Applying Ai Techniques To Analyze Soil Data And Improve Foundation Design In Construction In Iraq

Ameer Abdulridha AjmiAlali

doi:10.59581/jkts-widyakarya.v2i3.3769

Authors

Ameer Abdulridha AjmiAlali University of Thi-Qar

DOI:

https://doi.org/10.59581/jkts-widyakarya.v2i3.3769

Keywords:

Machine learning, neural network, svm, soil, Iraq, al Nasiriyah

Abstract

In geotechnical engineering, building robust structures is crucial to ensure the bearing capacity of structures against external forces, so making sure soil strength and unreliable build cost and duration prediction are also very important and preliminary aspects of any construction project. Therefore, in this first-of-its-kind modern examine, the capability of various artificially intelligent (AI)-based models toward reliable forecasting and estimation of preliminary construction expenses, duration, and strength at shear is explored. First, background information about the revolutionary artificial intelligence (AI) technique along with its many distinct models ideal for geotechnical and building engineering problems is presented, The use of AI-based models in the literature for the aforementioned construction and maintenance applications is discussed in a number of current works, together with their benefits, drawbacks, and future directions. Several important input elements that significantly affect the preliminary price of construction, construction time, and soil's shear strength estimation are listed and given through analysis. Finally, some obstacles to employing AI-based models for precise forecasts in these applications are discussed, along with elements influencing the problems with cost overruns. Thus, this work can help civil engineers make effective use of artificial intelligence (AI) to solve difficult and risky tasks. It can also be used to Internet of Things (IoT) environments for self-learning applications like smart architectural health-monitoring systems

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