Indra Prakash - Profile on Academia.edu (original) (raw)

Papers by Indra Prakash

Research paper thumbnail of Estimating the Compressive Strength of Self-compacting Concrete with fiber using an Extreme Gradient Boosting model

Self-compacting concrete reinforced with fiber (SCCRF) is extensively utilized in the constructio... more Self-compacting concrete reinforced with fiber (SCCRF) is extensively utilized in the construction and transportation industries due to its numerous advantages, such as ease of building in challenging sites, noise reduction, enhanced tensile strength, bending strength, and decreased structural cracking. Traditional methods for assessing the compressive strength of SCCRF are generally time-consuming and expensive, necessitating the development of a model to forecast compressive strength. This research aimed to predict the CS of SCCRF using the Extreme Gradient Boosting (XGB) machine learning technique. The research uses the grid search method to optimize the XGB model's hyperparameters. A database of 387 samples is collected in this work, which is also an enormous dataset compared to those utilized in previous studies. An excellent result (R 2 max = 0.97798 for the testing dataset) proves that the proposed XGB model has excellent predictive power. Finally, Shapley Additive exPlanations (SHAP) analysis is conducted to understand the effect of each input variable on the predicted CS of SCCRF. The results show that the samples' age and cement content are the most critical factors affecting the CS. As a result, the proposed XGB model is a valuable tool for helping materials engineers have the right orientation in the design of SCCRF components to achieve the required compressive strength.

Research paper thumbnail of Evaluation and Management of Waterlogging Problems in the Command Area of Kadana Dam, Kheda District Using GIS

International Journal for Scientific Research and Development, Jun 1, 2015

Water logging problem occurring in the command area of Mahi Right Bank Canal (MRBC) of Kadana dam... more Water logging problem occurring in the command area of Mahi Right Bank Canal (MRBC) of Kadana dam has been evaluated using remote sensing and Geographical Information Systems (GIS). Various thematic maps have been prepared such as topography, geohydrology, soil, land use, Digital Elevation Model (DEM) and synthesised with the ground water and surface water and collated with irrigation data in GIS environment Major part of MRBC command area is occupied by Alluvium of Sabarmati and Mahi River and most part of the area is flat. Appreciable rise in the sub-soil water level has been observed since 1980 that is after starting irrigation through canal network of MRBC. Now major part of the Kheda district is either affected by water logging and salinization or presently under critical condition requiring immediate remedial measures and suitable management plan. Three talukas of Kheda district namely Matar, Mahudha and Kapadvanj are almost water logged and remaining under critical stage except part of Balasinor and Mehmedabad where water table is still in safe zone. Flat topography (0 to 1° slope), inadequate surface and subsurface drainage, poor maintenance of existing drainage system, over irrigation and cultivation of more water intensive crops after the operation of canal network are some of major causes of water logging in the area.

Research paper thumbnail of Rock Mass Evaluation of the Sardar Sarovar (Narmada) Dam and Underground Powerhouse, India

Geotechnical evaluation of the 163 m high concrete gravity Sardar Sarovar (Narmada) dam, under co... more Geotechnical evaluation of the 163 m high concrete gravity Sardar Sarovar (Narmada) dam, under construction, and 1200 MW underground powerhouse (210m x 23m x 57.5m) and its ancillary structures has been done. The dam and powerhouse sites are occupied by basalt flows underlain by infra-trappean sedimentary rocks (Bagh beds) intruded by basic dykes. The area is structurally complex and seismically active. Intra-formational shears and sub-horizontal to low dipping weak layers like red bole, tuff, argillaceous sandstone having low values of shear parameters posed the problem of sliding stability of dam blocks. Concrete shear keys were provided as one of the remedial measures. Differential settlement was apprehended in the foundation of dam having varying physicoengineering properties and rock mass characteristics. Reinforced concrete mats were provided to treat the weathered and sheared rock mass and 34.5m deep reinforced concrete plug to prevent differential settlement of dam blocks located on river channel (dam base) fault. The horizontal seismic coefficient adopted for the dam is 0.125g. The construction of 1200 MW underground powerhouse located in basalt is nearing completion. During progressive excavation of the machine hall (cavern) cracks were observed in the 57.5m high shotcreted walls. Additional longer rock bolts/ cables/ tendons were provided as remedial measures. Draft tube and exit tunnels are passing through dolerite rocks dissected by chlorite-coated joints and slaked rock zones. Rib supports were introduced after observing behaviour of the rock mass and collapses in part of these tunnels.

Research paper thumbnail of Application of Observational Method in the Successful Construction of Underground Structures, Sardar Sarovar (Narmada) Project, Gujarat, India

The Sardar Sarovar (Narmada) underground powerhouse is located in Deccan Basalt flows in the lowe... more The Sardar Sarovar (Narmada) underground powerhouse is located in Deccan Basalt flows in the lower Narmada valley in Gujarat state. These flows are intruded by dolerite dykes and sill. Basalt flows and dolerite rocks are considered good tunneling media for locating underground structures. Therefore, initial designing of supports was done considering good rock mass conditions. However, during construction numerous geotechnical problems were encountered necessitating review of support system. Rock falls and collapses were observed in tunnel sections passing through dolerite dykes and sills. Cracks were observed in the walls of the powerhouse cavern. During progressive excavations back analysis was done to know the causes of distress in rock mass and structures The 'Observational Method' adopted during construction resulted in the safe execution of structures by timely modification and installation of adequate support system.

Research paper thumbnail of Geotechnical Problems and Treatments of Weathered Rock Seams Occurring in the Foundation of Karjan Dam, Western India 2.07

The Karjan dam is located on the Deccan Basalt flows of Cretaceous -Eocene age in the N~illnada v... more The Karjan dam is located on the Deccan Basalt flows of Cretaceous -Eocene age in the N~illnada va.lley in Gujarat state. A characteristic feature of lbc basalt flows in this area is conspicuous presence of a number of suh•lwrizont.al weathered rock seams posing the foundation problems of settlement, sliding and seepage. Concrete shear keys have been provided to increase the sliding stability of dam blocks besides other remedial measures. Additional curtain grouting has been done after completion of the dam to redm.::c seepage.

