Construction delays are a prevalent issue worldwide, and Saudi Arabia is just a short distance from this experience. Although the Saudi construction industry was booming due to Vision 2030, major delays were recorded in numerous construction projects, resulting in extra costs and schedule overruns. Studies on Building Information Modeling (BIM) have increased recently due to their crucial role in managing construction projects. However, more efforts need to be made to define the impact value of adopting BIM using advanced techniques such as System Dynamics (SD). It also aims to demonstrate the BIM impacts globally to encourage taking advantage of such a system. Limited papers studied BIM impact in-depth using advanced techniques such as SD. Thus, A questionnaire was distributed to fifty-nine experts to measure the extent of BIM’s influence on the ten factors affecting project delay times discovered in the literature. The statistical analysis results were used to calculate each factor’s relative importance index (RII), compare them to the results obtained without BIM use, and calculate the relative difference for each factor. These relative differences were considered in the modified SD. The modified SD was applied to a case study representing an academic building in the King Saud University campus. The main finding revealed that BIM decreased construction project delays by 14.55%. In addition, this study found that BIM has a maximum influence on poor site management and supervision by contractors, with a reduction percentage of 17.65%, and a minimum influence on lack of contractor experience and managerial skills, with 11.76%. The SD analysis results confirmed that BIM has a significant impact on reducing construction project delays.

Forecasting the final construction contract duration at an early stage plays a vital role in the progress of a project. An inaccurate project duration prediction may lead to the project’s benefits being lost. It is essential to precisely predict the duration due to the presence of several different factors. This paper contributed to developing a model to predict final construction contract duration (FCCD) in the early stages based on parameters characterized as few and shared for any contract. (contract cost, contract duration, and sector). This paper developed an Artificial Neural Network (ANN) model based on 135 Saudi construction project data. The development model has three stages. The first stage was standardization and augmentation using Zavadskas and Turskis’ logarithmic and Pasini methods. The second and third stages were the first and second analyses of the ANN models, respectively. The first analysis aimed to promote the used data and integrate them into the second analysis to develop the ANN model. The ANN models were compared with three linear regression (LR) models (LR1, LR2, and LR3) and other models in the literature. The results revealed that the accuracy of the ANN model provides reasonable accuracy with an average mean absolute percentage error (MAPE) of 12.22%, which is lower than the LR3′s MAPE by 27.03%. The accuracy of the ANN model is similar to that of earned value management (EVM) in the previous study. This paper supports research to deal with relatively little data and integrate them into a neural network. The ANN model assists the stakeholder in making appropriate decisions for the project during the pre-tendering phase by predicting the actual contract duration based on the CC, CD, and project sector.

Building Information Modeling (BIM) has arisen as a promising technology that can enhance construction project outcomes through cost reduction when compared to conventional practices. More studies need to be conducted to examine the influence of BIM technology on cost overruns. Therefore, the primary aim of this paper is to investigate how BIM influences critical risk factors related to cost overruns. This investigation involves reevaluating and reranking these risk factors while exploring their interrelationships. Research involves a comprehensive literature review to identify risk factors correlated with cost overruns, the development of a survey questionnaire for BIM specialists, and an analysis of quantitative data using a partial least square structure equation model (PLS–SEM) to identify and examine significant risk factors and their interdependencies. The study’s findings revealed that BIM significantly mitigates the impact of risk factors associated with the awarding of contracts to the lowest bidder, decreasing its ranking from first to third. Furthermore, BIM effectively minimizes the influence of risk factors related to changed orders, relegating it from second position to tenth position. In addition, risk factors related to experience exerted a more significant influence on risk factors related to finances than the influence of finances on experience. Findings contribute to the knowledge base by evaluating the influence of BIM technology on risk factors and cost overruns in the Saudi construction industry, thereby offering insights for improved project management and control.

Raising the final contract cost (FCC) is a significant risk for project owners. This study hypothesizes that the factors that cause owner’s cost estimation (OCE) accuracy and FCC changes share the same causes, and a case study confirmed that the two variables (OCE and FCC) could be correlated. Accordingly, this study aims to develop a forecast model to predict FCC on the basis of the initial OCE, which has not been studied previously. This study utilized data from 34 Saudi Arabian projects. Two linear regression models developed the data, and the square root function transformed the data. Moreover, the artificial neural network (ANN) model was developed after data standardization using Zavadskas and Turskis’ logarithmic method. The results showed that the ANN model had a MAPE smaller than the two linear regression models. Using Zavadskas and Turskis’ logarithmic standardization method and elimination of data that had an absolute percentage error (APE) of more than 35% led to an increase in ANN model accuracy and provided a MAPE value of less than 8.5%.

Building façades represent one of the most critical elements affecting a city’s quality of life, and they impact the country’s economic income by attracting visitors. However, performance data on façades are limited or incomplete, making it challenging for designers to evaluate their effectiveness in energy efficiency, thermal performance, durability, and other key performance metrics. This paper presents a comprehensive framework for evaluating and prioritizing material selection criteria in building cladding, establishing the relationship with available alternatives, and integrating decision-making processes with Building Information Modeling (BIM) to automate the Value Engineering (VE) concept. The material selection criteria from the literature and international standard manual were identified, and their criteria weight was then evaluated using SWARA (stepwise weight assessment ratio analysis). Additionally, WASPAS (weighted aggregated sum product assessment) was utilized to evaluate the alternative cladding materials based on the defined criteria and their associated quality weight (QW). The life cycle cost (LCC) of the alternatives was computed. The VE was computed and then ranked based on the QW and LCC of the alternatives. The procedure was connected to the BIM model to automate the assessment, specifying the necessary parameters and the BIM computation. A case study of an office building façade was conducted to validate the proposed framework. In this study, the significant criteria were durability, wind load resistance, and thermal insulation. This approach enables executives to evaluate cladding selection, ensuring efficient decision-making processes. The proposed method and its results were subjected to expert testing, and the satisfaction rate exceeded 80%, confirming the framework’s reliability in evaluating alternatives. This paper enhances the understanding of material selection methodologies and provides a valuable contribution to the field of construction management.