BITS Faculty Publications
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Item Condition Assessment of Reinforced Concrete Bridge Deck Using Infrared Thermography(Springer, 2022-05) Singh, Ajit Pratap; Srivastava, AnshumanEstimation of extent of deterioration in concrete bridge decks is a challenge. Non-destructive testing (NDT) techniques are significant since they provide fast, easy, and economical way to detect delaminations, cracks, voids, and corrosion in bridge decks. In the present work, an attempt has been made to assess the effectiveness of infrared thermography (IRT) method in detecting various subsurface flaws of bridge decks. Thus, a test facility containing concrete bridge deck induced with various artificial defects is constructed. This study concludes that IRT can quickly scan large areas and identify potential locations of defects, particularly at shallow depths, up to 50 mm depth from surface in this study. Therefore, it can be highly useful for field inspections of bridges. The ideal time for field testing is estimated to be 4–5 h after sunrise. However, it can be suitably used along with other NDT techniques and their combination would yield better results.Item Attention-enabled Deep Neural Network for Enhancing UAV-Captured Pavement Imagery in Poor Visibility(IEEE, 2023) Singh, Ajit Pratap; Srinivas, Rallapalli; Narang, PratikIntegrating Unmanned Aerial Vehicle (UAV) technology with Artificial Intelligence AI and Computer Vision has revolutionized asset management, particularly pavement health monitoring. However, current AI-based methods often struggle in low-visibility scenarios, limiting their effectiveness. To address this, we present a novel end-to-end deep learning pipeline that detects image degradation using an efficient Attention mechanism and performs subsequent enhancement. This algorithm can be seamlessly integrated into drones or used for post-processing of pavement imagery. Its efficiency allows for scalability, making it a valuable tool for downstream road health monitoring tasks, such as cost estimation for road repairs. Our approach achieves mean accuracies of 93.34% with a mean inference time of 0.154 sec., demonstrating its efficacy.Item Fast and Lightweight UAV-based Road Image Enhancement Under Multiple Low-Visibility Conditions. 2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events(Springer, 2023-12) Singh, Ajit PratapThe toxicity, Leachate Pollution Index (LPI), and risk assessment of the leachate of hazardous sludge are very rarely and scantly studied. This study evaluates the leachate characteristics of the textile industry–central effluent treatment plant sludge. X-ray fluorescence (XRF) analysis determines the sludge’s chemical composition. The Toxicity Characteristic Leaching Procedure (TCLP) is a sample extraction method performed to simulate the leaching through landfills. The leachate samples are tested using inductively coupled plasma-optical emission spectrometry (ICP-OES) techniques for the metal ions. The 30 TCLP tests are performed as per the scheme generated by the Central Composite Design of Experiment (CCDoE). The study provides a novel and flexible framework for developing the Textile-Leachate Pollution Index (T-LPI) using a hybrid fuzzy analytical hierarchical process (FAHP). The metal ions’ weights in the leachate (Al, Cu, Cr, Fe, Mn, Ni, Pb, Zn, K, Mg, Ca) are obtained using FAHP infused with inter-valued triangular fuzzy numbers. The membership grade functions are derived for each metal ion, and the Leachate Pollution Index is estimated for 30 experiments. The experimental runs are ranked based on their LPI values. Pearson’s correlation coefficient indicates a poor association between the metal ions and their presence from different sources. The Human Health Risk Assessment (HHRA) of metal ions (Al, Cu, Cr, Fe, Pb, Zn, Mn, Ni) present in leachate shows the potential non-carcinogenic impact by Ni, Pb, Zn, Cu, Cr, and Mn. In contrast, Fe and Al have shown no adverse non-carcinogenic effect. The carcinogenic risk by Pb and Cr metal ions in leachate lies in the high- and very high-risk levels. The ranking of hazardous sludge sites can help in the immediate disposal of higher LPI value sludge to treatment storage disposal facilities (TSDF) as compared to the sludge with lower LPI. The study provides insight into the human health risk associated with the consumption (oral intake and skin absorption) of leachate-polluted surface water.Item Fecal sludge characterization, treatment, and resource recovery options: a state-of-the-art review on fecal sludge management(Springer, 2023-11) Singh, Ajit PratapA rise in population and urbanization demanded that a robust fecal sludge management (FSM) value chain be used to restructure the sanitation system throughout the world securely. A significant global need exists to adopt efficient and sustainable FSM. On-site sanitation systems (OSS) produce fecal sludge (FS). FS is produced when excreta and blackwater are combined and stored or treated, either alone or in combination with greywater. FS can be semisolid or slurry and raw or partially digested. Critical examination of FS characteristics, i.e., biochemical oxygen demand (BOD), chemical oxygen demand (COD), total solids (TS), and pathogen count, varies from 600–56,836 mg/l, 6656 to 201,200 mg/l, 830–123,000 mg/l, and 105 to 109 