Megaprojects are often associated with poor delivery performance and poor benefits realization. This article provides a method of identifying, in a quantitative and rigorous manner, the characteristics related to project management success in megaprojects. It provides an investigation of how stakeholders can use this knowledge to ensure more effective design and delivery for megaprojects. The research is grounded in 44 mega-projects and a systematic, empirically based methodology that employs the Fisher's exact test and machine learning techniques to identify the correlation between megaprojects’ characteristics and performance, paving the way to an understanding of their causation.

The paper presents and analyzes the state-of-the-art machine learning techniques that can be applied as a decision-support system in the estimation of resource consumption in the construction of reinforced concrete and prestressed concrete road bridges. The formed database on the consumption of concrete in the construction of bridges, along with their project characteristics, was the basis for the formation of the assessment model. The models were built using information from 181 reinforced concrete bridges in the eastern and southern branches of Corridor X in Serbia, with a value of more than 100 million euros. The application of artificial neural network models (ANNs), models based on regression trees (RTs), models based on support vector machines (SVM), and Gaussian processes regression (GPR) were analyzed. The accuracy of each model is determined by multi-criterion evaluation against four accuracy criteria root mean square error (RMSE), mean absolute error (MAE), Pearson’s linear correlation coefficient (R), and mean absolute percentage error (MAPE). According to all established criteria, the model based on GPR demonstrated the greatest accuracy in calculating the concrete consumption of bridges. According to the study, using automatic relevance determination (ARD) covariance functions results in the most accurate and optimal models and also makes it possible to see how important each input variable is to the model’s accuracy.

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Estimation of basic material consumption in civil engineering is very important in the initial phases of project implementation. Its importance is reflected in the impact of material quantities on forming the prices of individual positions, hence on forming the total cost of construction. The construction companies use the estimate of material quantity, among other things, as a base to make a bid on the market. The precision of the offer, taking into account the overall conditions of the business realization, directly influences the profit that the company can make on a specific project. In the early stages of project implementation, there are not enough available data, especially when it comes to the data needed to estimate material consumption, and therefore, the accuracy of material consumption estimation in the early stages of project realization is smaller. The paper presents the research on the use of artificial intelligence for the estimation of concrete and reinforcement consumption and the selection of optimal models for estimation. The estimation model was developed by using artificial neural networks. The best artificial neural network model showed high accuracy in material consumption estimation expressed as the mean absolute percentage error, 8.56% for concrete consumption estimate and 17.31% for reinforcement consumption estimate.

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Due to numerous reasons, construction projects often fail to achieve the planned duration. Detecting causes of delays (CoD) is the first step in eliminating or mitigating potential delays in future projects. The goal of research is unbiased CoD detection at a single project level, with the ultimate goal to discover the root causes of delay. The existing approach is based on expert knowledge which is used to create CoD lists for projects in general or groups of similar projects. When applied to a single project, it is burdened with bias, as shown on a case project returning low Spearman Rank correlation values. This research introduces a Delay Root causes Extraction and Analysis Model—DREAM. The proposed model combines expert knowledge, machine learning techniques, and Minutes of Meetings (MoM) as an unutilized extensive source of information. In the first phase, DREAM outputs a CoD list based on occurring frequency in MoM with satisfactory recall values, significantly reducing expert-induced subjectivism. In the second phase, enabled by MoM dates, DREAM adds another dimension to delay analysis—temporal CoD distribution. By analyzing corresponding informative charts, experts can understand the nature of delays and discover the root CoD, allowing intelligent decision making on future projects.

One basic criteria for determining the replacement time of construction machinery is based on the age , that is, the increase in operating costs of the machine with time. The key factor which determines and justifies replacement at a given time is the magnitude of operating costs. This analysis will show a method for replacement planning and sale of machines in a future time period.

Fire-resistance prognostic model for reinforced concrete columns ThR prRdiction modRl usRd for dRfining firR rRsistancR of rRinforcRd concrRtR columns RxposRd to standard firR from all four sidRs is prRsRntRd in thR papRr. ThR proposRd modRl rRliRs on thR concRpt of artificial nRural nRtworks, in which numRrical analysis rRsults arR usRd as input paramRtRrs. A briRf dRscription of thR modRlling procRss is givRn, and an appropriatR RxamplR of thR nRural nRtwork prognostic modRl is prRsRntRd.