A modified firefly algorithm-artificial neural network expert system for predicting compressive and tensile strength of high-performance concrete

Environmental assessment of green concretes for structural use

Mechanical properties and prediction of fracture parameters of geopolymer/alkali-activated mortar modified with PVA fiber and nano-SiO2

Shear performance of reinforced concrete beams incorporating recycled concrete aggregate and high-volume fly ash

An intelligent structural damage detection approach based on self-powered wireless sensor data

This paper presents Multivariate Adaptive Regression Splines (MARS) model to predict the fracture characteristics of high strength and ultra high strength concrete beams. Fracture characteristics include fracture energy (GF), critical stress intensity factor (KIC) and critical crack tip opening displacement (CTODc). This paper also presents the details of development of MARS model to predict failure load (Pmax) of high strength concrete (HSC) and ultra high strength concrete (UHSC) beam specimens. Characterization of mix and testing of beams of high strength and ultra strength concrete have been described. Methodologies for evaluation of fracture energy, critical stress intensity factor and critical crack tip opening displacement have been outlined. MARS model has been developed by establishing a relationship between a set of predicators and dependent variables. MARS is based on a divide and conquers strategy partitioning the training data sets into separate regions; each gets its own regression line. Four MARS models have been developed by using MATLAB software for training and prediction of fracture parameters and failure load.MARS has been trained with about 70% of the total 87 data sets and tested with about 30% of the total data sets. It is observed from the studies that the predicted values of Pmax, GF , KIC and CTODC are in good agreement with those of the experimental values.

Multivariate Adaptive Regression Splines Model to PredictFracture Characteristics of High Strength and Ultra HighStrength Concrete Beams

This paper presents fracture mechanics based Artificial Neural Network (ANN) model to predict the fracture characteristics of high strength and ultra high strength concrete beams. Fracture characteristics include fracture energy (G f ), critical stress intensity factor (KIC) and critical crack tip opening displacement (CTODc). Failure load of the beam (Pmax) is also predicated by using ANN model. Characterization of mix and testing of beams of high strength and ultra strength concrete have been described. Methodologies for evaluation of fracture energy, critical stress intensity factor and critical crack tip opening displacement have been outlined. Back-propagation training technique has been employed for updating the weights of each layer based on the error in the network output. LevenbergMarquardt algorithm has been used for feed-forward back-propagation. Four ANN models have been developed by using MATLAB software for training and prediction of fracture parameters and failure load. ANN has been trained with about 70% of the total 87 data sets and tested with about 30% of the total data sets. It is observed from the studies that the predicted values of Pmax, G f , failure load, KIc and CTODc are in good agreement with those of the experimental values.

ANN Model to Predict Fracture Characteristics of HighStrength and Ultra High Strength Concrete Beams

Appraisal of groundwater quality for drinking and irrigation suitability using multivariate statistical approach in a rapidly developing urban area, Tirunelveli, India.

Recently, groundwater resources have become the main freshwater supply for human activities worldwide, especially in semi-arid regions, and groundwater pollution from anthropological events is one of the chief environmental problems in built-up and industrial coastal areas. Many researchers around the world have conducted studies to evaluate the impact of groundwater management. For this study, GIS based GOD vulnerability models were used to assess the intrinsic impact and risk of pollution of coastal and irrigated areas in Tiruchendur Taluk, Thoothukudi district in Tamil Nadu. Here, GOD stands for G—Groundwater hydraulic confinement, O—Overlying aquifer strata, and D—Depth to groundwater. The parameters of G, O, and D show that 70% of the study area consists of an unconfined aquifer whose central part often consists of sandstone and fine to medium clay with sand along the coast that acts as an aquifer. The recorded value was 1–28 mbgl. The map of vulnerability using the GOD method shows that 32% of the medium vulnerabilities are located in the almost northern part of the study area, where the main source of pollution is from agricultural land and anthropological activities. A total of 39 groundwater samples were collected from different types of aquifers and used to validate the pollution map, using the EC concentration (230 to 15,480 µs/cm with an average of 2758 µs/cm) and NO3− concentration (2 to 120 mg/L with an average of 46 mg/L) in groundwater as indicators of pollution. Finally, we measured how the EC and NO3− parameters represent the medium vulnerability zone of the GOD model based on the pattern of their concentrations in groundwater. Therefore, the GIS with GOD model is the best model among these models for predicting groundwater vulnerability in Tiruchendur Taluk.

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S.K. Sekar

Papers

Multivariate Adaptive Regression Splines Model to PredictFracture Characteristics of High Strength and Ultra HighStrength Concrete Beams

ANN Model to Predict Fracture Characteristics of HighStrength and Ultra High Strength Concrete Beams

Appraisal of groundwater quality for drinking and irrigation suitability using multivariate statistical approach in a rapidly developing urban area, Tirunelveli, India.

Appraisal of Groundwater Vulnerability Pollution Mapping Using GIS Based GOD Index in Tiruchendur, Thoothukudi District, India

Hydrogeological Studies At Jalakandapuram Sub – Basin Of Sarabanga Minor Basin, Salem District,Tamil Nadu.