) and support vector machines (SVM) are analysed and compared. The best SVM has shown higher precision, when estimating costs, with mean absolute percentage error (MAPE) of 7.06% compared to the most precise ANNs which has achieved precision of 25.38%. Estimation of works duration has proved to be more difficult. The best MAPEs were 22.77% and 26.26% for SVM and ANN, respectively.

The management of roads, as well as their maintenance, calls for an adequate assessment of the load-bearing capacity of the pavement structure. This serves as the basis on which future maintenance requirements are planned and plays a significant role in determining whether the rehabilitation or reconstruction of the pavement structure is required. The stability of the pavement structure depends on a large number of parameters, and it is not possible to fully assess all of them when making an estimation. One of the most significant parameters is the modulus of elasticity of asphalt layers (E_AC). The goal of this study is to use models based on machine learning to perform a quick and efficient assessment of the modulus of elasticity of asphalt layers, as well as to compare the formed models. The paper defines models for E_AC estimation using machine learning, in which the input data include the measured deflections and the temperature of the upper surface of the asphalt layer. Analyses of modeling using artificial neural networks (ANNs), support vector machines (SVMs) and boosted regression trees (BRT) were compared. The SVM method showed a higher accuracy in estimating the E_AC modulus, with a mean absolute percentage error (MAPE) of 7.64%, while the ANN method and the BRT achieved accuracies of 9.13% and 8.84%, respectively. Models formed in this way can be practically implemented in the management and maintenance of roads. They enable an adequate assessment of the remaining load-bearing capacity and the level of reliability of the pavement structure using non-destructive methods, at the same time reducing the financial costs.

Taking into consideration the significant impact of working force on the quality of realization of a construction process from the aspect of safety at work, it is essential to be familiar with the features of construction workers and identify their risk groups. Within the research, the analysis was carried out examining the influence of experience of workers in construction jobs at which they suffered an injury, along with the analysis of the influence of workers’ age on the occurrence of injuries. The type of works has an impact on the occurrence an injury only in the case when the works are realized by workers with less than 4 years of experience. It was determined that those types of works which require a certain level of professional training account for a smaller number of injuries compared with those types of work where “physical” work is dominant

The noticeable urgent need for the preservation of collective and cultural memory in Europe, coupled with a fear of loss of important aspects of heritage, indicates the importance of educating the youth who will become future policymakers in the field. Analyses of the survey conducted through this research show the current perspective of youth on the typology of World War II memorials on the territories of the former republics, now independent states, of SFR Yugoslavia in Southeast Europe. Targeting architecture and civil engineering students as a specific youth group who are directly involved in learning about the built memorial structures, as well as the phenomenological background of space, creation of place and memories, resulted in a qualitative observation of the group’s understanding and judgement of the topic. This article investigates the level of knowledge and emotional response to memorial heritage within the target group. It addresses relevant issues of age, gender, level of study, and respondents’ backgrounds, demonstrating the existence of an initial interest in memorial heritage among the architecture-oriented youth. With an additional focus on a possible active role in heritage preservation and future placement, the research indicates a segment in the youth’s comprehension of valuable memorial heritage that needs to be improved.

Architectural, engineering and construction (AEC) industry has large amounts of data, the analysis of which can predict risks such as project delays, project budget overruns, low resource use efficiency, environmental damage, reduced safety, and the like. Research has shown that 98% of construction projects are realized with the contracted budget and deadline exceeded. This paper aims to show the significant potential and possibilities of applying artificial intelligence (AI) in the AEC industry, pointing out the benefits and obstacles faced by the AEC industry during the implementation of AI. The benefits of applying AI in the AEC industry relate to increasing efficiency, reducing costs, increasing productivity, increasing safety, better planning and providing timely and accurate information. The key obstacles that hinder the implementation of AI in practice are high implementation costs, incomplete data, job loss due to automation, mistrust of AI technology, cyber vulnerability, and lack of understanding field of AI.