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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References
Siraj NB, Fayek AR (2019) Risk identification and common risks in construction: literature review and content analysis. J Constr Eng Manage
Gupta VK, Thakkar JJ (2018) A quantitative risk assessment methodology for construction project. Sadhana 43:116
Ling CX, Li C (1998) Data mining for direct marketing: problems and solutions. Am Assoc Artif Intell
Yaseen ZM, Ali ZH, Salih SQ, Al‐Ansari N (2020) Prediction of risk delay in construction projects using a hybrid artificial intelligence mode. Sustainability 12:1514
Asadi A, Alsubaey M, Makatsoris C (2015) A machine learning approach for predicting delays in construction logistics. Int J Adv Logistics 115–130
Keshk AM, Maarouf I, Annany Y (2018) Special studies in management of construction project risks, risk concept, plan building, risk quantitative and qualitative analysis, risk response strategies. Production and hosting by Elsevier B.V. on behalf of Faculty of Engineering, Alexandria University
Sanni-Anibire MO, Zin RM, Olatunji O (2020) Machine learning model for delay risk assessment in tall building projects. International journal of construction management. Informa UK Limited, trading as Taylor & Francis Group
El-Sayegh SM (2008) Risk assessment and allocation in the UAE construction industry. Int J Project Manage 431–438
Choi H-H, Cho H-N, Seo JW (2004) Risk assessment methodology for underground construction projects. J Constr Eng Manage 130:258–272
Tah JHM (2000) A proposal for construction project risk assessment using fuzzy logic. Constr Manag Econ 18:491–500
Padala SPS, Maheswari JU (2019) Axiomatic design framework for changeability in design of construction projects. Asian J Civil Eng 21(2):201–215
Subramanyan H, Sawant H, Batt V (2012) Construction project risk assessment: development of model based on investigation of opinion of construction project experts from India. J Constr Eng Manage 138:409–421
Padala SPS, Maheswari JU, Hirani H (2020) Identification and classification of change causes and effects in construction projects. Int J Constr Manage. https://doi.org/10.1080/15623599.2020.1827186
Ebrat M, Ghodsi R (2014) Construction project risk assessment by using adaptive-network-based fuzzy inference system: an empirical study. KSCE J Civil Eng
Gondia A, Siam A, El-Dakhakhni W, Nassar AH (2020) Machine learning algorithms for construction projects delay risk prediction. J Constr Eng Manage 146(1):04019085
Chattopadhyay DB, Putta J, Rao RM (2021) Risk identification, assessments, and prediction for mega construction projects: a risk prediction paradigm based on cross analytical-machine learning model. Buildings 11:172
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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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