Research paper thumbnail of Geotechnical Assessment and Evaluation of the Impact of Kachchh (Bhuj) India 2001 Earthquake

Intraplate earthquakes are ongoing activity in the Kachchh region. The area is seismically active... more Intraplate earthquakes are ongoing activity in the Kachchh region. The area is seismically active and falls in Zone V of the seismic Zoning Map of India. The devastating Bhuj (Kachchh) Earthquake of MS 7.6 occurred in this zone on 26 th January, 2001 causing severe damage to civil engineering structures and loss of human life. The surface manifestations of deformation of this earthquake include fractures/ fissures, lateral spread, slump, subsidence, upheaval, sand blows and craters. Bhuj Earthquake has caused considerable damage to earth dams lying in isoseist VIII and above in Kachchh. Damages to dams are in the form of longitudinal and transverse cracks, differential settlement, slumps/ slide, displacements and in some cases leakage. A review of the available geological, seismological and geotechnical data have been done to asses the impact of Bhuj Earthquake and subsequent seismic events in the area on ground deformation and engineering structures. Main cause of the ground deformation and damage is liquefaction of the soil under seismic shaking. Tectonic influence on the development of fissures/ cracks on the ground as well as on the earth dams has been observed.

Research paper thumbnail of An integrated approach of GIS-AHP-MCE methods for the selection of suitable sites for the shrimp farming and mangrove development- A case study of the coastal area of Vietnam

SAINS TANAH - Journal of Soil Science and Agroclimatology

This study was conducted to identify suitable sites for shrimp farming combined with the mangrove... more This study was conducted to identify suitable sites for shrimp farming combined with the mangrove development (SFM) in the coastal area of central Vietnam. An integrated approach using GIS with weighted Multi-Criteria Evaluation (MCE) by Analytic Hierarchy Process (AHP) was adopted for the selection of sites. In this study, fifteen sub-criteria belonging to three main criteria (geographical conditions, water quality and infrastructure availability) were selected as evaluation parameters in the GIS model. The study indicated that the geographical factors are the most important for the SFM development with 0.44 weight. However, the availability of such areas is limited. Results of the integrated study indicated that SFM area for development is highly suitable: 1127.82 ha (15.57%), moderately suitable: 2056.87 ha (28.4%), marginally suitable: 2835.52 ha (39.16 %) and not suitable: 3204.36 ha (17.0 %) in the Hau basin, Vietnam. In this study, we have also used GIS-AHP-MCE methods for de...

Research paper thumbnail of Novel Time Series Bagging Based Hybrid Models for Predicting Historical Water Levels in the Mekong Delta Region, Vietnam

Computer Modeling in Engineering & Sciences

Water level predictions in the river, lake and delta play an important role in flood management. ... more Water level predictions in the river, lake and delta play an important role in flood management. Every year Mekong River delta of Vietnam is experiencing flood due to heavy monsoon rains and high tides. Land subsidence may also aggravate flooding problems in this area. Therefore, accurate predictions of water levels in this region are very important to forewarn the people and authorities for taking timely adequate remedial measures to prevent losses of life and property. There are so many methods available to predict the water levels based on historical data but nowadays Machine Learning (ML) methods are considered the best tool for accurate prediction. In this study, we have used surface water level data of 18 water level measurement stations of the Mekong River delta from 2000 to 2018 to build novel time-series Bagging based hybrid ML models namely: Bagging (RF), Bagging (SOM) and Bagging (M5P) to predict historical water levels in the study area. Performances of the Bagging-based hybrid models were compared with Reduced Error Pruning Trees (REPT), which is a benchmark ML model. The data of 19 years period was divided into 70:30 ratio for the modeling. The data of the period 1/2000 to 5/2013 (which is about 70% of total data) was used for the training and for the period 5/2013 to 12/2018 (which is about 30% of total data) was used for testing (validating) the models. Performance of the models was evaluated using standard statistical measures: Coefficient of Determination (R2), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Results show that the performance of all the developed models is good (R2 > 0.9) for the prediction of water levels in the study area. However, the Bagging-based hybrid models are slightly better than another model such as REPT. Thus, these Bagging-based hybrid time series models can be used for predicting water levels at Mekong data.

Research paper thumbnail of Hybrid Model: Teaching Learning-Based Optimization of Artificial Neural Network (TLBO-ANN) for the Prediction of Soil Permeability Coefficient

Mathematical Problems in Engineering, 2022

The permeability coefficient (k-value) of the soil is an important parameter used in the civil en... more The permeability coefficient (k-value) of the soil is an important parameter used in the civil engineering design of roads, tunnels, dams, and other structures. However, the determination of k-value by experimental methods in the laboratory or the field is still costly and time-consuming. Moreover, it requires special equipment and special care in the collection of soil samples for laboratory study. Therefore, in this study, we have proposed machine learning (ML) hybrid model: teaching learning-based optimization of artificial neural network (TLBO-ANN) to predict the k-value of soil based on limited parameters (natural water content, void ratio, specific gravity, liquid limit, plastic limit, and clay content) which can be determined easily in the laboratory. Test results of 84 soil samples obtained from the Da Nang-Quang Ngai expressway project in Vietnam are used in the model development. Statistical indicators such as correlation coefficient (R), root mean square error (RMSE), and...

