نوع مقاله : مقاله پژوهشی
نویسندگان
1 دانشجوی کارشناسی ارشد، گروه مهندسی عمران، دانشکده فنی و مهندسی، دانشگاه لرستان، خرم آباد، ایران
2 استاد، گروه مهندسی عمران، دانشکده فنی و مهندسی، دانشگاه لرستان، خرم آباد، ایران
3 دانشجوی دکتری مهندسی سازه، دانشگاه آزاد اسلامی واحد علوم و تحقیقات، تهران، ایران
چکیده
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Self-compacting concrete (SCC) is a dynamic field in construction worldwide. This type of concrete encompasses a wide range of mix designs that possess the necessary fresh and hardened concrete properties for specific applications. Although strength remains the primary criterion for the success of SCC, its fresh concrete properties are significantly broader than those of conventional vibrated concrete. These desirable properties must be maintained during placement and at the site. SCC is a preferred option in cases where reinforcement bars are densely arranged. Moreover, the absence of the need for vibrators significantly reduces environmental noise pollution. Despite its favorable features, the mix design and execution of SCC depend on various factors, such as the gradation of aggregates, the type of additives, and the fillers used. Considering each of these criteria influences the quality of hardened concrete and the workability of fresh concrete. This research has been conducted due to the need for improving accuracy and efficiency in SCC mix design and reducing the time and cost of physical testing. In this paper, the strength of SCC has been predicted using laboratory data and the application of artificial neural networks. The results indicate a high level of accuracy in the estimates made through soft computing techniques.
کلیدواژه‌ها [English]
)
ارسال نظر در مورد این مقاله