ارائه مدلی جهت ارزیابی ریسک‌های ایمنی به منظور افزایش مدیریت بهره‌وری در پروژه‌های ساختمانی با به‌کارگیری سیستم خبره فازی و الگوریتم ژنتیک
محورهای موضوعی : مدیریت پروژه
مهدی وکیل زاده
1
(دانشجوی دکتری گروه مهندسی عمران، واحد علوم و تحقیقات تهران، دانشگاه آزاد اسلامی، تهران، ایران)
محسنعلی شایانفر
2
(دانشیار گروه مهندسی عمران، دانشگاه علم و صنعت ایران، تهران، ایران)
مسعود ذبیحی سامانی
3
(استادیار گروه مهندسی عمران، واحد پرند، دانشگاه آزاد اسلامی، تهران،ایران)
مهدی روانشادنیا
4
(دانشیارگروه مهندسی عمران، واحد علوم و تحقیقات تهران، دانشگاه آزاد اسلامی، تهران، ایران)
کلید واژه: الگوریتم ژنتیک, منطق فازی, سیستم خبره, مدیریت بهره‌وری, ریسک ایمنی,
چکیده مقاله :
امروزه صنعت ساختمان سهم قابل‌توجهی در اقتصاد دارد و به‌عنوان صنعت پیشران و اشتغال‌زا محسوب می‌شود. با توجه به فعالیت‌های متنوع و گاهی خطرآفرین در طول انجام عملیات ساختمانی، احتمال وقوع خطرات و آسیب‌هایی جانی و مالی وجود دارد که کنترل و کاهش این خطرات می‌تواند در موفقیت پروژه بسیار تأثیرگذار باشد. در پژوهش حاضر، مدلی جهت ارزیابی ریسک‌های ایمنی پروژه‌های ساختمانی ارائه گردیده و از ابزارهای نو و قدرتمندی همچون سیستم خبره فازی بهبود یافته با الگوریتم ژنتیک بهره گرفته شده است. ازاین‌رو در ابتدا عوامل اصلی تأثیرگذار در ایمنی پروژه‌های ساختمانی با مطالعه پیشینة موضوع و مشاوره با افراد خبره به دست آمد. سپس پرسش‌نامه‌ای جهت کسب نظرات خبرگان و جهت ارزیابی میزان شدت اثر ریسک و احتمال وقوع هر یک از ریسک‌ها در اختیار خبرگان قرار گرفت. بر اساس نظرات خبرگان، سیستم خبره‌ای جهت ارزیابی ریسک‌ها به دست آمد که به‌جای استفاده از منطق صفر و یکی حاکم بر سیستم‌های خبره، از منطق فازی استفاده گردیده است. در مدل پیشنهادی جهت بهبود عملکرد سیستم خبره فازی، از الگوریتم ژنتیک به‌عنوان یک بهینه‌ساز استفاده‌شده است. نتایج تحقیق حاکی از کارایی مطلوب مدل پیشنهادی در ارزیابی ریسک‌های ایمنی پروژه‌های ساختمانی بوده، به‌نحوی‌که میزان خطای مدل قابل قبول است. همچنین مدل می‌تواند ابزاری کارآمد برای مدیران پروژه در ارزیابی و پالایش وضعیت ایمنی در کارگاه ساختمانی را ارائه نماید. با افزایش ایمنی و سازماندهی آن، ریسک تعطیلی کارگاه‌ها به علت فقدان ایمنی کم شده و مدیریت بهره‌وری در کارگاه‌های ساختمانی افزایش می‌یابد.
Today, the construction industry has a significant share of the economy and is considered as a leading and employment-generating industry. Due to various and sometimes risky activities during construction, there is a possibility of risks and personal and financial injuries, the control of which can be very effective in the success of the project. In the present study, a model for evaluating the safety risks of construction projects has been presented and new powerful tools such as fuzzy expert system improved with genetic algorithm have been used. Therefore, at first, the main factors influencing the safety of construction projects were identified by studying the literature and consulting with experts. Then, a questionnaire was provided to the experts to obtain their views and assess the severity of the risk effect and the probability of the occurrence of each risk. Then, based on the experts’ views, an expert system was obtained to assess the risks, which instead of using zero and one logic, governing expert systems, fuzzy logic was used. In the proposed model, to improve the performance of the fuzzy expert system, the genetic algorithm was employed as an optimizer. The research results indicated the optimal performance of the proposed model in assessing the safety risks of construction projects, so that the error rate of the model was acceptable. The model can also provide an effective tool for project managers in assessing and refining the safety situation in the construction site.
