Bio: S.K. Sekar is an academic researcher from VIT University. The author has contributed to research in topic(s): Lime mortar & Flexural strength. The author has an hindex of 10, co-authored 19 publication(s) receiving 253 citation(s).
Abstract: This paper examines the applicability of support vector machine (SVM) based regression to predict fracture characteristics and failure load ( P max ) of high strength and ultra high strength concrete beams. Characterization of mix and testing of beams of high strength and ultra strength concrete have been described briefly. Methodologies for evaluation of fracture energy, critical stress intensity factor and critical crack tip opening displacement have been outlined. Support Vector Regression (SVR) is the extension of SVMs to solve regression and prediction problems. The main characteristics of SVR includes minimizing the observed training error, attempts to minimize the generalized error bound so as to achieve generalized performance. Four Support Vector Regression (SVR) models have been developed using MATLAB software for training and prediction of fracture characteristics. It is observed that the predicted values from the SVR models are in good agreement with those of the experimental values.
TL;DR: The Nilgiris district of Tamil Nadu state is one of the severe to very high landslide hazard prone areas of India, according to the landslide hazard atlas published by Building Materials and Technology Promotion Council, Government of India.
Abstract: Landslide is one of the major natural hazards that are commonly experienced in hilly terrains all over the world. Landslides are affect at least 15 per cent of the land area of Indian area which exceeds 0.49 million km2. In India the incidence of landslides in Himalayas and other hill ranges is an annual and recurring phenomenon. There is a variation in the degree of landslide incidences in various hill ranges. For example, the landslide incidences are high to very high in Himalayas, high in Northeastern hill ranges, high to moderatein Western Ghats & Nilgiris and low in the hill ranges of Eastern Ghats & Vindhyas. The landslide hazard zonation atlas of India published by Building Materials and Technology Promotion Council (BMTPC), Government of India reveals that the Nilgiris district of Tamil Nadu state is one of the severe to very high landslide hazard prone areas of India.
Abstract: This paper deals with experimental investigation on mechanical properties and fracture toughness of Eco-friendly concrete produced, using coconut shell as coarse aggregate, blast furnace slag as a partial replacement for cement and manufactured sand as fine aggregate. Three mixes were selected and three types of curing like water curing, steam curing and conceal curing of concrete were adopted. Mechanical properties like compressive strength, flexural strength, static modulus of elasticity, Poisson’s ratio and fracture toughness were investigated. The results proved that the mechanical properties and fracture toughness of coconut shell concrete are on par with other light weight concrete.
Abstract: In the present study, lime mortar samples from the restoration site of Vadakumnathan temple, Kerala, India have been analyzed. Samples from three different locations of the temple such as ancient wall, gopuram and arch have been taken. Traditional methods such as chemical analysis, acid loss analysis and organic test were conducted on mortar samples. Modern instrumental techniques such as electronic particle size distribution, X-ray Diffraction (XRD), scanning electron microscopy coupled with energy dispersive X-ray spectroscopy (SEM/EDX), Thermo Gravimetric Analysis (TGA), Differential Thermal Analysis (DTA) and Infrared Spectroscopy (FT-IR) were employed in the study. The binder used in the mortar is calcium high with 30% of clay mineral. A binder to aggregate ratio in the range of 1:1.5–2.5 has been established from acid loss analysis. Particle of the aggregate are mostly silt in nature, hence nominal sand would have been grinded to reduce the particle size and to induce pozzolanic reaction. The presence of carbohydrate, protein and fats are identified by organic test that are in agreement with FT-IR analysis and TGA. Calcite, aragonite and calcium complexes of silicate and aluminates in form of hydro thermal product namely gyrolite and okenite are present in wall and gopuram samples. The formation of hydrothermal products confirms that the mortar was produced by hot lime technology. In TGA, the decomposition of CaCO 3 to CO 2 between 600 and 770 °C reveals the transformation of calcite from complex forms of CSH (gyrolite and okenite) and CAH. The presence of degraded products such as syngenite and gypsum in arch sample shows that the lime mortar is in complete deterioration where as mortar remained in good condition in gopuram and wall samples.Texture along with elemental analysis (EDX) confirms the results of chemical analysis.
Abstract: In this study the stress-strain behaviour of coconut shell concrete incorporating ground granulated blast furnace slag and manufactured sand was obtained and it was in good fit with popovics model (Mo et al., 2015). Coconut shell concrete of grade M20 was achieved using 401 kg/m 3 of cement by conceal curing. The flexural behaviour of under-reinforced and over-reinforced coconut shell concrete designed by limit state method using the actual stress-strain behaviour is analogous with the experimental values. The deflection and crackwidth of coconut shell concrete is comparable with the permissible values given by IS 456:2000, ACI-318 and EC 2:1992.
