Department of Civil Engineering

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ABSTRACTS: International Conference on Sports Engineering,
(Excel Publisher, 2017-10) Gupta, Rajiv
An Algorithm of Road Enhancement in SAR Images using Wavelet Transform
(Springer, 2007) Gupta, Rajiv; Gupta, Karunesh K
Annual Thermal Performance of a Hollow Roof in Combination with a Cavity Wall and Static Sunshade: Experimental Study of Energy-Efficient Rooms
(ASCE, 2013-12) Gupta, Rajiv
Energy consumption by the building sector constitutes a large portion of global energy use. Energy-efficient building technologies help to reduce heat gain in hot summer months, heat loss in cold winter months, and modify energy requirements in buildings. In the writers’ paper, the effect of a proposed hollow roof (independent of and combined with a designed brick-cavity wall with brick projections and static sunshade) on indoor air temperature has been analyzed experimentally by constructing four rooms of habitable dimensions (3.0×4.0×3.0-m high) and studying average hourly temperatures for 1 year. Each room has a different combination of type of roof, wall, and static sunshade; hence, the difference in indoor air temperature of the rooms will primarily be attributable to differences in heat transferred through these building elements. The proposed hollow roof combined with the designed brick-cavity wall with brick projections and static sunshade lessened indoor air temperature in summer and increased indoor air temperature in winter mornings and nights. The writers’ structures are thus useful for energy conservation per seasonal needs
Application of BIM framework on rural infrastructure
(Springer, 2022-02) Gupta, Rajiv
Rural infrastructure (RI) projects face multiple obstacles, such as unavailability of materials and machinery, poor estimation, failure in documentation, cost overrun during their operations. The current study identifies a method to surpass the encountered challenges. Building information modelling (BIM), a construction management process, has been adopted in various fields for acquiring efficient construction deliverables in a project lifecycle. Previous researchers have focused on enhancing and applying the BIM framework for infrastructure projects in urban areas such as airports, bridges, highways, and tunnels. However, the application of BIM in a rural scenario is minimal, and its execution for overcoming the hurdles faced in RI projects remains a gap. The current research work modifies BIM in a rural viewpoint, as rural BIM (R-BIM) framework, by incorporating essential attributes based on previously performed rural studies to get over the uncertainties. The R-BIM framework explores various dimensions emphasising a rural water management (RWM) project and highlights the operation of the BIM cloud with the engagement of concerned stakeholders.
Application of Recycled Coarse Aggregates and E-Waste for Pavements with Low Traffic
(IOSR, 2015-04) Gupta, Rajiv
Huge quantities of construction wastes, demolition, and electronic wastes are being generated these days in many of the countries and the disposal of them has become a serious problem. This study is an integrated experiment in which different combinations of e-wastes and recycled coarse aggregate together are used as a substitute of conventional aggregate. Recycled aggregates from site-tested concrete specimens were collected and are integrated with the e-waste by altering the proportions of these wastes. The compressive strength of M20 mix designed is assessed by casting cubes and the flexural strength by prisms. This study is carried out to ensure the usage of integrated- waste and the recycled coarse aggregate as a replacement of coarse aggregate. Experimental study is carried out to find if the e-waste strips can be used as the reinforcement instead of steel. Results are checked against the standards of IRC to use for the sub-grade of the pavement
ARIMA and NAR based prediction model for time series analysis of COVID-19 cases in India
(Elsiever, 2020-09) Gupta, Rajiv
In this paper, we have applied the univariate time series model to predict the number of COVID-19 infected cases that can be expected in upcoming days in India. We adopted an Auto-Regressive Integrated Moving Average (ARIMA) model on the data collected from 31st January 2020 to 25th March 2020 and verified it using the data collected from 26th March 2020 to 04th April 2020. A nonlinear autoregressive (NAR) neural network was developed to compare the accuracy of predicted models. The model has been used for daily prediction of COVID-19 cases for next 50 days without any additional intervention. Statistics from various sources, including the Ministry of Health and Family Welfare (MoHFW) and http://covid19india.org/ are used for the study. The results showed an increasing trend in the actual and forecasted numbers of COVID-19 cases with approximately 1500 cases per day, based on available data as on 04th April 2020. The appropriate ARIMA (1,1,0) model was selected based on the Bayesian Information Criteria (BIC) values and the overall highest R2 values of 0.95. The NAR model architecture constitutes ten neurons, which was optimized using the Levenberg-Marquardt optimization training algorithm (LM) with the overall highest R2 values of 0.97.