Research paper thumbnail of Performance assessment of artificial neural networks and support vector regression models for stream flow predictions

Environmental Monitoring and Assessment, 2018

Water resources planning, development, and management need reliable forecasts of river flows. In ... more Water resources planning, development, and management need reliable forecasts of river flows. In past few decades, an important dimension has been introduced in the prediction of the hydrologic phenomenon through artificial intelligence-based modeling. In this paper, the performance of three artificial neural network (ANN) and four support vector regression (SVR) models was investigated to predict streamflows in the Upper Indus River. Results from ANN models using three different optimization techniques, namely Broyden-Fletcher-Goldfarb-Shannon, Conjugate Gradient, and Back Propagation algorithms, were compared with one another. A further comparison was made between these ANNs and four types of SVR models which were based on linear, polynomial, radial basis function, and sigmoid kernels. Past 30 years' monthly data for precipitation, temperature, and streamflow obtained from Pakistan Surface Water Hydrology Department Lahore were used for this purpose. Three types of input combinations with respect to the main input variables (temperature, precipitation, and stream flow) and several types of input combinations with respect to time lag were tested. The best input for ANN and SVR models was identified using correlation coefficient analysis and genetic algorithm. The performance of the ANN and SVR models was evaluated by mean bias error, Nash-Sutcliffe efficiency, root mean square error, and correlation coefficient. The efficiency of the Broyden-Fletcher-Goldfarb-Shannon-ANN model was found to be much better than that of other models, while the SVR model based on radial basis function kernel predicted stream flows with comparatively higher accuracy than the other kernels. Finally, long-term predictions of streamflow have been made by the best ANN model. It was found that stream flow of Upper Indus River has a decreasing trend.

Research paper thumbnail of Predicting Load-Deflection of Composite Concrete Bridges Using Machine Learning Models

Journal of Science and Transport Technology, Dec 28, 2023

The main objective of this study is to predict accurately the loaddeflection of composite concret... more The main objective of this study is to predict accurately the loaddeflection of composite concrete bridges using two popular machine learning (ML) models namely Random Tree (RT) and Artificial Neural Network (ANN). Data from 83 track loading tests conducted on various bridges in Vietnam were collected and analyzed. Various input parameters namely bridge's crosssectional shape, length of concrete beam, number of years in use, height of the main girder, distance between the main girders were selected for the modelling. Validation indicators like R, RMSE, and MAE, and Taylor diagram were used for validation and comparison of the models. Results of this study showed that both RT and ANN are good for prediction of the load-deflection of composite concrete bridges, but RT outperforms ANN. Thus, the developed ML models can facilitate efficient bridge health monitoring and management by predicting the load-deflection of simple-span concrete bridges.

Research paper thumbnail of Prediction of white spot disease susceptibility in shrimps using decision trees based machine learning models

Applied Water Science, Dec 8, 2023

Recently, the spread of white spot disease in shrimps has a major impact on the aquaculture activ... more Recently, the spread of white spot disease in shrimps has a major impact on the aquaculture activity worldwide affecting the economy of the countries, especially SouthEast Asian countries like Vietnam. This deadly disease in shrimps is caused by the White Spot Syndrome Virus (WSSV). Researchers are trying to understand the spread and control of this disease by doing field and laboratory studies considering effect of environmental conditions on shrimps affected by WSSV. Generally, they have not considered spatial factors in their study. Therefore, in the present study, we have used spatial (distances to roads and factories) as well as physio-chemical factors of water: Chemical Oxygen Demand (COD), Dissolved Oxygen (DO), Salinity, NO 3 , P 3 O 4 and pH, for developing WSSV susceptibility maps of the area using Decision Tree (DT)-based Machine Learning (ML) models namely Random Tree (RT), Extra Tree (ET), and J48. Model's performance was evaluated using standard statistical measures including Area Under the Curve (AUC). The results indicated that ET model has the highest accuracy (AUC: 0.713) in predicting disease susceptibility in comparison to other two models (RT: 0.701 and J48: 0.641). The WSSV susceptibility maps developed by the ML technique, using DT (ET) method, will help decision makers in better planning and control of spatial spread of WSSV disease in shrimps.

Research paper thumbnail of Forecasting Construction Price Index using Artificial Intelligence Models: Support Vector Machines and Radial Basis Function Neural Network

Journal of Science and Transport Technology

Estimation of Construction Price Index (CPI) is important for a market economy and it is a measur... more Estimation of Construction Price Index (CPI) is important for a market economy and it is a measure to manage construction investment costs. This is a tool to help organizations and individuals to reduce the effort and management of expenses for construction projects by reducing time of procedures for calculating and adjusting the total investment for the estimation and evaluation of contract price. The CPI is an indicator that reflects the level of construction price fluctuations of the type of work over time. In this study, the CPI data of Son La province, Vietnam from January 2016 to March 2022 (75 dataset) has been used for the modelling. Two Artificial Intelligence (AI) models namely Support Vector Machine (SVM) and Radial Basis Function Neural Network (RBFN) were proposed to predict the CPI based on limited input data. Performance of the models in correctly predicting CPI was evaluated using standard statistical indicators such as Coefficient of Determination (R2), Root Mean ...

Research paper thumbnail of Development of effective XGB model to predict the Axial Load Capacity of circular CFST columns

Journal of Science and Transport Technology

The Axial Load Capacity (ALC) of Concrete-Filled Steel Tubular (CFST) structural members is regar... more The Axial Load Capacity (ALC) of Concrete-Filled Steel Tubular (CFST) structural members is regarded as one of the most crucial technical factors for the design of these composite structures. This work proposes the development and application of the Extreme Gradient Boosting (XGB) model to forecast the ALC of circular CFST structural components using the affecting input parameters, namely column diameter, steel tube thickness, column length, steel yield strength, and concrete compressive strength. A dataset of 2073 experimental results from the literature was used for the model development. The performance of the XGB model was evaluated using statistical criteria such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of Determination (R2), and Mean Absolute Percentage Error (MAPE). The five-fold cross-validation technique and Monte Carlo simulation method were used to evaluate the model's performance. The results show good performance of the XGB model (R2...

Research paper thumbnail of Development and application of hybrid artificial intelligence models for groundwater potential mapping and assessment

Vietnam Journal of Earth Sciences

Groundwater potential assessment is essential for optimum utilization and recharge of groundwater... more Groundwater potential assessment is essential for optimum utilization and recharge of groundwater resources for the proper development and management of an area. The main aim of this study is to develop an accurate groundwater potential map of the Dak Nong Province (Vietnam) using hybrid artificial intelligence models, which are a combination of Random Forest (RF) and its Ensemble Framework (AdaBoost - ABRF, Bagging - BRF and LogitBoost - LBRF). In this study, twelve conditioning factors, namely topography (aspect, elevation, Topographic Wetness Index - TWI, slope, and curvature), hydrology (infiltration and river density, rainfall, Sediment Transport Index - STI, Stream Power Index - SPI), land use, and soil were used to develop the models. Well, yield data was also utilized to develop and validate potential groundwater zones. One Rule (R) feature selection method was utilized to prioritize the importance of groundwater potential affecting parameters. The results indicated that the...

