Dynamic scheduling model for the construction industry

Purpose:Basic project control through traditional methods is not sufficient to manage the majority of realtime events in most construction projects. This paper proposes a Dynamic Scheduling (DS) model that utilizes multi-objective optimization of cost, time, resources and cashflow, throughout project construction.Design/methodology/approach:Upon reviewing the topic of Dynamic Scheduling, a worldwide Internet survey with 364 respondents was conducted to define end-user requirements. The model was formulated and solution algorithms discussed. Verification was reported using predefined problem sets and a real-life case. Validation was performed via feedback from industry experts.Findings:The need for multi-objective dynamic software optimization of construction schedules and the ability to choose among a set of optimal alternatives were highlighted. Model verification through well-known test cases and a real-life project case study showed that the model successfully achieved the required dynamic functionality whether under the small solved example or under the complex case study. The model was validated for practicality, optimization of various DS schedule quality gates, ease of use, and software integration with contemporary project management practices.Practical/Social implications:Optimized real-time scheduling can provide better resources management including labour utilization and cost efficiency. Furthermore, DS contributes to optimum materials procurement, thus minimizing waste.Originality/value:The paper illustrates the importance of DS in construction, identifies the user needs, and overviews the development, verification and validation of a model that supports the generation of high quality schedules beneficial to large scale projects.</div

Innovations in safety management for construction sites: The role of deep learning and computer vision techniques

Purpose: This study investigates the potential of using computer vision and deep learning techniques for improving safety on construction sites. It provides an overview of the current state of research in the field of construction site safety (CSS) management using these technologies. Specifically, the study focuses on identifying hazards and monitoring the usage of Personal Protective Equipment (PPE) on construction sites. The findings highlight the potential of computer vision and deep learning to enhance safety management in the construction industry.  Design/Methodology/Approach:  The study involves a scientometric analysis of the current direction for using computer vision and deep learning for CSS management. The analysis reviews relevant studies, their methods, results, and limitations, providing insights into the state of research in this area.  Findings:  The study finds that computer vision and deep learning techniques can be effective for enhancing safety management in the construction industry. The potential of these technologies is specifically highlighted for identifying hazards and monitoring PPE usage on construction sites. The findings suggest that the use of these technologies can significantly reduce accidents and injuries on construction sites. Originality:  This study provides valuable insights into the potential of computer vision and deep learning techniques for improving safety management in the construction industry. The findings can help construction companies adopt innovative technologies to reduce the number of accidents and injuries on construction sites. The study also identifies areas for future research in this field, highlighting the need for further investigation into the use of these technologies for CSS management.</p

CMBAS tool for assessing BIM adoption status in construction markets: application for Egypt

Countless efforts were conducted worldwide to obtain the BIM position of markets. Yet, a gap in the contemporary literature to comprehensively assess construction markets’ BIM adoption status (CMBAS) exists. A comprehensive assessment will assist decisionmakers in making insightful decisions regarding the wide adoption of BIM in order to make appropriate strategic decisions. The authors designed a tool to detect the BIM adoption status of construction markets comprehensively to ensure a complete understanding of CMBAS when designing BIM initiatives. The tool was applied to Egypt as an example of a developing market and verified and validated through experts’ reviews. A comparative analysis was performed to benchmark the BIM status of this market throughout the years. Appropriate research methods, sampling techniques, and descriptive and inferential analyses were deployed. It was concluded that BIM professionals in Egypt can play an essential role in spreading BIM to the construction market. The market is changing from the use of AutoCAD 2D to other BIM tools; however, more than half the market reached high BIM levels mostly through self-training, particularly with the deficiency of relevant university courses. A CMBAS tool will facilitate benchmarking BIM status among countries to assist in closing technological gaps with the evolving digital transformation.</p