Department of Civil Engineering

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Author: search.filters.author.Singh, Ajit Pratap

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    Decentralized faecal sludge management in rural India by integrating drying beds for dewatering and coagulation for leachate treatment: A sustainable, cost-effective, nature-based solution
    (Elsevier, 2025-11) Singh, Ajit Pratap
    Discharging untreated faecal sludge (FS) from on-site sanitation into the environment poses environmental and public health risks in semi-urban and rural areas of India. Therefore, designing a low-cost, resource-efficient, sustainable, and effective FS treatment system is crucial. This study investigated a hybrid treatment approach combining planted and unplanted drying beds for FS dewatering, followed by leachate treatment using natural coagulants, Moringa oleifera and chitosan. The planted and unplanted drying beds achieved removal efficiencies of 95.84 % and 94.47 % for total solids, 98.09 % and 97.71 % for chemical oxygen demand, 93.46 % and 91.17 % for biochemical oxygen demand, 95.09 % and 86.85 % for total phosphorus, 80.04 % and 71.71 % for total nitrogen, and 99 % and 97.71 % for E. coli, respectively. The macrophyte Nerium oleander, planted in the drying bed, exhibited healthy growth under FS loading conditions, with the plant height increasing from 0.75 m to 1.8 m, indicating its potential for phytoremediation and resilience. The natural coagulants M. oleifera and chitosan achieved 95.77 % and 96.29 % turbidity removal, and 81.67 % and 84.17 % COD removal, respectively. The dried FS exhibited C/N ratios of 8.56 (planted) and 7.16 (unplanted), alongside favourable organic matter and nutrient content, indicating its suitability for organic manure. The calorific value of the dried sludge was 14.76 MJ/ kg (planted) and 15.93 MJ/ kg (unplanted), suggesting potential for use as solid fuel. This treatment system can provide a sustainable FS management solution for rural areas of India, as it achieves overall treatment goals and enables resource recovery.
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    A comparative analysis of composite and grab sampling methods for fecal sludge characterization: a case study from Pilani, India
    (Springer, 2025-08) Singh, Ajit Pratap
    Fecal sludge (FS) is biohazardous waste from on-site sanitation (OSS) containers like septic tanks and pit latrines, potentially harming the environment if discharged untreated. The design of the FS treatment system depends on its characteristic properties. Earlier and already existing characterization studies have shown that FS age, OSS type, water inclusion, and usage of additives significantly impact FS characteristics. There are various sampling methods to collect and characterize the sample. However, no study has compared the sampling methods of FS, which may potentially impact characterization. This study compares composite and grab sampling methods by analyzing 15 samples of each collected from the same FS discharge during a vacuum truck emptying vehicle in Pilani, a town in Rajasthan, India. The characterization of FS samples from OSS revealed variations between the two sampling methods, even though the samples were obtained from the same FS discharge. In composite sampling, total solids (TS) varied from 14.9 to 90 g/l (mean: 42.3 g/l, median: 33.4 g/l), and chemical oxygen demand (COD) varied from 16 g/l to 122.7 g/l (mean: 54.7 g/l, median: 42.7 g/l). While in grab sampling, TS varied from 12.1 to 91.5 g/l (mean: 36.2 g/l, median: 25.6 g/l), and COD varied from 8.7 g/l to 114.7 g/l (mean: 43.9 g/l, median: 29.3 g/l). A paired Wilcoxon signed-rank test shows that sampling methods significantly affect the TS (p = 0.041) and COD (p = 0.018) of FS samples.
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    Spatiotemporal change analysis of urbanization in Gurugram district of Haryana, India, using a geospatial technique
    (Springer, 2024-07) Singh, Ajit Pratap
    Remote sensing and GIS play a very important role in monitoring, managing, and mapping the operation of natural resources. This study includes the Landsat 5 and Landsat 8 satellite imagery for the analysis of urban sprawl for the years 1999 and 2019, respectively, in the Gurugram (previously known as Gurgaon) district of Haryana, India. The methodology of the study follows by calculating the NDBI (normalized built-up index) spectral indices for extracting the built-up feature class from the Landsat time series data sets of 1999 and 2019. The aim of this study is to: (i). find out urbanization patterns in the area of interest and (ii). temporal change analysis, in particular, urban feature class from 1999–2019. The outcomes of the study illustrate a rapid urban sprawl in the region by the percent increase of 293%. In conclusion, the study findings suggest to secure a livable and resilient future for the residents of the region; policymakers and urban planners must embrace sustainable and integrated approaches to urban development that balance economic growth with environmental protection.