Research paper thumbnail of Evaluation of Shannon Entropy and Weights of Evidence Models in Landslide Susceptibility Mapping for the Pithoragarh District of Uttarakhand State, India

Advances in Civil Engineering

Landslide susceptibility mapping is considered a useful tool for planning, disaster management, a... more Landslide susceptibility mapping is considered a useful tool for planning, disaster management, and natural hazard mitigation of a region. Although there are different methods for predicting landslide susceptibility, the bivariate statistical analysis method is considered to be simple and popular. In this study, the main aim is to evaluate the performance of Shannon entropy (SE) and weights of evidence (WOE) statistical models in landslide susceptibility mapping of Pithoragarh district of Uttarakhand state, India. For this purpose, ten landslide affecting factors, namely, slope degree, aspect, curvature, elevation, land cover, slope forming materials, geomorphology (landforms), distance to rivers, distance to roads, and overburden depth were used for the development of landslide susceptibility maps using the SE and WOE methods. Data extracted from the Google Earth images, Aster Digital Elevation Model, and Geological Survey of India report were used for the construction and evaluati...

Research paper thumbnail of Hybridization of Differential Evolution and Adaptive-NetworkBased Fuzzy Inference System in Estimation of Compression Coefficient of Plastic Clay Soil

Computer Modeling in Engineering & Sciences, 2022

One of the important geotechnical parameters required for designing of the civil engineering stru... more One of the important geotechnical parameters required for designing of the civil engineering structure is the compressibility of the soil. In this study, the main purpose is to develop a novel hybrid Machine Learning (ML) model (ANFIS-DE), which used Differential Evolution (DE) algorithm to optimize the predictive capability of Adaptive-Network-based Fuzzy Inference System (ANFIS), for estimating soil Compression coefficient (Cc) from other geotechnical parameters namely Water Content, Void Ratio, Specific Gravity, Liquid Limit, Plastic Limit, Clay content and Depth of Soil Samples. Validation of the predictive capability of the novel model was carried out using statistical indices: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Correlation Coefficient (R). In addition, two popular ML models namely Reduced Error Pruning Trees (REPTree) and Decision Stump (Dstump) were used for comparison. Results showed that the performance of the novel model ANFIS-DE is the best (R = 0.825, MAE = 0.064 and RMSE = 0.094) in comparison to other models such as REPTree (R = 0.7802, MAE = 0.068 and RMSE = 0.0988) and Dstump (R = 0.7325, MAE = 0.0785 and RMSE = 0.1036). Therefore, the ANFIS-DE model can be used as a promising tool for the correct and quick estimation of the soil Cc, which can be employed in the design and construction of civil engineering structures.

Research paper thumbnail of A Comparative Study of Soft Computing Models for Prediction of Permeability Coefficient of Soil

Mathematical Problems in Engineering, 2021

Determination of the permeability coefficient (K) of soil is considered as one of the essential s... more Determination of the permeability coefficient (K) of soil is considered as one of the essential steps to assess infiltration, runoff, groundwater, and drainage in the design process of the construction projects. In this study, three cost-effective algorithms, namely, artificial neural network (ANN), support vector machine (SVM), and random forest (RF), which are well-known as advanced machine learning techniques, were used to predict the permeability coefficient (K) of soil (10−9 cm/s), based on a set of simple six input parameters such as natural water content w (%), void ratio (e), specific density (g/cm3), liquid limit (LL) (%), plastic limit (PL) (%), and clay content (%). For this, a total of 84 soil samples data collected from the detailed design stage investigations of Da Nang-Quang Ngai national road project in Vietnam was used to generate training (70%) and testing (30%) datasets for building and validating the models. Statistical error indicators such as RMSE and MAE and c...

Research paper thumbnail of GIS-Based Soft Computing Models for Landslide Susceptibility Mapping: A Case Study of Pithoragarh District, Uttarakhand State, India

Mathematical Problems in Engineering, 2021

The main objective of the study was to investigate performance of three soft computing models: Na... more The main objective of the study was to investigate performance of three soft computing models: Naïve Bayes (NB), Multilayer Perceptron (MLP) neural network classifier, and Alternating Decision Tree (ADT) in landslide susceptibility mapping of Pithoragarh District of Uttarakhand State, India. For this purpose, data of 91 past landslide locations and ten landslide influencing factors, namely, slope degree, curvature, aspect, land cover, slope forming materials (SFM), elevation, distance to rivers, geomorphology, overburden depth, and distance to roads were considered in the models study. Thematic maps of the Geological Survey of India (GSI), Google Earth images, and Aster Digital Elevation Model (DEM) were used for the development of landslide susceptibility maps in the Geographic Information System (GIS) environment. Landslide locations data was divided into a 70 : 30 ratio for the training (70%) and testing/validation (30%) of the three models. Standard statistical measures, namely,...

Research paper thumbnail of Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil

Mathematical Problems in Engineering, 2021

The main objective of this study is to evaluate and compare the performance of different machine ... more The main objective of this study is to evaluate and compare the performance of different machine learning (ML) algorithms, namely, Artificial Neural Network (ANN), Extreme Learning Machine (ELM), and Boosting Trees (Boosted) algorithms, considering the influence of various training to testing ratios in predicting the soil shear strength, one of the most critical geotechnical engineering properties in civil engineering design and construction. For this aim, a database of 538 soil samples collected from the Long Phu 1 power plant project, Vietnam, was utilized to generate the datasets for the modeling process. Different ratios (i.e., 10/90, 20/80, 30/70, 40/60, 50/50, 60/40, 70/30, 80/20, and 90/10) were used to divide the datasets into the training and testing datasets for the performance assessment of models. Popular statistical indicators, such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Correlation Coefficient (R), were employed to evaluate the predictive cap...