E. coli/l of FS respectively. Helminth eggs range from 2500–25,000/l of FS. Public health and the environment are negatively impacted by septic tank overflows and the careless discharge of FS into open spaces affecting groundwater quality, water bodies, irrigation fields, open drains, places outside villages, etc. Thus, deciding on a proper treatment technology for FS before discharging it into open land or reusing FS is essential to create a pollution-free environment. This paper highlights the practices adopted for FSM under its different processes, such as collecting, characterization, treating, and reusing of on-site FS and bibliometric analysis on documents on fecal sludge. A thorough analysis has been carried out by reviewing all important literature available globally.Item Multivariate statistical algorithms for landslide susceptibility assessment in Kailash Sacred landscape, Western Himalaya(Taylor & Francis, 2023-07) Singh, Ajit PratapLandslide susceptibility mapping plays an imperative role in mitigating hazards and determining the future direction of developmental activities in mountainous regions. Here, we used 518 landslide occurrences and nine landslide-conditioning parameters to build landslide vulnerability models in the Kailash Sacred Landscape (KSL), India. Four multivariate statistical models were applied, namely the generalized linear model (GLM), maximum entropy (MaxEnt), Mahalanobis D2 (MD), and support vector machine (SVM), to calibrate and compare four maps of landslide susceptibility. The results demonstrated the outperformance of Mahalanobis D2 for predictability compared to other models obtained from the area under the receiver operating characteristic curve (ROC). The ensemble model data shows that 10.5% of the landscape has susceptible conditions for future landslides, whereas 89.50% of the landscape falls under the safe zone. The occurrence of landslides in the KSL is linked to the middle elevations, vicinity to water bodies, and the motorable roads. Furthermore, the observed patterns and the resulting models exhibit the major variables that cause landslides and their respective significance. The current modelling approach could provide baseline data at the regional scale to improve the developmental planning in the KSL.Item Flood risk assessment in the Karamana river basin, Kerala, using HEC-RAS(Springer, 2023-07) Singh, Ajit PratapThe State of Kerala has frequently been facing a series of flooding phenomena that have adversely affected its multiple sectoral growths. The floods of 2018 have happened to be one of the most devastating floods that have occurred in the State of Kerala. It was seen that nearly thirteen out of fourteen districts in Kerala were tremendously affected during the 2018 August floods. The worst affected districts during the 2018 floods were Trivandrum, Pathanamthitta, Idukki, Thrissur, Ernakulam, and Kottayam. A sub-region near the Karamana basin located in the Trivandrum district is considered for the present study. The Karamana sub-region is a highly urbanized area that is also more or less prone to intense riverine flooding. The major rivers—Karamana and Killi—along with their respective tributaries, are the water bodies in the study region. Extensive urbanization, along with the overflowing of rivers during monsoon seasons, has paved the way for intense flooding in the region. This, in turn, necessitates developing a flood model for the sub-region. The development of an efficient flood model will aid in understanding the future challenges related to a flooding event in a region. In this study, the flood return probability water levels for the 5-year, 10-year, 25-year, 50-year, 100-year, 250-year, and 500-year were estimated for the Karamana sub-region. Besides, the flood risk zoning for the study area was conducted and elaborated as very high risk, high risk, moderate risk, and low risk for the different areas of the sub-region. Overall, the study can be helpful in identifying the most vulnerable areas to flooding in the Karamana region. By the proper identification of vulnerable areas in the region, proper planning and early warning measures can be devised and carried out by policymakers.Item Removal of COD and color from textile industrial wastewater using wheat straw activated carbon: an application of response surface and artificial neural network modeling(Springer, 2023) Singh, Ajit PratapA novel approach has been undertaken wherein chemically modified wheat straw activated carbon (WSAC) as adsorbent is developed, characterized, and examined for the removal of COD and color from the cotton dyeing industry effluent. Thirty experimental runs are designed for batch reactor study using the central composite method (CCM) for optimizing process parameters, namely biochar dose, time of contact, pH, and temperature, for examining the effect on COD and color-removing efficiency of WSAC. The experimental data have been modeled using the machine learning approaches such as polynomial quadratic regression and artificial neural networks (ANN). The determined optimum conditions are pH: 7.18, time of contact: 85.229 min, adsorbent dose: 2.045 g/l, and temperature: 40.885 °C, at which the COD and color removal efficiency is 90.92 and 94.48%, respectively. The nonlinear pseudo-second order (PSO) kinetic model shows good coefficient of