Alipouri, Yaqub, Ardeshir, Abdullah, Sibt, Mohammad Hassan, Fazel Zarandi, Mohammad Hussein. (2015). Using fuzzy expert system and genetic algorithm to score safety management performance in Iranian construction workshops: a study of safety environment factors and personal experience. AmirKabir Civil Engineering Journal, 31.2(4.1), 31-39. [In persian]
Ardeshir Abdullah, Mohajeri Mehdi, Amiri Mehran. (2014). Safety assessment in construction projects based on gray hierarchical and fuzzy analysis methods. Occupational health in Iran, 2(11), 87-98. [In persian]
Asad, M. M., and Hasan, R., B. (2014). A Systematic Review: Development Techniques and Utilization of Expert Systems Inferences for Health and Safety Environment in Oil & Gas and Petroleum Industries. . In .: Malaysia University Conference Engineering Technology.
Baron, P., Brázda, P., Dobránsky, J., & Kočiško, M. (2012). Expert system approach to safety management. Risk Analysis,44(8), 77-88.
Center, S. a. (2019). Special issue of World Occupational Health and Safety Day. Tehran: Statistics and Strategic Information Center. [In persian]
Farmakis, P. M. (2018)). Genetic algorithm optimization for dynamic construction site layout planning. Organization, technology & management in construction: an international journal, 10(1), 1655-1664.
Hamidi, H. (1395). Optimization of TSK type fuzzy system rules database using hybrid heuristics algorithms. Computational Intelligence in Electrical Engineering, 7(3), 47-68.
Khanjani, H., & Saharnorchei, G. H. (2016). Safety hazards of construction workshops and the role of the Engineering System Organization in reducing accidents (with emphasis on Tehran province). Third National Conference on Crisis. [In persian]
Martinez-Aires, M. D., Lopez-Alonso, M., & Martinez-Rojas, M. (2018). Safety science. Building information modeling and safety management: A systematic review, 101, 11-18.
Omidi, L., Zakerian, S. A., Saraji, J. N. (2018). Safety performance assessment among control room operators based on feature extraction and genetic fuzzy system in the process industry. Process. Safety and Environmental Protection, 116, 590-602.
Oveysi Oskooi, Amir Hossein and Ravanshadnia, Mehdi. (2013). Application of simulation in multi-objective optimization of earth operations. 7th National Congress of Civil Engineering. Zahedan. [In persian]
Oveysi Oskooi, Amirhossein and Ravanshadnia, Mehdi and Harischian, Mahmoud,. (2016). Determining the optimal arrangement of the fleet of ground operations machinery using genetic algorithm. Fourth National Conference on Applied Research in Civil Engineering. [In persian]
Patel, D. A., & Jha, K. N. (2016). Structural equation modeling for relationship-based determinants of safety performance in construction projects. Journal of management in engineering, 32(6).
Sanchez, F. A. S., Peláez, G. I. C., & Alis, J. C.. (2017). Occupational safety and health in construction: a review of applications and trends. Industrial health, 55(3), 210-218.
Sunindijo, R. Y., & Zou, P. X.. (2011). Political skill for developing construction safety climate. Journal of Construction Engineering and Management, 138(5), 605-612.
Suokas, J., Heino, P., & Karvonen, I. (1990). Expert systems in safety management. Journal of occupational accidents, 12(1), 63-78.
Taghinejad, A. (2017). Evaluating the effect of safety management on improving the safety performance of construction workshops and providing appropriate solutions. Tabari Institute of Higher Education. [In persian]
Taherkhani Farhad, Mirza Ebrahim Tehrani Mahnaz, Malmasi Saeed. (2016). Assessing safety risks based on fuzzy logic in subway construction projects. Journal of Occupational Health Engineering, 4(3), 49-62.
Tang, K. H. D., Dawal, S. Z. M., & Olugu, E. U. (2018). Integrating fuzzy expert system and scoring system for safety performance evaluation of offshore oil and gas platforms in Malaysia. Journal of Loss Prevention in the Process Industries, 56, 32-45.
