Abstract:
Fire resistance is one of the most critical performance requirements for structural concrete in modern construction, particularly in high-rise buildings, tunnels, industrial structures, and underground facilities. Ordinary Portland Cement (OPC) concrete undergoes progressive deterioration including strength loss, spalling, and microcracking when exposed to temperatures above 300°C. This study investigates the development of fire-resistant concrete (FRC) mixes using advanced supplementary cementitious materials (SCMs) including silica fume (SF) and fly ash (FA), polypropylene (PP) fibers, and refractory aggregates. Four concrete mix designs were systematically formulated and experimentally evaluated: a control mix (M1), an SF-blended mix (M2), and FA-blended mix (M3), and a hybrid SF–PP fiber mix (M4). Specimens were subjected to ISO 834 standard fire exposure at temperatures of 25°C, 200°C, 400°C, 600°C, and 800°C. Evaluation parameters included residual compressive strength, flexural strength, splitting tensile strength, thermal conductivity, mass loss, spalling resistance, water absorption, and chloride ion penetration. Results demonstrate that Mix M4 retained 27.2% of its ambient compressive strength at 800°C nearly twice the retention of the control achieved a fire resistance rating exceeding four hours, and exhibited markedly superior spalling resistance attributed to the vapor pressure relief mechanism of PP fibers. The findings provide actionable material design strategies for fire-resistant structural concrete meeting international standards.
Area: Dept. of Civil Engineering
Author: Dr. U J Jadhav, Dr. P R Modak, Mrs. M S Chiwande, Mr. C S Misal
DOI: MJAP/05/1002
Abstract:
For civil infrastructure managers worldwide, the structural integrity of steel and concrete bridges is a critical concern. Fatigue life and deterioration rates have become crucial in determining maintenance schedules, especially with monitors for aging bridge networks and hundreds of thousands of bridges across the United States being rapidly overloaded. Although traditional empirical methods and physics-based models are cornerstones in the degradation mechanism modeling, they struggle with effectively capturing the complex nonlinear nature of deterioration for real-world bridge structures. For the last twenty years, machine learning (ML) algorithms have provided revolutionary data-driven high-fidelity predictive capabilities that are complementary to and often surpass traditional approaches. This review paper is a meta-analysis of the studies available in existing literature on predicting fatigue life and deterioration of bridge infrastructure using ML approaches such as artificial neural networks (ANN), support vector machine(s) (SVM), random forests (RF), gradient boosting methods(GBM, XGBoost, Light GBM)), convolutional neural networks (CNN) and physics-informed neural networks(PINN). This review (n = 30 studies from 2005–2024) systematically evaluates methodology, data-set characteristics, predictive accuracy and practical deployment barriers. They demonstrate that, when used alone on their own, single-algorithm approaches are outperformed consistently by both ensemble learning and deep learning models achieving MAE reductions as high as 35% compared to regression baselines. Important shortcomings include limited bridge typology generalization, minimal real-world sensor deployment and no standard benchmark datasets. The paper ends with directions for future research towards hybrid physics-ML frameworks, federated learning for distributed monitoring networks, and explainable AI (XAI) to garner engineer trust in decision support.
Area: Department of Engineering
Author: TVS Ramanjaneyulu1, Dr. Ananda Babu Kurakula2
DOI: MJAP/05/0024