Research paper thumbnail of Estimating the Compressive Strength of Self-compacting Concrete with fiber using an Extreme Gradient Boosting model

Self-compacting concrete reinforced with fiber (SCCRF) is extensively utilized in the constructio... more Self-compacting concrete reinforced with fiber (SCCRF) is extensively utilized in the construction and transportation industries due to its numerous advantages, such as ease of building in challenging sites, noise reduction, enhanced tensile strength, bending strength, and decreased structural cracking. Traditional methods for assessing the compressive strength of SCCRF are generally time-consuming and expensive, necessitating the development of a model to forecast compressive strength. This research aimed to predict the CS of SCCRF using the Extreme Gradient Boosting (XGB) machine learning technique. The research uses the grid search method to optimize the XGB model's hyperparameters. A database of 387 samples is collected in this work, which is also an enormous dataset compared to those utilized in previous studies. An excellent result (R 2 max = 0.97798 for the testing dataset) proves that the proposed XGB model has excellent predictive power. Finally, Shapley Additive exPlanations (SHAP) analysis is conducted to understand the effect of each input variable on the predicted CS of SCCRF. The results show that the samples' age and cement content are the most critical factors affecting the CS. As a result, the proposed XGB model is a valuable tool for helping materials engineers have the right orientation in the design of SCCRF components to achieve the required compressive strength.

Research paper thumbnail of Evaluation and Management of Waterlogging Problems in the Command Area of Kadana Dam, Kheda District Using GIS

International Journal for Scientific Research and Development, Jun 1, 2015

Water logging problem occurring in the command area of Mahi Right Bank Canal (MRBC) of Kadana dam... more Water logging problem occurring in the command area of Mahi Right Bank Canal (MRBC) of Kadana dam has been evaluated using remote sensing and Geographical Information Systems (GIS). Various thematic maps have been prepared such as topography, geohydrology, soil, land use, Digital Elevation Model (DEM) and synthesised with the ground water and surface water and collated with irrigation data in GIS environment Major part of MRBC command area is occupied by Alluvium of Sabarmati and Mahi River and most part of the area is flat. Appreciable rise in the sub-soil water level has been observed since 1980 that is after starting irrigation through canal network of MRBC. Now major part of the Kheda district is either affected by water logging and salinization or presently under critical condition requiring immediate remedial measures and suitable management plan. Three talukas of Kheda district namely Matar, Mahudha and Kapadvanj are almost water logged and remaining under critical stage except part of Balasinor and Mehmedabad where water table is still in safe zone. Flat topography (0 to 1° slope), inadequate surface and subsurface drainage, poor maintenance of existing drainage system, over irrigation and cultivation of more water intensive crops after the operation of canal network are some of major causes of water logging in the area.

Research paper thumbnail of Rock Mass Evaluation of the Sardar Sarovar (Narmada) Dam and Underground Powerhouse, India

Geotechnical evaluation of the 163 m high concrete gravity Sardar Sarovar (Narmada) dam, under co... more Geotechnical evaluation of the 163 m high concrete gravity Sardar Sarovar (Narmada) dam, under construction, and 1200 MW underground powerhouse (210m x 23m x 57.5m) and its ancillary structures has been done. The dam and powerhouse sites are occupied by basalt flows underlain by infra-trappean sedimentary rocks (Bagh beds) intruded by basic dykes. The area is structurally complex and seismically active. Intra-formational shears and sub-horizontal to low dipping weak layers like red bole, tuff, argillaceous sandstone having low values of shear parameters posed the problem of sliding stability of dam blocks. Concrete shear keys were provided as one of the remedial measures. Differential settlement was apprehended in the foundation of dam having varying physicoengineering properties and rock mass characteristics. Reinforced concrete mats were provided to treat the weathered and sheared rock mass and 34.5m deep reinforced concrete plug to prevent differential settlement of dam blocks located on river channel (dam base) fault. The horizontal seismic coefficient adopted for the dam is 0.125g. The construction of 1200 MW underground powerhouse located in basalt is nearing completion. During progressive excavation of the machine hall (cavern) cracks were observed in the 57.5m high shotcreted walls. Additional longer rock bolts/ cables/ tendons were provided as remedial measures. Draft tube and exit tunnels are passing through dolerite rocks dissected by chlorite-coated joints and slaked rock zones. Rib supports were introduced after observing behaviour of the rock mass and collapses in part of these tunnels.

Research paper thumbnail of Application of Observational Method in the Successful Construction of Underground Structures, Sardar Sarovar (Narmada) Project, Gujarat, India

The Sardar Sarovar (Narmada) underground powerhouse is located in Deccan Basalt flows in the lowe... more The Sardar Sarovar (Narmada) underground powerhouse is located in Deccan Basalt flows in the lower Narmada valley in Gujarat state. These flows are intruded by dolerite dykes and sill. Basalt flows and dolerite rocks are considered good tunneling media for locating underground structures. Therefore, initial designing of supports was done considering good rock mass conditions. However, during construction numerous geotechnical problems were encountered necessitating review of support system. Rock falls and collapses were observed in tunnel sections passing through dolerite dykes and sills. Cracks were observed in the walls of the powerhouse cavern. During progressive excavations back analysis was done to know the causes of distress in rock mass and structures The 'Observational Method' adopted during construction resulted in the safe execution of structures by timely modification and installation of adequate support system.

Research paper thumbnail of Geotechnical Problems and Treatments of Weathered Rock Seams Occurring in the Foundation of Karjan Dam, Western India 2.07

The Karjan dam is located on the Deccan Basalt flows of Cretaceous -Eocene age in the N~illnada v... more The Karjan dam is located on the Deccan Basalt flows of Cretaceous -Eocene age in the N~illnada va.lley in Gujarat state. A characteristic feature of lbc basalt flows in this area is conspicuous presence of a number of suh•lwrizont.al weathered rock seams posing the foundation problems of settlement, sliding and seepage. Concrete shear keys have been provided to increase the sliding stability of dam blocks besides other remedial measures. Additional curtain grouting has been done after completion of the dam to redm.::c seepage.

