Probabilistic
Assessment of Coarse Aggregate Reliability for Infrastructure in Northeast India
using Bayesian Networks and Variable Elimination Method
Al-Rashid Mohammed Harun and Hazarika Palash Jyoti
Disaster Advances; Vol. 18(4); 19-29;
doi: https://doi.org/10.25303/184da019029; (2025)
Abstract
Coarse materials are critical in construction, playing a pivotal role in the durability
and structural integrity of concrete and other infrastructure components, particularly
in disaster-prone regions. This research investigates the resilience and reliability
of coarse materials sourced from various quarries in Assam, Northeast India, which
is essential for disaster prevention and infrastructure resilience. By employing
Bayesian Networks and the Variable Elimination technique, a comprehensive probabilistic
framework was developed based on nine essential laboratory tests including water
absorption capacity, specific density, flatness index, elongation index, stability,
material crushing strength, material impact strength, alkali-silica reaction and
Los Angeles abrasion strength. Specimens were collected from 18 sites and the assessment
results were analyzed to determine the probabilistic conditions of each node in
the Bayesian network.
The findings revealed significant variations in material quality across different
locations, with some areas exhibiting reduced reliability and increased vulnerability
to structural failures. These insights are crucial for implementing targeted interventions
such as enhancing quality control, sourcing higher-grade materials and optimizing
construction techniques to improve disaster resilience. Additionally, regular monitoring
and maintenance can mitigate potential infrastructure failures, thereby strengthening
disaster preparedness. This study provides valuable insights to support the Assam
Road Research and Training Institute (ARRTI) and similar organizations in improving
construction practices and ensuring the long-term resilience of infrastructure projects
in the region.