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    Random tree classifier for land use classification in hilly terrain using sentinel 2 imagery: a case study of Almora town, Uttarakhand, India
    (Springer, 2024-07) Singh, Ajit Pratap
    Mountain landscapes are extremely complex and heterogeneous. These zones are highly sensitive and dynamic in nature. Rapid uncontrolled urbanization and population growth lead to severe environmental degradation in a hilly region. Due to the change in surface altitude and steep slopes, image processing in these regions is challenging. This study includes the sentinel 2B satellite imagery for classifying the Almora town situated in Uttarakhand state of India. The classification was performed by applying the object-based image analysis technique (OBIA) with a machine learning classifier. The study includes two scenarios of different image parameter training of the classifier using sentinel 2B satellite imagery. The major objectives of this study were to (1) define the optimal segmentation level for better image feature extraction using a multi-resolution segmentation approach, (2) find out the optimal image feature (mean, standard deviation (sd), and geometry) for better classification of LULC in the mountain region, and (3) evaluate the performance of a random tree (RT) machine learning (ML) classifier in assessing the land use land cover (LULC) analysis in Almora town of Uttarakhand, India. The study aims at exploring the potential of the RT algorithm with different parameter settings using different sets of image features to improve the overall accuracy of the classification. The results suggest that selecting proper sets of image features is essential for achieving high classification accuracy. The most satisfactory results have been obtained from scenario I, with an overall accuracy of 86.92. Overall, the study shows that the OBIA technique with ML classifier was effective and capable of achieving a high LULC classification accuracy in a mountainous landscape.
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    Floating solar photovoltaic (FSPV) installations at varying heights: evaporation reduction estimation for major dams of the tropical region of Uttar Pradesh, India
    (IOP, 2025) Singh, Ajit Pratap; Jha, Shibani Khanra; Mittal, Ravi Kant
    Any tropical region is well known for its high levels of sunshine and is suitable for PV installations with the associated disadvantage of high evaporation rates. FSPV is an alternate approach for solar PV installations in such regions to harness maximum solar energy with the additional advantage of reducing evaporation from water bodies. The study on estimating the reduction in evaporation due to FSPV installations and associated panel height above the water surface is limited. This study aims to quantify the reduction in evaporation resulting from the deployment of floating solar photovoltaic (FSPV) systems above water surfaces. It also determines the panel height above water bodies to maximize evaporation reduction. These findings are then extended to evaluate the impact of FSPV installations on reducing evaporation over the major dams in the tropical region of Uttar Pradesh, India. The experimental results highlighted that the maximum evaporation reduction occurred from the water surface covered with a panel at a height of 300 mm above the water with an evaporation reduction of 23.44 %. The extrapolation of the study for 28 major dams of Uttar Pradesh, reveals an annual water saving of 92.56 million cubic meters (MCM) with FSPV coverage of 25%. Based on estimations, a 1 MWp FSPV installation considerable amount of water annually can fetch water for 67 individuals in a tropical region, assuming 100 lpcd. These research outcomes would provide valuable insights into FSPV technology and its potential to mitigate water evaporation, with implications for regional and national water and energy resource management policies.
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    Developing strategic and staging optimization pathways for urban flood damage mitigation
    (Elsevier, 2025-10) Srinivas, Rallapalli; Singh, Ajit Pratap; Goonetilleke, Ashantha
    Despite significant advancements in flood risk assessment and damage monetization, research is lacking for simultaneously examining the impacts of the complexity of factors such as rising sea levels, changing rainfall patterns, and urbanization, on flood damage assessment. This study adopts an innovative staging procedure that progressively and strategically optimizes flood damage mitigation measures while addressing the uncertainties associated with the implementation of flood mitigation measures over three different time horizons (2040, 2070, and 2100), with each subsequent stage refined based on the constraints and optimal results of the previous stage. Using the Non-dominated Sorting Genetic Algorithm (NSGA II), the study compares 27 optimized pathways for mitigating flood damages, balancing investment costs and Average Annual Damage (AAD) reduction. The results demonstrate that the proposed approach achieves an AAD reduction of up to 2.89% in 2040, 4.03% in 2070, and 2.12% in 2100 under the most comprehensive mitigation pathways while balancing the costs. The study highlights cost-effective alternatives, such as combining dredging and permeable asphalt, achieving a 1.31% AAD reduction in 2040 with no additional costs. Compared to static single-stage mitigation policies, the proposed staging approach offers greater flexibility and efficiency in addressing dynamic urbanization and climate change scenarios. These results underline the trade-offs between cost and effectiveness, equipping policymakers with a robust decision-making framework to tailor flood mitigation strategies for diverse global contexts. Overall, this study significantly advances the strategic planning of urban flood damage mitigation, enabling adaptation to evolving environmental and socio-economic challenges.
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    Condition Assessment of Reinforced Concrete Bridge Deck Using Infrared Thermography
    (Springer, 2022-05) Singh, Ajit Pratap; Srivastava, Anshuman
    Estimation 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.
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    Attention-enabled Deep Neural Network for Enhancing UAV-Captured Pavement Imagery in Poor Visibility
    (IEEE, 2023) Singh, Ajit Pratap; Srinivas, Rallapalli; Narang, Pratik
    Integrating 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.
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    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 Pratap
    The 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.
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    Fecal sludge characterization, treatment, and resource recovery options: a state-of-the-art review on fecal sludge management
    (Springer, 2023-11) Singh, Ajit Pratap
    A 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.