Application of multiple linear regression in management of civil projects regarding crack analysis

Saeid Darrudi¹ Ali Ahmadi²

Abstract :
In currently developed societies, the executive management of large-scale projects plays an important role in economic, social and cultural progressive movements. Proper implementation of any project depends on its suitable and principled management, especially in construction projects that are related to different sections and usually involve many people. Effective use of correct structural design approaches, along with the utilization of appropriate materials and modern construction methods, ensures the quality of work and increases productivity in construction projects. In addition to steel, concrete is one of the most common types of materials for the construction of various structures. Today, concrete structures are widely used as various buildings and other components in the development of urban spaces. The main purpose of this paper is the accurate calculation of the width of cracks in concrete sections in terms of material properties, environmental conditions and loading so that by optimization of cement consumption and concrete mix design to better manage the projects related to concrete structures. Due to the lack of solid theoretical solutions, by modifying some assumptions in previous studies, new analytical approaches can be defined to more accurately predict the crack width in reinforced and unreinforced concrete sections. Furthermore, using the statistical models based on multiple linear regression (MLR) provides an excellent estimation for the cracking parameters of concrete members under the pure bending moment in actual construction projects and reduces the costs associated with the project.

Keywords : concrete structure crack cement consumption optimization mlr

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زبان مقاله : انگلیسی
محور مقاله : مهندسی عمران
محل انتشار : چهارمین کنگره بین المللی مهندسی، فناوری و نوآوری
آدرس وب سایت کنفرانس : eticong.com/4th
کد IOI مقاله : XEEA-HBADH
لینک مستقیم مقاله : https://isnac.ir/XEEA-HBADH

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