Zhou, Z. G. (2015). Overview and analysis of safety management studies in the construction industry. Safety science, 72, 337-350.
_||_Alipouri, Yaqub, Ardeshir, Abdullah, Sibt, Mohammad Hassan, Fazel Zarandi, Mohammad Hussein. (2015). Using fuzzy expert system and genetic algorithm to score safety management performance in Iranian construction workshops: a study of safety environment factors and personal experience. AmirKabir Civil Engineering Journal, 31.2(4.1), 31-39. [In persian]
Ardeshir Abdullah, Mohajeri Mehdi, Amiri Mehran. (2014). Safety assessment in construction projects based on gray hierarchical and fuzzy analysis methods. Occupational health in Iran, 2(11), 87-98. [In persian]
Asad, M. M., and Hasan, R., B. (2014). A Systematic Review: Development Techniques and Utilization of Expert Systems Inferences for Health and Safety Environment in Oil & Gas and Petroleum Industries. . In .: Malaysia University Conference Engineering Technology.
Baron, P., Brázda, P., Dobránsky, J., & Kočiško, M. (2012). Expert system approach to safety management. Risk Analysis,44(8), 77-88.
Center, S. a. (2019). Special issue of World Occupational Health and Safety Day. Tehran: Statistics and Strategic Information Center. [In persian]
Farmakis, P. M. (2018)). Genetic algorithm optimization for dynamic construction site layout planning. Organization, technology & management in construction: an international journal, 10(1), 1655-1664.
Hamidi, H. (1395). Optimization of TSK type fuzzy system rules database using hybrid heuristics algorithms. Computational Intelligence in Electrical Engineering, 7(3), 47-68.
Khanjani, H., & Saharnorchei, G. H. (2016). Safety hazards of construction workshops and the role of the Engineering System Organization in reducing accidents (with emphasis on Tehran province). Third National Conference on Crisis. [In persian]
Martinez-Aires, M. D., Lopez-Alonso, M., & Martinez-Rojas, M. (2018). Safety science. Building information modeling and safety management: A systematic review, 101, 11-18.
Omidi, L., Zakerian, S. A., Saraji, J. N. (2018). Safety performance assessment among control room operators based on feature extraction and genetic fuzzy system in the process industry. Process. Safety and Environmental Protection, 116, 590-602.
Oveysi Oskooi, Amir Hossein and Ravanshadnia, Mehdi. (2013). Application of simulation in multi-objective optimization of earth operations. 7th National Congress of Civil Engineering. Zahedan. [In persian]
Oveysi Oskooi, Amirhossein and Ravanshadnia, Mehdi and Harischian, Mahmoud,. (2016). Determining the optimal arrangement of the fleet of ground operations machinery using genetic algorithm. Fourth National Conference on Applied Research in Civil Engineering. [In persian]
Patel, D. A., & Jha, K. N. (2016). Structural equation modeling for relationship-based determinants of safety performance in construction projects. Journal of management in engineering, 32(6).
Sanchez, F. A. S., Peláez, G. I. C., & Alis, J. C.. (2017). Occupational safety and health in construction: a review of applications and trends. Industrial health, 55(3), 210-218.
Sunindijo, R. Y., & Zou, P. X.. (2011). Political skill for developing construction safety climate. Journal of Construction Engineering and Management, 138(5), 605-612.
Suokas, J., Heino, P., & Karvonen, I. (1990). Expert systems in safety management. Journal of occupational accidents, 12(1), 63-78.
Taghinejad, A. (2017). Evaluating the effect of safety management on improving the safety performance of construction workshops and providing appropriate solutions. Tabari Institute of Higher Education. [In persian]
Taherkhani Farhad, Mirza Ebrahim Tehrani Mahnaz, Malmasi Saeed. (2016). Assessing safety risks based on fuzzy logic in subway construction projects. Journal of Occupational Health Engineering, 4(3), 49-62.
Tang, K. H. D., Dawal, S. Z. M., & Olugu, E. U. (2018). Integrating fuzzy expert system and scoring system for safety performance evaluation of offshore oil and gas platforms in Malaysia. Journal of Loss Prevention in the Process Industries, 56, 32-45.
Zhou, Z. G. (2015). Overview and analysis of safety management studies in the construction industry. Safety science, 72, 337-350.