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All Journal Civil Engineering Journal
Alrasheed, Khaled A.
Unknown Affiliation
Civil Engineering, Building, Construction & Architecture

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Journal : Civil Engineering Journal

 1 
The Challenges of Implementing Cognitive Computing in Small Construction Projects: A Data-Driven Perspective Alsehaimi, Abdullah; Alrasheed, Khaled A.; Hayat, Saleh; Nisar, Saad; Benjeddou, Omrane
Civil Engineering Journal Vol 10, No 9 (2024): September
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2024-010-09-011

Abstract

This study aims to identify and analyze the challenges of implementing cognitive computing in small construction projects, where decision-making, process optimization, and sustainability enhancements are crucial yet challenging. The research adopts a mixed-methods approach, integrating a thorough literature review, quantitative evaluation, and structural equation modeling (SEM) to explore the relationships between the identified barriers and the effective application of cognitive computing. The findings reveal significant hurdles, including complexity in customization (β = 0.327, t = 9.848, p < 0.001), data integrity and integration issues (β = 0.389, t = 14.534, p < 0.001), financial and cultural constraints (β = 0.295, t = 7.850, p < 0.001), and ethical and privacy concerns (β = 0.319, t = 8.963, p < 0.001). These barriers impede the seamless adoption of cognitive computing technologies. This research contributes novel insights into the specific challenges faced by small construction projects and provides practical recommendations to overcome these obstacles. By addressing these challenges, this study offers valuable guidance for stakeholders aiming to leverage cognitive computing to improve project outcomes in the construction industry. The novelty of this research lies in its focus on small-scale projects, a relatively underexplored area, and its comprehensive analysis of the multifaceted barriers that hinder the successful implementation of cognitive computing. Doi: 10.28991/CEJ-2024-010-09-011 Full Text: PDF
Optimizing Injection Moulding Processes for Structural Components in Construction Management Qurashi, Muhammad Nasir; Xiao, Cheng-Long; Alrasheed, Khaled A.; Benjeddou, Omrane
Civil Engineering Journal Vol 10, No 10 (2024): October
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2024-010-10-020

Abstract

The optimization of injection molding processes for structural components is critical in construction management, particularly for enhancing precision, efficiency, and sustainability. However, existing research has not fully addressed the complex interplay of factors that influence this optimization. This study aims to fill this gap by identifying and analyzing five key constructs: Structural Performance, Material Efficiency, Sustainability and Integration, Precision and Consistency, and Design Flexibility. Data were collected from 249 professionals in China using a Likert-scale survey and analyzed through Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), and Structural Equation Modeling (SEM). The results show that Structural Performance is the most significant factor (β = 0.943, p < 0.001), followed by Material Efficiency (β = 0.858, p < 0.001) and Sustainability and Integration (β = 0.772, p < 0.001). The model's predictive relevance, with a Q² value of 0.659, confirms its robustness and accuracy. These findings highlight the need for construction managers to focus on improving Structural Performance and Material Efficiency while integrating sustainability and ensuring precision and flexibility. Optimizing injection molding for construction components is challenging due to complex factors like structural performance, material efficiency, and sustainability. This study develops a novel framework using Structural Equation Modeling to rank these factors, providing insights for cost-effective, high-performance outcomes, and advancing sustainable practices in construction management. Doi: 10.28991/CEJ-2024-010-10-020 Full Text: PDF
Impact of the Application of Smart Sensor Networks for the Construction Management of Geotechnical Activities Alselami, Nimer; Aati, Khaled; Mutnbak, Mohammed; Alrasheed, Khaled A.; Basit Khan, Muhammad
Civil Engineering Journal Vol 11, No 1 (2025): January
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2025-011-01-020

Abstract

The primary objective of this study is to evaluate the impact of smart sensor networks on geotechnical data management, specifically enhancing accuracy, real-time monitoring, safety, and reliability. To achieve this, data was collected through a survey of 380 geotechnical professionals in Saudi Arabia, with 106 valid responses analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Principal Component Analysis (PCA) and Factor Analysis (FA) were employed to identify the key variables and underlying relationships among them. The findings demonstrate that smart sensor networks significantly improve the accuracy of geotechnical data (path coefficient = 0.662), real-time monitoring and early warning systems (path coefficient = 0.701), safety and risk management (path coefficient = 0.761), and data reliability (path coefficient = 0.410). This study introduces a novel framework integrating advanced statistical methods with smart sensor networks, offering a practical approach to optimizing geotechnical operations. The research highlights the importance of advanced data analytics in enhancing the full potential of smart sensors, presenting an innovative solution for improving decision-making and risk management in geotechnical engineering. These findings provide a significant contribution to sustainable and effective geotechnical practices. Doi: 10.28991/CEJ-2025-011-01-020 Full Text: PDF
Co-Authors Aati, Khaled Alsehaimi, Abdullah Alselami, Nimer Basit Khan, Muhammad Benjeddou, Omrane Hayat, Saleh Mutnbak, Mohammed Nisar, Saad Qurashi, Muhammad Nasir Xiao, Cheng-Long