Citation :

Radhika S. Thakre, Uday P. Waghe, Yogesh P. Kherde, Rajesh M. Bhagat, Abhay G. Hirekhan, Pallavi B. Gadge, Amar Jain, "Design of an Integrated IoT-FuzzyAHP Framework for Real-Time Geotechnical Risk Management Using Entropy and Reinforcement Learning-Driven Decision Optimizations," International Journal of Civil Engineering, vol. 13, no. 4, pp. 396-412, 2026. Crossref, https://doi.org/10.14445/23488352/IJCE-V13I4P124

Abstract

For effective geotechnical risk management, risk detection must be timely, and priority capacity must prioritize unsafe environmental and terrain dynamics. In the realm of complex terrain processes, existing DSS recognize one specific slope failure using the input model or measured sensor data. Errors and limitations of capability severely affect accuracy. Designing the prospective DSS will be unresolved. This paper has sought to outline the Multi-Layer Decision Support Model (MCDSS) with the support of Fuzzy Analytical Hierarchy in compliance with the Internet of Things and real-time data streaming. The design is such that it allows me to have successive blocks to facilitate easy and somewhat block-wise data flow; that is, it gives the concept of an accurate feedback loop with iterative optimization. The first block looks inside Real-Time IoT Data Collection, where live feed from the wells for soil moisture, pore pressure, safety, and settlement is referred to. This does direct into the Dynamic Entropy-Driven Fuzzy-AHP (DE-FuzzyAHP) module so that the set of the fuzzy membrane function could be adaptively tuned with Shannon entropy, increasing sensitivity over data variability in the said process. The tuned set of weights is improved using the Outcome-Optimized Reinforcement Learning AHP Tuner (OORL-AHP), which will learn weights very quickly by applying Q-learning using field mitigation feedback. Awareness of the learning from the optimization of the retrieval weight and the outcome. These outcomes are embedded in the very Fuzzy-AHP Integrated Bayesian Belief Risk Network (FBBRN) from where the very updated causal risk dependencies and probabilistic forecasts are framed at real timestamp scenarios. During the heightened risk, a triggered alert from the Risk Mapping block-Spatially Adaptive Multi-Resolution Risk Mapping (SAMR-RM) for drone or sensor-assisted high-resolution hotspots will be mapped. Moreover, later, with Ns/Ni, the Decision Confidence Quantification Meta-Layer (DCQML) trains and builds the sensors’ reliability, data perturbations, and expert communication into a confidence score in the very process. This is a unified technique for improving the accuracy of risk prioritization (from ~78% to ~95%), setting tension on forecast lead delays, enhancing mapping that adapts, and putting forth quantified decision confidence sets. This represents a new wave of integration that makes entropy movements and machine learning work together seamlessly, with a significant impact on probabilistic risk forecasting and spatial intelligence for new geotechnical risk management.

Keywords

Geotechnical Risk, Fuzzy-AHP, IoT Sensors, Bayesian Belief Network, Reinforcement Learning.

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