Browsing by Author "Rallapalli, Srinivas"

Now showing 1 - 3 of 3

Compounded fuzzy entropy-based derivation of uncertain critical factors causing corrosion in buried concrete sewer pipeline
(Springer Nature, 2025-05) Rallapalli, Srinivas; Singhal, Anupam
Corrosion in buried concrete sewer pipelines remains a critical challenge for infrastructure sustainability, driven by the complex interplay of environmental, material, operational, pipe-related, and physical factors with inherent uncertainty and interdependency, aspects often overlooked previously. This study introduces a novel compounded fuzzy entropy-based approach to systematically prioritize critical corrosion-inducing factors, integrating environmental (H₂S, pH, humidity, temperature, O₂), material (cement content, alkalinity, w/c ratio, porosity, permeability), pipe-related (age, length, diameter, depth, slope), operational (flow velocity, water pressure, hydraulic energy loss, sewage residence time, sewer type), and physical (soil type, corrosivity, moisture, groundwater level, external load) factors. Results identify H₂S (0.2073), pH (0.2055), humidity (0.2031), pipe age (0.2039), length (0.2019), cement content (0.2026), alkalinity (0.2015), water pressure (0.2073), flow velocity (0.2043), soil type (0.2042), and soil corrosivity (0.2025) as the most influential contributors, enabling targeted corrosion mitigation strategies and enhancing infrastructure resilience.
Hydro-chemical profiling and contaminant source identification in agricultural canals using data driven clustering approaches
(Springer Nature, 2025) Singhal, Anupam; Rallapalli, Srinivas
Canal networks are vital for irrigated agriculture in semi-arid regions, yet their water quality is increasingly endangered by diffuse agro-chemical runoff and unregulated effluent discharges. Despite this growing risk, long-term, high-resolution assessments that simultaneously capture spatial patterns and seasonal dynamics remain scarce—leaving practitioners with limited evidence for targeted interventions. Addressing this gap, the study sampled ten canal sites monthly for 11 months across Charkhi Dadri District (Haryana, India) and analysed sixteen physicochemical parameters, including heavy metals and irrigation-relevant ions. A suite of multivariate techniques—R- and Q-mode hierarchical clustering, principal-component analysis (PCA), correlation matrices and one-way ANOVA—was employed to disentangle pollution drivers, while the Irrigation Water Quality Index (IWQI) translated complex chemistry into management-ready scores. Two principal components explained 72.6% of variance, with aluminium, iron and copper emerging as dominant contributors; ANOVA revealed significant seasonal shifts (p < 0.05) in these metals. Cluster analysis pinpointed contamination hotspots, and IWQI values of 67.3–85.5 classified canal water as “good” to “very good” for irrigation. By integrating granular spatiotemporal monitoring with advanced multivariate statistics, the study delivers a scalable framework for managing irrigation canals in data-limited, semi-arid landscapes.
Hydro-conditioning: Advanced approaches for cost-effective water quality management in agricultural watersheds
(Elsevier, 2022-05-22) Rallapalli, Srinivas; Singh, Ajit Pratap; ; ; Goonetilleke, Ashantha
Accurate simulation of landscape hydrological connectivity is pivotal for planning practices required for treating agricultural farm pollution. This study assesses the role of an advanced geospatial approach, namely, ‘hydro-conditioning’ employed for modifying Digital Elevation Models, termed hDEMs to replicate landscape hydrology by simulating continuous downslope flow through drainage structures such as bridges and culverts. The capabilities of manual and automated hDEMs in delineating optimal locations and water treatment potential of Best Management Practices (BMPs) in a typical agricultural watershed were evaluated. Parallel processing of both hDEMs revealed that ‘ground truthing’ plays a critical role in the accurate placement of breach lines for allowing water movement through digitally elevated surfaces. Outcomes guide the practitioners in selecting appropriate hDEM (manual or automated) depending on the complexity of modeled hydrological pathways, which is essential for planning BMPs in a cost-effective manner at different spatial scales. Modeling results show that hDEMs greatly influence hydrological connectivity, catchment boundaries, BMP locations, treatment capacities, and related costs. The accuracy of hDEMs was verified using a robust sub-basin scale validation approach. The study recommends a hybrid approach for utilizing the strengths of both, automated and manual hDEMs for efficient agricultural farm pollution in an economical manner.