Assessment of Groundwater Quality Using GIS and Various Water Quality Indices: A Case Study of the Shekhawati Region of Rajasthan, Northwest India
(EJSR, 2016-09) Gupta, Rajiv
Assessment of Groundwater quality using Water Quality Index (WQI) and Geographic Information System (GIS) was carried out in the Shekhawati region of Rajasthan. The results of 15 physico-chemical parameters were used for the calculation of WQI. The results indicated that WQI values range from 0 to 1304 and 0-11,701 for two different approaches used and thus indicates very poor groundwater quality status in the region. The Fuzzy method as a third approach was also used to generate a WQI and resulted in only 2 values. The geographical information system using the Inverse Distance Weighted method (IDW) delineated groundwater quality zones into good to very poor potential areas. The hierarchal cluster analysis identified anthropogenic contamination, natural mineralization, reverse cation exchange as the major processes controlling groundwater chemistry. From the correlation matrix, it could be said that Turbidity, Total Hardness as CaCO3, Ca hardness as CaCO3, Mg hardness as CaCO3, Chlorides as Cl-, Fluorides as F- and TDS are responsible for high WQI values in the region.
Assessment of sustainability index for rural water management using ANN
(IWA, 2022) Gupta, Rajiv; Kumar, Gaurav
The current study proposes a sustainability index (SI) measure based on artificial neural networks (ANN) and globally accepted parameters. Some of the available methods for SI measurement are multi-criteria analysis, external costs, energy analysis, and ecological footprint methods. However, validity remains a concern due to a system's needs, criteria, and requirements. Generally, sustainability is assessed in economic, environmental, and social issues, which varies across regions and countries. Most of the studies accept sub-indices but to a limited extent. Therefore, the proposed study develops an SI evaluation method based on the idea of multi-sustainability incorporating operations, institutions, risks, and climate factors besides economic, environmental, and social issues. All these issues might not be applicable to a single project but may help to develop a complete index when applied. The present study considered different scenarios in building a method to calculate SI using ANN. The results obtained by the ANN model for various input parameters helped to identify the best water conservation strategy. Sensitivity analysis was also performed to determine the uncertainty contribution/significance of the input variables for the water scarcity in the study region. The developed model in the study is tested on a rural water management system.
Automated Bacteria Colony Counting on Agar Plates Using Machine Learning
(ASCE, 2021-10) Gupta, Rajiv
The identification of E. coli bacteria is critical for the prevention of health risks. According to EPA-approved gold standard methods, 24–48 h are required to count viable cells in water. Manual counting of viable bacteria colonies on agar plates is time-consuming and can be prone to human error. The method requires experts to identify and count colonies on agar plates using a microscope. Hence, the bacterial counting procedure must be automated in order to decrease error. The main objective of this study was to develop an automatic system for bacteria colony counting. A total of 1,301 groundwater samples were collected from eight districts in Rajasthan, India, for a field investigation. The results were validated using artificial intelligence (AI) methods on this experimental data set. We automated the process of E. coli bacteria identification using a convolutional neural network (CNN). We developed a smartphone application for the rapid detection of E. coli bacteria on agar plates using CNN. We also automated the process of bacteria colony counting using faster region-based convolutional neural network (R-CNN) to overcome manual cell counting process limitations. A graphical user interface (GUI) application was created to rapidly count bacteria colony–forming units on agar plates using faster R-CNN. The developed faster R-CNN model achieved an overall accuracy of 97% and an error (loss) of 0.10. The performance of the CNN and faster R-CNN models was validated using F-score, precision, sensitivity, and accuracy statistical measures. The comparative analysis showed that the faster R-CNN model is reliable and effective in E. coli cell counting. The study developed a system for identifying and counting viable cells of E. coli bacteria in water that can be used to forecast hotspots of water contamination.