Research paper thumbnail of Geotechnical Assessment and Evaluation of the Impact of Kachchh (Bhuj) India 2001 Earthquake

Intraplate earthquakes are ongoing activity in the Kachchh region. The area is seismically active... more Intraplate earthquakes are ongoing activity in the Kachchh region. The area is seismically active and falls in Zone V of the seismic Zoning Map of India. The devastating Bhuj (Kachchh) Earthquake of MS 7.6 occurred in this zone on 26 th January, 2001 causing severe damage to civil engineering structures and loss of human life. The surface manifestations of deformation of this earthquake include fractures/ fissures, lateral spread, slump, subsidence, upheaval, sand blows and craters. Bhuj Earthquake has caused considerable damage to earth dams lying in isoseist VIII and above in Kachchh. Damages to dams are in the form of longitudinal and transverse cracks, differential settlement, slumps/ slide, displacements and in some cases leakage. A review of the available geological, seismological and geotechnical data have been done to asses the impact of Bhuj Earthquake and subsequent seismic events in the area on ground deformation and engineering structures. Main cause of the ground deformation and damage is liquefaction of the soil under seismic shaking. Tectonic influence on the development of fissures/ cracks on the ground as well as on the earth dams has been observed.

Research paper thumbnail of An integrated approach of GIS-AHP-MCE methods for the selection of suitable sites for the shrimp farming and mangrove development- A case study of the coastal area of Vietnam

SAINS TANAH - Journal of Soil Science and Agroclimatology

This study was conducted to identify suitable sites for shrimp farming combined with the mangrove... more This study was conducted to identify suitable sites for shrimp farming combined with the mangrove development (SFM) in the coastal area of central Vietnam. An integrated approach using GIS with weighted Multi-Criteria Evaluation (MCE) by Analytic Hierarchy Process (AHP) was adopted for the selection of sites. In this study, fifteen sub-criteria belonging to three main criteria (geographical conditions, water quality and infrastructure availability) were selected as evaluation parameters in the GIS model. The study indicated that the geographical factors are the most important for the SFM development with 0.44 weight. However, the availability of such areas is limited. Results of the integrated study indicated that SFM area for development is highly suitable: 1127.82 ha (15.57%), moderately suitable: 2056.87 ha (28.4%), marginally suitable: 2835.52 ha (39.16 %) and not suitable: 3204.36 ha (17.0 %) in the Hau basin, Vietnam. In this study, we have also used GIS-AHP-MCE methods for de...

Research paper thumbnail of Novel Time Series Bagging Based Hybrid Models for Predicting Historical Water Levels in the Mekong Delta Region, Vietnam

Computer Modeling in Engineering & Sciences

Water level predictions in the river, lake and delta play an important role in flood management. ... more Water level predictions in the river, lake and delta play an important role in flood management. Every year Mekong River delta of Vietnam is experiencing flood due to heavy monsoon rains and high tides. Land subsidence may also aggravate flooding problems in this area. Therefore, accurate predictions of water levels in this region are very important to forewarn the people and authorities for taking timely adequate remedial measures to prevent losses of life and property. There are so many methods available to predict the water levels based on historical data but nowadays Machine Learning (ML) methods are considered the best tool for accurate prediction. In this study, we have used surface water level data of 18 water level measurement stations of the Mekong River delta from 2000 to 2018 to build novel time-series Bagging based hybrid ML models namely: Bagging (RF), Bagging (SOM) and Bagging (M5P) to predict historical water levels in the study area. Performances of the Bagging-based hybrid models were compared with Reduced Error Pruning Trees (REPT), which is a benchmark ML model. The data of 19 years period was divided into 70:30 ratio for the modeling. The data of the period 1/2000 to 5/2013 (which is about 70% of total data) was used for the training and for the period 5/2013 to 12/2018 (which is about 30% of total data) was used for testing (validating) the models. Performance of the models was evaluated using standard statistical measures: Coefficient of Determination (R2), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Results show that the performance of all the developed models is good (R2 > 0.9) for the prediction of water levels in the study area. However, the Bagging-based hybrid models are slightly better than another model such as REPT. Thus, these Bagging-based hybrid time series models can be used for predicting water levels at Mekong data.

Research paper thumbnail of Hybrid Model: Teaching Learning-Based Optimization of Artificial Neural Network (TLBO-ANN) for the Prediction of Soil Permeability Coefficient

Mathematical Problems in Engineering, 2022

The permeability coefficient (k-value) of the soil is an important parameter used in the civil en... more The permeability coefficient (k-value) of the soil is an important parameter used in the civil engineering design of roads, tunnels, dams, and other structures. However, the determination of k-value by experimental methods in the laboratory or the field is still costly and time-consuming. Moreover, it requires special equipment and special care in the collection of soil samples for laboratory study. Therefore, in this study, we have proposed machine learning (ML) hybrid model: teaching learning-based optimization of artificial neural network (TLBO-ANN) to predict the k-value of soil based on limited parameters (natural water content, void ratio, specific gravity, liquid limit, plastic limit, and clay content) which can be determined easily in the laboratory. Test results of 84 soil samples obtained from the Da Nang-Quang Ngai expressway project in Vietnam are used in the model development. Statistical indicators such as correlation coefficient (R), root mean square error (RMSE), and...

