• Yeruva Ramana Reddy

The main aim of this paper is to analyze how artificial intelligence methods may be utilized to lower geotechnical engineering. It is now conventional practice to include risk management into construction projects of all sizes; yet, most risk assessments are qualitative rather than quantitative. Additional formal quantitative approaches are required to ensure that project costs and delivery delays are accurate. Moreover, the benefit of AI is its capacity to analyze unstructured data concerning potentially dangerous practices or activities inside the company's operations. AI systems have the ability to recognize patterns of behavior connected to previous occurrences and then transfer such patterns as risk predictors [1]. Protecting the data acquired and utilized, as well as the implementation costs, should be front of mind when deploying artificial intelligence (AI) technology. If you're looking for a fresh way to look at risk, you may want to consider using artificial neural networks (ANNs). Predictions may be made based on data that has been de-defined. ANNs define the starting values for simulations, which are used to simulate risk. Because of this, combining both approaches is an effective risk analysis technique [1]. In most situations, risk analysis in the professional arena, particularly in the construction business, is conducted qualitatively only. In order to identify and assess risks, contractors often rely on a checklist of items. This crisis has shown that qualitative risk analysis is not sufficient for correctly evaluating risks.

Artificial intelligence, geotechnical engineering, artificial Neural Networks, risk assessment.

"Reducing The Risks In Geotechnical Engineering Using Artificial Intelligence Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 6, page no.k334-k337, June-2022, Available :http://www.jetir.org/papers/JETIR2206A46.pdf

"Reducing The Risks In Geotechnical Engineering Using Artificial Intelligence Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 6, page no. ppk334-k337, June-2022, Available at : http://www.jetir.org/papers/JETIR2206A46.pdf