Biosorption of Fe (II) by Scenedesmus sp. in batch and continuous systems
(Springer, 2019-11) Gupta, Rajiv
The removal of toxic or economically important heavy metal ions from wastewaters is of great importance from an environmental and industrial viewpoint. The biosorption of heavy metal ions by algae is a promising property with a potential for industrial use. The focus of this chapter is on investigating the biosorption of single- and multi-metal ions to free and immobilized micro algae in batch and continuous systems. There will be a discussion of heavy metal pollution, the usage and advantages of free and immobilized algal cells for heavy metal removal, biosorption mechanisms and developing isotherms in single- and multi-metal systems. Mathematical description of biosorption of a single metal ion to algae in a batch and in a continuous fixed bed column will also be presented and discussed.
Classification and Prediction of Developed Water Quality Indexes Using Soft Computing Tools
(Springer, 2023-04) Gupta, Rajiv
The water quality index (WQI) is a coherent method of expressing the state of the water quality, which has become complicated due to subjectivity and ambiguity in the data. The proposed study aims to classify and predict the water quality for potable water by applying soft computing techniques, namely fuzzy, adaptive-network-based fuzzy inference system (ANFIS) and artificial neural network (ANN), along with a novel weight-integrated health hazard index (HI). Initially, the water classification was performed through the fuzzy and HI indexes and was subsequently used for predictive modelling through the ANFIS and ANN on the 349 water samples for total dissolved solids (TDS), chloride (Cl−), hardness (as CaCO3), fluoride (F−), nitrates (NO3−), iron (Fe), and copper (Cu) parameters collected from the municipality region of Jaipur, India. The trained ANFIS model was proven satisfactory for fuzzy and HI indexes with the coefficient of determination (R2) value of 0.8413 and 0.996, respectively. In contrast, the ANN model failed to provide an adequate result for the fuzzy index with an R2 value of 0.5243 and a satisfactory result for HI with an R2 value of 0.839. The study’s novelty lies in predicting the water quality index using ANFIS and ANN for the developed unique HI and fuzzy-based indexes. The results of this study prove that ANFIS is a trustworthy and reliable approach for predicting WQI for potable water, which serves as a valuable guide for decision-makers in the field of water resource management.
Climate Change and Human Health: Denial to Positive Action
(ASTRAL, 2021) Gupta, Rajiv
Environmental cleanness is essential for the livelihood of people and nation, especially in the developing world; therefor, any impact on it will have significant economic, social and political ramifications. Scholars from around the world and from various fields have been brought together to explore this important topic. This book provides information about the different aspects of environmental pollution and the techniques to control their level. This book reveals the research work being carried out by the researchers from India and abroad in the field of pollution and its abatement. It will definitely help to the young researchers, scientists and to the scientific community as it contains most of the critical issues currently being raised by the researchers from different areas and is essential for conserving our precious environment and to use nature sustainably.
Comparative assessment of LSTM approaches for enhanced prediction of rainfall climatology with minimum uncertainty
(Inder Science, 2025) Gupta, Rajiv
Forecasting precipitation is highly challenging for scientific modellers due to the complexity and uncertainty of atmospheric data and weather prediction models. To investigate the hydrological alternations such as rising sea levels, increasing floods and evaporation, and changes in snowpack caused by climate change, it is essential to accurately predict precipitation, a function of several interrelated climatic variables. This study presents a unique approach to predicting precipitation with minimum uncertainty by performing a comparative assessment of long-short-term memory (LSTM) approaches. The LSTM prediction models were run using quarterly, semi-annual, annual, and biannual precipitation data and other data such as temperature, vapour pressure, cloud cover, rainy days, and potential evaporation. Bivariate models using potential evaporation and temperature produced equivalent results to the multivariate model as the mean absolute error (MAE) was found to be 23.89% and 26.35%, respectively, compared to the univariate model (MAE 76.29%).
