Faculty Publications

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Publications by NITK Faculty

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    Human Capital Approach for road accident costing in an Indian City
    (Institute for Transport Studies in the European Economic Integration, 2023) Sumayya Naznin, P.H.; Gidugu, S.; Cyril, A.; Ravi Shankar, A.U.
    The monetization of road crashes helps improve road safety awareness. This study focuses on the cost of Road Traffic Accidents (RTAs) in Ernakulam, a South Indian city, based on the Human Capital (HC) methodology, as it is most effective to estimate the cost of RTAs in developing nations. The loss is calculated from various data sources, including in-depth accident databases (police), questionnaire surveys, private hospital records, and vehicle garage bills considering the collision types. Most of the total costs are attributed to lost productivity, followed by medical expenses, vehicle damage, and human costs. Administrative costs comprise the smallest portion (0.73%) of the overall accident costs. The total cost estimation of RTAs in Ernakulam city for the years 2018 to 2021 is in the range of INR 66,96,04,438 to INR 103,05,12,440, which represents 0.44% to 0.7% of the city’s Gross Domestic Product (GDP), which is a non-repairable loss to the nation. © 2023 Institute for Transport Studies in the European Economic Integration. All rights reserved.
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    A hybrid machine learning approach for early cost estimation of pile foundations
    (Emerald Publishing, 2025) Deepa, G.; Niranjana, A.J.; Balu, A.S.
    Purpose: This study aims at proposing a hybrid model for early cost prediction of a construction project. Early cost prediction for a construction project is the basic approach to procure a project within a predefined budget. However, most of the projects routinely face the impact of cost overruns. Furthermore, conventional and manual cost computing techniques are hectic, time-consuming and error-prone. To deal with such challenges, soft computing techniques such as artificial neural networks (ANNs), fuzzy logic and genetic algorithms are applied in construction management. Each technique has its own constraints not only in terms of efficiency but also in terms of feasibility, practicability, reliability and environmental impacts. However, appropriate combination of the techniques improves the model owing to their inherent nature. Design/methodology/approach: This paper proposes a hybrid model by combining machine learning (ML) techniques with ANN to accurately predict the cost of pile foundations. The parameters contributing toward the cost of pile foundations were collected from five different projects in India. Out of 180 collected data entries, 176 entries were finally used after data cleaning. About 70% of the final data were used for building the model and the remaining 30% were used for validation. Findings: The proposed model is capable of predicting the pile foundation costs with an accuracy of 97.42%. Originality/value: Although various cost estimation techniques are available, appropriate use and combination of various ML techniques aid in improving the prediction accuracy. The proposed model will be a value addition to cost estimation of pile foundations. © 2023, Emerald Publishing Limited.