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The variance between the winning bid and the owner's estimated cost (OEC) is one of the construction management risks in the pre-tendering phase. The study aims to enhance the quality of the owner's estimation for predicting precisely the contract cost at the pre-tendering phase and avoiding future issues that arise through the construction phase.

This paper integrated artificial neural networks (ANN), deep neural networks (DNN) and time series (TS) techniques to estimate the ratio of a low bid to the OEC (R) for different size contracts and three types of contracts (building, electric and mechanic) accurately based on 94 contracts from King Saud University. The ANN and DNN models were evaluated using mean absolute percentage error (MAPE), mean sum square error (MSSE) and root mean sums square error (RMSSE).

The main finding is that the ANN provides high accuracy with MAPE, MSSE and RMSSE a 2.94%, 0.0015 and 0.039, respectively. The DNN's precision was high, with an RMSSE of 0.15 on average.

The owner and consultant are expected to use the study's findings to create more accuracy of the owner's estimate and decrease the difference between the owner's estimate and the lowest submitted offer for better decision-making.

This study fills the knowledge gap by developing an ANN model to handle missing TS data and forecasting the difference between a low bid and an OEC at the pre-tendering phase.

Heating, ventilating, air-conditioning and cooling (HVAC) systems are crucial in daily health-care facility services. Design-related defects can lead to maintenance issues, causing service disruptions and cost overruns. These defects can be avoided if a link between the early design stages and maintenance feedback is established. This study aims to use experts’ experience in HVAC maintenance in health-care facilities to list and evaluate the risk of each maintenance issue caused by a design defect, supported by the literature.

Following semistructured interviews with experts, 41 maintenance issues were identified as the most encountered issues. Subsequently, a survey was conducted in which 44 participants evaluated the probability and impact of each design-caused issue.

Chillers were identified as the HVAC components most prone to design defects and cost impact. However, air distribution ducts and air handling units are the most critical HVAC components for maintaining healthy conditions inside health-care facilities.

The unavailability of comprehensive data on the cost impacts of all design-related defects from multiple health-care facilities limits the ability of HVAC designers to furnish case studies and quantitative approaches.

This study helps HVAC designers acquire prior knowledge of decisions that may have led to unnecessary and avoidable maintenance. These design-related maintenance issues may cause unfavorable health and cost consequences.

Although there is a boom in the construction industry in the Kingdom of Saudi Arabia (KSA), it is yet to fully adopt building information modeling (BIM), which has received a lot of attention in the US, UK and Australian construction industries. Thus, the purpose of this paper is to provide the current state of the art in BIM implementation in Saudi Arabia, as well as perceived benefits and barriers through a case study.

A broad overview of BIM, the construction industry in KSA and the research and implementation of BIM in KSA was presented in this study. The research further established the perceived benefits and barriers of BIM implementation through a case study of a local AEC firm. A questionnaire survey was used to obtain lessons learned from the BIM team of the pilot project and was further analyzed using the RII approach.

The study’s findings include the lack of policy initiatives in KSA to enforce BIM in the construction industry, as well as the lack of sufficient research in the domain of BIM in KSA. Furthermore, the case study also revealed that the most important benefit of BIM adoption is “detection of inter-disciplinary conflicts in the drawings to reduce error, maintain design intent, control quality and speed up communication,” whereas the most important barrier is “the need for re-engineering many construction projects for successful transition towards BIM.”

The study provides a background for enhanced research towards the implementation of BIM in Saudi Arabia and also demonstrates the potential benefits and barriers in BIM implementation.

Blockchain, a groundbreaking technology that recently surfaced, is under thorough scrutiny due to its prospective utility across different sectors. This research aims to delve into and assess the cognitive elements that impact the integration of blockchain technology (BT) within library environments.

Utilizing the Stimulus–Organism–Response (SOR) theory, this research aims to facilitate the implementation of BT within academic institution libraries and provide valuable insights for managerial decision-making. A two-staged deep learning structural equation modelling artificial neural network (ANN) analysis was conducted on 583 computer experts affiliated with academic institutions across various countries to gather relevant information.

The research model can correspondingly expound 71% and 60% of the variance in trust and adoption intention of BT in libraries, where ANN results indicate that perceived possession is the primary predictor, with a technical capability factor that has a normalized significance of 84%. The study successfully identified the relationship of each variable of our conceptual model.

Unlike the SOR theory framework that uses a linear model and theoretically assumes that all relationships are significant, to the best of the authors’ knowledge, it is the first study to validate ANN and SEM in a library context successfully. The results of the two-step PLS–SEM and ANN technique demonstrate that the usage of ANN validates the PLS–SEM analysis. ANN can represent complicated linear and nonlinear connections with higher prediction accuracy than SEM approaches. Also, an importance-performance Map analysis of the PLS–SEM data offers a more detailed insight into each factor's significance and performance.

This study aims to empirically analyze the symmetries and asymmetries among the critical factors affecting building information modeling (BIM) implementation between countries with different income levels. To achieve that aim, the study objectives are to identify: critical factors affecting BIM implementation in low-, lower-middle-, upper-middle- and high-income countries; overlapping critical factors between countries with different income levels; and agreements on the critical factors between countries with different income levels.

This study identified potential BIM implementation factors using a systematic literature review and semi-structured interviews with architectural, engineering and construction (AEC) professionals. Then, the factors were inserted into a questionnaire survey and sent to AEC professionals in Afghanistan, India, Malaysia and Saudi Arabia. The collected data was analyzed using the following techniques and tests: mean, standard deviation, normalized value, Kruskal–Wallis, Dunn and Mann–Whitney.

Five critical factors overlap between all countries: “availability of guidelines for implementing BIM,” “cost-benefit of implementing BIM,” “stakeholders’ willingness to learn the BIM method,” “consistent views on BIM between stakeholders” and “existence of standard contracts on liability and risk allocation.” Also, the criticality of the factors often differs between income levels, especially between low- and high-income countries, suggesting a significant gap between low- and high-income countries in BIM implementation.

This study differs from prior works by empirically analyzing the symmetries and asymmetries in BIM implementation factors between countries with different income levels (i.e. low-, lower-middle-, upper-middle- and high-income countries).