Abstract
Construction projects are one among various major businesses which need high investments like time, money, resources to meet project requirements. As a result, risk is involved in executing construction projects. Timely completion of project within allocated budget is the main goal of any project. However, due to various risks involved, most of the projects are delayed and result in cost overruns. Thus, prediction of risk impacts on time and cost before their occurrence is essential for successful management of projects. Therefore, the objective of the study is to develop a model to predict risks using artificial neural network (ANN) approach. To achieve this objective initially, through literature review, 60 risk factors are identified. A questionnaire survey was conducted with 100 respondents to determine the probability and impact values of all risk factors. Based on survey data, an ANN model was developed using MATLAB software to predict risks. The findings revealed that ANN-based prediction can be utilized effectively to predict risks at early stages of construction project.
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Anirudh, N., Padala, S.P.S., Reddy, H.N.E. (2023). Development of ANN-Based Risk Prediction Model in Construction Projects. In: Reddy, K.R., Kalia, S., Tangellapalli, S., Prakash, D. (eds) Recent Advances in Sustainable Environment . Lecture Notes in Civil Engineering, vol 285. Springer, Singapore. https://doi.org/10.1007/978-981-19-5077-3_9
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DOI: https://doi.org/10.1007/978-981-19-5077-3_9
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