Big Data Analytics for Predictive Modeling in Civil Engineering and Infrastructure Development

DOI: https://doi.org/10.63345/ijrmeet.org.v10.i5.1

Er Apoorva Jain

Chandigarh University

Mohali, Punjab, India

apoorvajain2308@gmail.com

Abstract
Big data analytics has emerged as a transformative approach in civil engineering and infrastructure development, enabling practitioners to leverage large-scale datasets for predictive modeling of structural performance, traffic flow, and resource utilization. This manuscript explores the application of big data frameworks—such as Hadoop Distributed File System (HDFS), Apache Spark, and Apache Flink—to aggregate and preprocess heterogeneous engineering datasets (e.g., sensor readings, satellite imagery, traffic counts). We evaluate statistical and machine learning techniques—multiple linear regression, random forest regression, and gradient boosting—for forecasting structural health indicators and traffic congestion levels. A case study on bridge load capacity prediction demonstrates the efficacy of these models, achieving an R² of 0.87 and a root mean square error (RMSE) reduction of 15% compared to baseline approaches. The results highlight the potential of big data–driven predictive analytics to improve maintenance scheduling, optimize resource allocation, and enhance decision-making in civil infrastructure projects.

Keywords
Big Data Analytics; Predictive Modeling; Civil Engineering; Infrastructure Development; Regression Analysis

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