Amr S. Allam, Hesham Bassioni, Mohammed Ayoub and Wael Kamel
This study aims to compare the performance of two nature-inspired metaheuristics inside Grasshopper in optimizing daylighting and energy performance against brute force in terms…
Abstract
Purpose
This study aims to compare the performance of two nature-inspired metaheuristics inside Grasshopper in optimizing daylighting and energy performance against brute force in terms of the resemblance to ideal solution and calculation time.
Design/methodology/approach
The simulation-based optimization process was controlled using two population-based metaheuristic algorithms, namely, the genetic algorithm (GA) and particle swarm optimization (PSO). The objectives of the optimization routine were optimizing daylighting and energy consumption of a standard reference office while varying the urban context configuration in Alexandria, Egypt.
Findings
The results from the GA and PSO were compared to those from brute force. The GA and PSO demonstrated much faster performance to converge to design solution after conducting only 25 and 43% of the required simulation runs, respectively. Also, the average proportion of the resulted weighted sum optimization (WSO) per case using the GA and PSO to that from brute force algorithm was 85 and 95%, respectively.
Originality/value
The work of this paper goes beyond the current practices for showing that the performance of the optimization algorithm can differ by changing the urban context configuration while solving the same problem under the same design variables and objectives.
Details
Keywords
Amer Fahmy, Tarek Hassan, Hesham Bassioni and Ronald McCaffer
Basic project control through traditional methods is not sufficient to manage the majority of real-time events in most construction projects. The purpose of this paper is to…
Abstract
Purpose
Basic project control through traditional methods is not sufficient to manage the majority of real-time events in most construction projects. The purpose of this paper is to propose a Dynamic Scheduling (DS) model that utilizes multi-objective optimization of cost, time, resources and cash flow, throughout project construction.
Design/methodology/approach
Upon reviewing the topic of DS, 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 implications
Optimized real-time scheduling can provide better resources management including labor utilization and cost efficiency. Furthermore, DS contributes to optimum materials procurement, thus minimizing waste.
Social implications
Optimized real-time scheduling can provide better resources management including labor 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.
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Amr A. Mohy, Hesham A. Bassioni, Elbadr O. Elgendi and Tarek M. Hassan
The purpose of this study is to investigate the potential of using computer vision and deep learning (DL) techniques for improving safety on construction sites. It provides an…
Abstract
Purpose
The purpose of this study is to investigate the potential of using computer vision and deep learning (DL) 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 DL 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 DL 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 DL 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/value
This study provides valuable insights into the potential of computer vision and DL 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.
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Hesham S. Ahmad, Maha D. Ayoush and Majed S. Al-Alwan
The purpose of this paper is to investigate the main causes of delay in public construction projects. This is motivated by feedback from public construction experts concerning…
Abstract
Purpose
The purpose of this paper is to investigate the main causes of delay in public construction projects. This is motivated by feedback from public construction experts concerning substantive delays during the last decade. The study thus seeks to help decision makers in Jordan and elsewhere identify problems and develop mitigating strategies.
Design/methodology/approach
Causes of delay were identified from previous related studies and then augmented after consultation with experts. This resulted in 56 delay factors classified into eight groups. The sampling frame for the study was defined in terms of public construction projects (mostly related to roads) owned by the Ministry of Public Works and Housing in Jordan. A survey was conducted with engineers working as representatives of the owner, contractors or consultants to elicit and evaluate the importance of the 56 delay factors.
Findings
Overall, 113 completed questionnaire responses were returned and analyzed to rank the causes of delay using the relative importance index method. Owners and consultants showed more interest in factors related to themselves, while contractors showed highest interest in an external factor related to the owner of services. Four recommendations are put forward for decision makers to mitigate against delays.
Originality/value
This research investigates a relatively large number of delay factors compared to other studies and these are categorized into groups to facilitate thematic understanding. Further, compared to previous related research, this research fills a gap by exploring the opinions of different contract parties.