determination (R2 ~ 1) values. The maximum adsorption capacity for COD and color by WSAC is at the pH of 7, the temperature of 40 °C, adsorbent dose of 2 g/l is obtained at the contact time of 80 min is 434.78 mg/g and 331.55 PCU/g, respectively. The COD removal and decolorization is more than 70% in the first 20 min of the experiment. The primary adsorption mechanism involves hydrogen bonding, electrostatic attraction, n-π interactions, and cation exchange. Finally, the adsorbent is environmentally benign and cost-effective, costing 16.66% less than commercially available carbon. The result of the study indicates that WSAC is a prominent solution for treating textile effluent. The study is beneficial in reducing the pollutants from textile effluents and increasing the reuse of treated effluent in the textile industries.Item Ecological Shifting and Land Use Land Cover Changes in Pindari Valley, Uttarakhand Himalaya, India(Springer, 2024) Singh, Ajit PratapThe current study assessed the spatial and temporal ecological dynamics shift of land use and land cover of Pindar valley in Uttarakhand Himalaya, India, for five decades from 1972 to 2018 (46 years). The area is classified using supervised and unsupervised classification, maximum likelihood, and K-means techniques. Seven major LULC categories were identified: agriculture, forest, grassland, scrubland, settlement, snow, and water. Results indicate that since 1972, settlement, agriculture, grassland, scrubland, and water body have increased by 0.1% (0.07 km2), 0.31% (0.23 km2), 4.55% (17.84 km2), 0.8% (2.41 km2), and 1.47% (5.73 km2) respectively, while forest and snow cover has decreased by − 6.32% (− 24.74 km2) and − 2.79% (− 2.26 km2), respectively. In addition, water bodies increased due to the rapid melting of glaciers. It is a maiden attempt to study the upper Himalayas, a part of the Pindar Valley region, for change detection of an ecological shift in land use and land cover.Item A comprehensive study on the physicochemical characteristics of faecal sludge from Septic Tanks and Single Pit Latrines facilities in a typical semi-urban Indian town: A Case Study of Rajasthan, India(RSC, 2024-09) Singh, Ajit PratapFaecal sludge (FS) generated from the Onsite sanitation (OSS) system has become a significant pollutant that negatively impacts the environment. Environmental contamination results from the disposal of untreated FS. In semi-urban areas where numerous toilets are linked to OSS systems, such as septic tanks and single pits, faecal sludge management (FSM) becomes crucial to ensure a safe sanitation service chain. Integral to the faecal sludge management framework, treating the FS is imperative, ensuring safe disposal and resource recovery. FS characterization plays a significant role in designing FS treatment plants. This case study characterized FS samples of OSS collected from Pilani, Rajasthan, India. The pH, electrical conductivity, total solids, chemical oxygen demand, faecal coliform, total nitrogen, total phosphorus, and capillary suction time varied from 4.64 to 7.93, 20.6 to 27.5℃, 1.857 to 6.315 mS/cm, 3430 to 95393.33 mg/l, 4406 to 160000 mg/l, 103 to 109 CFU/ml, 81.7 to 709.2 mg/l, 285 to 4471 mg/l, 149 to 1256.8 seconds respectively. The significant factors influencing the key FS characteristics parameter COD are found to be FS age (p<0.001), type of OSS (p=0.044), and for total solids, the factors affecting is identified as FS age (p<0.001), type of OSS (p=0.002) and greywater dilution (p=0.011). This case study can assist FSM stakeholders in designing FS treatment plants in Indian semi-urban towns and other developing nations with infrastructure, geographical and demographic factors, sanitation types, and FSM models similar to those in Pilani.Item Spatiotemporal snowline status and climate variability impact assessment: a case study of Pindari River Basin, Kumaun Himalaya, India(Springer, 2024-05) Singh, Ajit PratapThe snowline exhibits significant seasonal shifts upward and downward, reflecting the ever-changing dynamics of the seasons and being influenced by climate variations, which can vary annually. These fluctuations profoundly impact the cryosphere, biota, and ecosystem processes in high mountain regions. Despite the critical role of snowline variations, comprehensive information on how actual climate variability affects snow cover trends in the central mountain range of the western Himalayas is scarce. In the 'Pindari' region of the Uttarakhand district, India, which is part of the Himalayas, these challenges are exacerbated by the unchecked growth of anthropogenic activities and the broader impacts of climate change. This study analyses snowline variations in the Pindari glacial region from 1972 to 2018. The findings revealed that the snowline elevation significantly shifted upward between 1972 and 2018. Notably, this research revealed a decrease in snow-covered areas of approximately 5.01 km2 over the course of 46 years. This decrease is attributed to a direct response to the increasing number of high-temperature events that occurred during this extended period. This study emphasizes the urgent need for conservation measures in the study region and similar high mountains to combat global warming and safeguard the snowline, which serves as a visible proxy indicator to safeguard high-altitude Himalayan glaciers.