Research paper thumbnail of Performance assessment of artificial neural networks and support vector regression models for stream flow predictions

Environmental Monitoring and Assessment, 2018

Water resources planning, development, and management need reliable forecasts of river flows. In ... more Water resources planning, development, and management need reliable forecasts of river flows. In past few decades, an important dimension has been introduced in the prediction of the hydrologic phenomenon through artificial intelligence-based modeling. In this paper, the performance of three artificial neural network (ANN) and four support vector regression (SVR) models was investigated to predict streamflows in the Upper Indus River. Results from ANN models using three different optimization techniques, namely Broyden-Fletcher-Goldfarb-Shannon, Conjugate Gradient, and Back Propagation algorithms, were compared with one another. A further comparison was made between these ANNs and four types of SVR models which were based on linear, polynomial, radial basis function, and sigmoid kernels. Past 30 years' monthly data for precipitation, temperature, and streamflow obtained from Pakistan Surface Water Hydrology Department Lahore were used for this purpose. Three types of input combinations with respect to the main input variables (temperature, precipitation, and stream flow) and several types of input combinations with respect to time lag were tested. The best input for ANN and SVR models was identified using correlation coefficient analysis and genetic algorithm. The performance of the ANN and SVR models was evaluated by mean bias error, Nash-Sutcliffe efficiency, root mean square error, and correlation coefficient. The efficiency of the Broyden-Fletcher-Goldfarb-Shannon-ANN model was found to be much better than that of other models, while the SVR model based on radial basis function kernel predicted stream flows with comparatively higher accuracy than the other kernels. Finally, long-term predictions of streamflow have been made by the best ANN model. It was found that stream flow of Upper Indus River has a decreasing trend.

Research paper thumbnail of Predicting Load-Deflection of Composite Concrete Bridges Using Machine Learning Models

Journal of Science and Transport Technology, Dec 28, 2023

The main objective of this study is to predict accurately the loaddeflection of composite concret... more The main objective of this study is to predict accurately the loaddeflection of composite concrete bridges using two popular machine learning (ML) models namely Random Tree (RT) and Artificial Neural Network (ANN). Data from 83 track loading tests conducted on various bridges in Vietnam were collected and analyzed. Various input parameters namely bridge's crosssectional shape, length of concrete beam, number of years in use, height of the main girder, distance between the main girders were selected for the modelling. Validation indicators like R, RMSE, and MAE, and Taylor diagram were used for validation and comparison of the models. Results of this study showed that both RT and ANN are good for prediction of the load-deflection of composite concrete bridges, but RT outperforms ANN. Thus, the developed ML models can facilitate efficient bridge health monitoring and management by predicting the load-deflection of simple-span concrete bridges.

Research paper thumbnail of Prediction of white spot disease susceptibility in shrimps using decision trees based machine learning models

Applied Water Science, Dec 8, 2023

Recently, the spread of white spot disease in shrimps has a major impact on the aquaculture activ... more Recently, the spread of white spot disease in shrimps has a major impact on the aquaculture activity worldwide affecting the economy of the countries, especially SouthEast Asian countries like Vietnam. This deadly disease in shrimps is caused by the White Spot Syndrome Virus (WSSV). Researchers are trying to understand the spread and control of this disease by doing field and laboratory studies considering effect of environmental conditions on shrimps affected by WSSV. Generally, they have not considered spatial factors in their study. Therefore, in the present study, we have used spatial (distances to roads and factories) as well as physio-chemical factors of water: Chemical Oxygen Demand (COD), Dissolved Oxygen (DO), Salinity, NO 3 , P 3 O 4 and pH, for developing WSSV susceptibility maps of the area using Decision Tree (DT)-based Machine Learning (ML) models namely Random Tree (RT), Extra Tree (ET), and J48. Model's performance was evaluated using standard statistical measures including Area Under the Curve (AUC). The results indicated that ET model has the highest accuracy (AUC: 0.713) in predicting disease susceptibility in comparison to other two models (RT: 0.701 and J48: 0.641). The WSSV susceptibility maps developed by the ML technique, using DT (ET) method, will help decision makers in better planning and control of spatial spread of WSSV disease in shrimps.

Research paper thumbnail of Forecasting Construction Price Index using Artificial Intelligence Models: Support Vector Machines and Radial Basis Function Neural Network

Journal of Science and Transport Technology

Estimation of Construction Price Index (CPI) is important for a market economy and it is a measur... more Estimation of Construction Price Index (CPI) is important for a market economy and it is a measure to manage construction investment costs. This is a tool to help organizations and individuals to reduce the effort and management of expenses for construction projects by reducing time of procedures for calculating and adjusting the total investment for the estimation and evaluation of contract price. The CPI is an indicator that reflects the level of construction price fluctuations of the type of work over time. In this study, the CPI data of Son La province, Vietnam from January 2016 to March 2022 (75 dataset) has been used for the modelling. Two Artificial Intelligence (AI) models namely Support Vector Machine (SVM) and Radial Basis Function Neural Network (RBFN) were proposed to predict the CPI based on limited input data. Performance of the models in correctly predicting CPI was evaluated using standard statistical indicators such as Coefficient of Determination (R2), Root Mean ...

Research paper thumbnail of Development of effective XGB model to predict the Axial Load Capacity of circular CFST columns

Journal of Science and Transport Technology

The Axial Load Capacity (ALC) of Concrete-Filled Steel Tubular (CFST) structural members is regar... more The Axial Load Capacity (ALC) of Concrete-Filled Steel Tubular (CFST) structural members is regarded as one of the most crucial technical factors for the design of these composite structures. This work proposes the development and application of the Extreme Gradient Boosting (XGB) model to forecast the ALC of circular CFST structural components using the affecting input parameters, namely column diameter, steel tube thickness, column length, steel yield strength, and concrete compressive strength. A dataset of 2073 experimental results from the literature was used for the model development. The performance of the XGB model was evaluated using statistical criteria such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of Determination (R2), and Mean Absolute Percentage Error (MAPE). The five-fold cross-validation technique and Monte Carlo simulation method were used to evaluate the model's performance. The results show good performance of the XGB model (R2...

Research paper thumbnail of Development and application of hybrid artificial intelligence models for groundwater potential mapping and assessment

Vietnam Journal of Earth Sciences

Groundwater potential assessment is essential for optimum utilization and recharge of groundwater... more Groundwater potential assessment is essential for optimum utilization and recharge of groundwater resources for the proper development and management of an area. The main aim of this study is to develop an accurate groundwater potential map of the Dak Nong Province (Vietnam) using hybrid artificial intelligence models, which are a combination of Random Forest (RF) and its Ensemble Framework (AdaBoost - ABRF, Bagging - BRF and LogitBoost - LBRF). In this study, twelve conditioning factors, namely topography (aspect, elevation, Topographic Wetness Index - TWI, slope, and curvature), hydrology (infiltration and river density, rainfall, Sediment Transport Index - STI, Stream Power Index - SPI), land use, and soil were used to develop the models. Well, yield data was also utilized to develop and validate potential groundwater zones. One Rule (R) feature selection method was utilized to prioritize the importance of groundwater potential affecting parameters. The results indicated that the...

