Predicting schedule delays of construction projects in the oil and gas industry: comparative study Awsan Mohammed, Anas Bahatheq, Ahmed Ghaithan, Adel Alshibani, Khwaja Mateen Mazher, et al. Built Environment Project and Asset Management, 2026 Purpose Delays in oil and gas construction projects present major challenges due to the sector's complex operations, high capital investment and strict timelines. Such disruptions affect project execution and can have serious socioeconomic impacts. The purpose of this paper is to identify key factors contributing to schedule delays and develop predictive models using artificial neural networks (ANN), decision trees (DTs) and multiple linear regression (MLR) to estimate delay percentages. Design/methodology/approach This study adopts a mixed-methods approach, combining qualitative and quantitative techniques. Expert interviews and a literature review identified and prioritized key delay factors. Data from completed oil and gas construction projects selected through purposive sampling to reflect varied sizes, complexities and locations were analyzed using correlation and outlier tests to ensure reliability. Predictive models: ANNs, DTs and MLR, were then developed, trained and validated with real project data. Findings Design consulting experience, project location and scope changes are assessed as the leading factors influencing delay percentages in oil and gas construction projects. The MLR demonstrated the highest accuracy, exceeding 96%, outperforming both ANNs and DTs. Originality/value This paper focused on the quantitative models rather than the qualitative aspects of predicting schedule delays in construction projects in the oil and gas industry. The proposed models support planning stages by enabling more realistic project schedules and decision-making processes. The proposed models also empower project stakeholders to optimize project outcomes.
Enhancing the reliability of steam turbine-driven air blowers in industrial processing units: a maintenance-centric approach Awsan Mohammed, Talal AlShamrani, Adel Alshibani, Ahmed Ghaithan, Ahmed M. Attia Journal of Quality in Maintenance Engineering, 2026 Purpose This paper aims to enhance the reliability of steam turbine systems in sulfur recovery units (SRUs) within the gas processing industry. These turbines are essential for driving air blower systems in the Claus process, yet they frequently encounter reliability challenges such as high vibration, misalignment and component degradation. The paper seeks to identify critical failure points and propose a robust improvement strategy. Design/methodology/approach A quantitative reliability framework is applied using ten years of historical maintenance data. Component failure behavior is modeled using an exponential failure distribution. System reliability is evaluated through reliability block diagram (RBD) modeling for series–parallel configurations, while availability is assessed using MTBF and MTTR metrics. A quantitative risk-based criticality index combining failure rate and repair duration is employed to prioritize failure-prone subsystems and guide reliability improvement efforts. Findings The results identify the steam turbine (ST), air blower (AB), overspeed mechanical trip mechanism (OMM) and coupling (CT) as the most critical components impacting system reliability. The proposed optimized reliability model achieves system availability of 99%, contributing to enhancing operational continuity, reducing maintenance costs and improving overall system safety. Originality/value This research provides a novel, component-level reliability assessment model for SRU steam turbines. It offers practical guidance for improving turbine performance and supports broader industry efforts toward predictive maintenance and operational excellence by integrating risk-based prioritization and reliability metrics.
Exploring emerging barriers to BIM adoption for sustainable project tracking and control readiness in Saudi Arabia: an ISM-based approach Mohamed Mudawi, Naji Osman, Adel Alshibani, Mohammad A. Hassanain, Awsan Mohammed Engineering Construction and Architectural Management, 2026 Purpose Although BIM has advanced globally, its implementation still faces resistance and practical challenges. This paper examines barriers to implementing building information modeling (BIM) for project tracking and control readiness from the perspectives of architectural/engineering (A/E) firms and contractors in Saudi Arabia. Design/methodology/approach Literature review was integrated with expert interviews. Two approaches were utilized for ranking barriers to BIM adoption for sustainable project tracking and control readiness. The first was the relative importance index (RII) to assess obstacles facing A/E firms and contractors. The second was the interpretive structural modeling (ISM) to analyze interdependence and key influencing barriers. Findings The top barriers for A/E firms were a lack of client demand, insufficient software skills, poor data quality, reliance on traditional practices, and budget constraints. For contractors, the main challenges included budget limitations, lack of client demand, inadequate software skills, early contractor involvement and project type. The ISM analysis highlighted incomplete adoption across stakeholders as a key dependent variable, driven by fundamental barriers. Practical implications This study points out the main barriers to adopting BIM as a new digital technology for tracking and controlling ongoing construction projects during the construction stage. Originality/value An integrated TOE–RII–ISM approach was proposed for identifying and prioritizing the key barriers to adopting BIM in project tracking and control, from two perspectives (A/E and Contractors). The findings offer valuable insights into current practices by providing a quantitative method that uses advanced models to determine the relationships among key barriers.
Fuzzy-based model for developing a digitalization index for evaluating construction project digitalization levels in Saudi Arabia Awsan Mohammed, Yazeed AlSalamah, Ahmed Ghaithan, Ali Shash, Adel Alshibani Construction Innovation, 2026 Purpose This paper aims to propose a digitalization index (DI) based on a fuzzy approach for evaluating the digitalization level of construction projects in the Saudi Arabian construction sector. It also aims to identify and rank potential digitalization activities and evaluate these activities using modified technology acceptance measures. This provides a quantitative framework for assessing digitalization levels in uncertain environments. Design/methodology/approach The research identifies potential digitalization activities within construction projects in the Saudi Arabian construction sector and ranks them based on their digitalization level. A fuzzy-based model is then developed to construct a DI and evaluate the degree of digitalization. The model is validated using data from a real project in Saudi Arabia. Findings The findings revealed significant differences in project activities based on the DI within the Saudi construction sector. Deviation tracking, document system updates and energy consumption improvements were the most digitalized activities. The proposed fuzzy model accurately assessed digitalization, aligning with actual project data, highlighting the sector’s generally low digital maturity and the need for targeted digital transformation in Saudi construction management. Practical implications The proposed model offers a practical tool for construction stakeholders to assess and quantify digitalization activities. The model helps stakeholders address challenges in digital transformation and prioritize activities for improving digital integration in construction projects. Originality/value This research proposes a DI as an innovative approach to assess the digitalization levels in construction projects, using a fuzzy-based model to deal with uncertainty. It incorporates modified technology acceptance measures and provides an innovative method for quantifying digital transformation in an uncertain environment.
A Green Manufacturing Model: Joint Optimization of Maintenance, Scheduling, and Sustainability Proceedings of International Conference on Computers and Industrial Engineering CIE, 2025
ECONOMIC-ENVIRONMENTAL ASSESSMENT OF CONCENTRATED SOLAR POWER FOR SEAWATER DESALINATION Proceedings of International Conference on Computers and Industrial Engineering CIE, 2024
Multi-objective mathematical model for closed loop supply chain design Proceedings of the International Conference on Industrial Engineering and Operations Management, 2019