Comparative thermal performance of static sunshade and brick cavity wall for energy efficient building envelope in composite climate
(Doiserbia, 2014) Gupta, Rajiv; Charde, Meghana
Energy efficient building technologies can reduce energy consumption in buildings. In present paper effect of designed static sunshade, brick cavity wall with brick projections and their combined effect on indoor air temperature has been analyzed by constructing three test rooms each of habitable dimensions (3.0 m × 4.0 m × 3.0 m) and studying hourly temperatures on typical days for one month in summer and winter each. The three rooms have also been simulated using a software and the results have been compared with the experimental results. Designed static sunshade increased indoor air temperature in winter while proposed brick cavity wall with brick projections lowered it in summer. Combined effect of building elements lowered indoor air temperature in summer and increased it in winter as compared to outdoor air temperature. It is thus useful for energy conservation in buildings in composite climate.
Compressive Strength Characteristics of Normal Strength Concrete Cured Using Colored Polythene Sheets
(Scientific Net, 2013-04) Gupta, Rajiv
Concrete performance is severely influenced when placed in extreme environmental conditions and hence certain measures are required to control it. The present paper proposes covering the structural members with colored polythene sheets which serves dual purpose of curing. Polythene sheets do not allow appreciable loss of water by evaporation and thus they do not delay or prevent hydration besides providing optimum temperature and humidity conditions for strength development and being water conservative. In present study concrete cubes were wrapped using five different colored polythene sheets for a period of 7-days. The study incorporates prevailing environmental conditions by relating effect of curing, including conventional methods like sprinkling and ponding on compressive strength of concrete at varied atmospheric temperatures, relative humidity and influence of available solar radiation. Significant compressive strength characteristics like initial tangent modulus and strain at peak stress are also determined for cubes at a period of 7-, 28-, and 180-days. The quality assessment of concrete cubes cast was carried out using ultrasonic pulse velocity test results. The study aims at developing a knowledge base to design an extensive decision making algorithm that identifies appropriate curing method in prevailing environmental conditions.
Computational techniques in use of linear graph theory in truss analysis and influence line construction
(Institution of Engineers, Calcutta, 2002) Gupta, Rajiv
Concrete compressive strength prediction using ultrasonic pulse velocity through artificial neural networks
(Elsiever, 2006-05) Gupta, Rajiv
Numerous attempts to use ultrasonic pulse velocity (UPV) as a measure of compressive strength of concrete has been made due to obvious advantages of non-destructive testing methods. The present study is conducted for prediction of compressive strength of concrete based on weight and UPV for two different concrete mixtures (namely M20 and M30) involving specimens of two different sizes and shapes as a result of need for rapid test method for predicting long-term compressive strength of concrete. The prediction is done using multiple regression analysis and artificial neural networks. A comparison between two methods depicts that artificial neural networks can be used to predict the compressive strength of concrete effectively. The results are plotted as experimentally evaluated compressive strength versus predicted strength through both methods of analysis.
Construction planning and technology
(CBS Publishers, 2013) Gupta, Rajiv
The book js a supplementary to the course in which all the building materials components of a building (doors windows finish staircase etc.) are taught. The author feels that these components can be understood if one goes through them. This is the reason for not including these topics in this book. These topics can be found in any book dealing with construction and building materials. In the text Costing and Estimation is covered in chapter one which is the first phase of planning to execute a project. After estimation CPM or PERT networks can be drawn with proper duration of the activities involved. Once network is prepared resource levelling can be done.
Construction Planning and Technology
(CBS, 2014) Gupta, Rajiv
A convolutional neural network approach for detection of E. coli bacteria in water
(Elsiever, 2021-06-24) Gupta, Rajiv
The detection of Escherichia coli bacteria is essential to prevent health diseases. According to the laboratory-based methods, 12–48 h is required to detect bacteria in water. The drawback of depending on laboratory-based methods for the detection of E. coli bacteria can be prone to human errors. Hence, the bacterial detection process must be automated to reduce error. We implement an automated E. coli bacteria detection process using convolutional neural network (CNN) to address this issue. We have also proposed a mobile application for the rapid detection of E. coli bacteria in water that uses CNN. The developed CNN model achieved an accuracy of 96% and an error (loss) of 0.10, predicting each sample in only 458ms. The performance of the model was validated using the F-score, precision, sensitivity, and accuracy statistical measures, which shows that the model is reliable and effective in detecting E. coli. The study generates a methodology for predicting E. coli bacteria in water, which can be used to predict hotspots in terms of continuous exposure to water contamination.