Research paper thumbnail of Evaluation of Shannon Entropy and Weights of Evidence Models in Landslide Susceptibility Mapping for the Pithoragarh District of Uttarakhand State, India

Advances in Civil Engineering

Landslide susceptibility mapping is considered a useful tool for planning, disaster management, a... more Landslide susceptibility mapping is considered a useful tool for planning, disaster management, and natural hazard mitigation of a region. Although there are different methods for predicting landslide susceptibility, the bivariate statistical analysis method is considered to be simple and popular. In this study, the main aim is to evaluate the performance of Shannon entropy (SE) and weights of evidence (WOE) statistical models in landslide susceptibility mapping of Pithoragarh district of Uttarakhand state, India. For this purpose, ten landslide affecting factors, namely, slope degree, aspect, curvature, elevation, land cover, slope forming materials, geomorphology (landforms), distance to rivers, distance to roads, and overburden depth were used for the development of landslide susceptibility maps using the SE and WOE methods. Data extracted from the Google Earth images, Aster Digital Elevation Model, and Geological Survey of India report were used for the construction and evaluati...

Research paper thumbnail of Hybridization of Differential Evolution and Adaptive-NetworkBased Fuzzy Inference System in Estimation of Compression Coefficient of Plastic Clay Soil

Computer Modeling in Engineering & Sciences, 2022

One of the important geotechnical parameters required for designing of the civil engineering stru... more One of the important geotechnical parameters required for designing of the civil engineering structure is the compressibility of the soil. In this study, the main purpose is to develop a novel hybrid Machine Learning (ML) model (ANFIS-DE), which used Differential Evolution (DE) algorithm to optimize the predictive capability of Adaptive-Network-based Fuzzy Inference System (ANFIS), for estimating soil Compression coefficient (Cc) from other geotechnical parameters namely Water Content, Void Ratio, Specific Gravity, Liquid Limit, Plastic Limit, Clay content and Depth of Soil Samples. Validation of the predictive capability of the novel model was carried out using statistical indices: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Correlation Coefficient (R). In addition, two popular ML models namely Reduced Error Pruning Trees (REPTree) and Decision Stump (Dstump) were used for comparison. Results showed that the performance of the novel model ANFIS-DE is the best (R = 0.825, MAE = 0.064 and RMSE = 0.094) in comparison to other models such as REPTree (R = 0.7802, MAE = 0.068 and RMSE = 0.0988) and Dstump (R = 0.7325, MAE = 0.0785 and RMSE = 0.1036). Therefore, the ANFIS-DE model can be used as a promising tool for the correct and quick estimation of the soil Cc, which can be employed in the design and construction of civil engineering structures.

Research paper thumbnail of A Comparative Study of Soft Computing Models for Prediction of Permeability Coefficient of Soil

Mathematical Problems in Engineering, 2021

Determination of the permeability coefficient (K) of soil is considered as one of the essential s... more Determination of the permeability coefficient (K) of soil is considered as one of the essential steps to assess infiltration, runoff, groundwater, and drainage in the design process of the construction projects. In this study, three cost-effective algorithms, namely, artificial neural network (ANN), support vector machine (SVM), and random forest (RF), which are well-known as advanced machine learning techniques, were used to predict the permeability coefficient (K) of soil (10−9 cm/s), based on a set of simple six input parameters such as natural water content w (%), void ratio (e), specific density (g/cm3), liquid limit (LL) (%), plastic limit (PL) (%), and clay content (%). For this, a total of 84 soil samples data collected from the detailed design stage investigations of Da Nang-Quang Ngai national road project in Vietnam was used to generate training (70%) and testing (30%) datasets for building and validating the models. Statistical error indicators such as RMSE and MAE and c...

Research paper thumbnail of GIS-Based Soft Computing Models for Landslide Susceptibility Mapping: A Case Study of Pithoragarh District, Uttarakhand State, India

Mathematical Problems in Engineering, 2021

The main objective of the study was to investigate performance of three soft computing models: Na... more The main objective of the study was to investigate performance of three soft computing models: Naïve Bayes (NB), Multilayer Perceptron (MLP) neural network classifier, and Alternating Decision Tree (ADT) in landslide susceptibility mapping of Pithoragarh District of Uttarakhand State, India. For this purpose, data of 91 past landslide locations and ten landslide influencing factors, namely, slope degree, curvature, aspect, land cover, slope forming materials (SFM), elevation, distance to rivers, geomorphology, overburden depth, and distance to roads were considered in the models study. Thematic maps of the Geological Survey of India (GSI), Google Earth images, and Aster Digital Elevation Model (DEM) were used for the development of landslide susceptibility maps in the Geographic Information System (GIS) environment. Landslide locations data was divided into a 70 : 30 ratio for the training (70%) and testing/validation (30%) of the three models. Standard statistical measures, namely,...

Research paper thumbnail of Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil

Mathematical Problems in Engineering, 2021

The main objective of this study is to evaluate and compare the performance of different machine ... more The main objective of this study is to evaluate and compare the performance of different machine learning (ML) algorithms, namely, Artificial Neural Network (ANN), Extreme Learning Machine (ELM), and Boosting Trees (Boosted) algorithms, considering the influence of various training to testing ratios in predicting the soil shear strength, one of the most critical geotechnical engineering properties in civil engineering design and construction. For this aim, a database of 538 soil samples collected from the Long Phu 1 power plant project, Vietnam, was utilized to generate the datasets for the modeling process. Different ratios (i.e., 10/90, 20/80, 30/70, 40/60, 50/50, 60/40, 70/30, 80/20, and 90/10) were used to divide the datasets into the training and testing datasets for the performance assessment of models. Popular statistical indicators, such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Correlation Coefficient (R), were employed to evaluate the predictive cap...