TL;DR: The result indicates that the MFA-ANN hybrid system can obtain a better prediction of the high-performance concrete properties and can provide an efficient and accurate tool to predict and design HPC.
Abstract: The compressive and tensile strength of high-performance concrete (HPC) is a highly nonlinear function of its constituents. The significance of expert frameworks for predicting the compressive and tensile strength of HPC is greatly distinguished in material technology. This study aims to develop an expert system based on the artificial neural network (ANN) model in association with a modified firefly algorithm (MFA). The ANN model is constructed from experimental data while MFA is used to optimize a set of initial weights and biases of ANN to improve the accuracy of this artificial intelligence technique. The accuracy of the proposed expert system is validated by comparing obtained results with those from the literature. The result indicates that the MFA-ANN hybrid system can obtain a better prediction of the high-performance concrete properties. The MFA-ANN is also much faster at solving problems. Therefore, the proposed approach can provide an efficient and accurate tool to predict and design HPC.
Abstract: This paper presents a comparative environmental assessment of several different green concrete mixes for structural use. Four green concrete mixes were compared with a conventional concrete mix: recycled aggregate concrete with a cement binder, high-volume fly ash concrete with natural and recycled aggregates, and alkali activated fly ash concrete with natural aggregates. All five concrete mixes were designed and experimentally verified to have equal compressive strength and workability. An attributional life cycle assessment, based on the scenario which included construction practice, transport distances, and materials available in Serbia, was performed. When treating fly ash impacts, three allocation procedures were compared: ‘no allocation’, economic, and mass allocation, with mass allocation giving unreasonably high impacts of fly ash. Normalization and aggregation of indicators was performed and the impact of each concrete mix was expressed through a global sustainability indicator. A sensitivity analysis was also performed to evaluate the influence of possibly different carbonation resistance and long-term deformational behavior on the functional unit. In this specific case study, regardless of the choice of the functional unit, the best overall environmental performance was shown by the alkali activated fly ash concrete mix with natural aggregates and the high-volume fly ash recycled aggregate concrete mix. The worst performance was shown by the recycled aggregate concrete mix with a cement binder.
Abstract: This study presents the results of an ongoing research project conducted by the U.S. Federal Highway Administration (FHWA) on developing an intelligent approach for structural damage detection. The proposed approach is established upon the simulation of the compressed data stored in memory chips of a newly developed self-powered wireless sensor. An innovative data interpretation system integrating finite element method (FEM) and probabilistic neural network (PNN) based on Bayesian decision theory is developed for damage detection. Several features extracted from the cumulative limited static strain data are used as damage indicator variables. Another contribution of this paper is to define indicator variables that simultaneously take into account the effect of array of sensors. The performance of the proposed approach is first evaluated for the case of a simply supported beam under three-point bending. Then, the efficiency of the method is tested for the complicated case of a bridge gusset plate. The beam and gusset plate structures are analyzed as 3D FE models. The static strain data from the FE simulations for different damage scenarios is used to calibrate the sensor-specific data interpretation algorithm. The viability and repeatability of the method is demonstrated by conducting a number of simulations. Furthermore, a general scheme is presented for finding the optimal number of data acquisition points (sensors) on the structure and the associated optimal locations. An uncertainty analysis is performed through the contamination of the damage indicator features with different Gaussian noise levels.
Abstract: The study reported in this paper investigates the shear capacity of full-scale reinforced concrete beams fabricated with high volume fly ash and coarse recycled concrete aggregate (RCA). The study involved testing 24 full-scale beams. The beams were fabricated with three different longitudinal reinforcement ratios of 1.27%, 2.03%, and 2.71%. Four concrete mixtures were employed for casting the beams: conventional concrete (CC) without any fly ash or RCA as the reference; fly ash concrete with 50% of Class C fly ash replacement (FA50 beams); RCA concrete with 50% coarse RCA replacement (RCA50 beams); and sustainable concrete (SC) proportioned with 50% Class C fly ash and 50% RCA. In order to evaluate the performance of concrete in shear, the beams were cast without any stirrups in the shear zone. The test results were compared with theoretical models provided by different design codes as well as a shear data base for CC. The experimental results were also compared to analytical approaches based on fracture mechanics as well as the modified compression field theory method. On the average, the SC beams had a 10% lower shear capacity than the CC beams. The average shear capacity of the SC beams was 18% and 16% lower than those of the FA50 and RCA50 beams, respectively.
TL;DR: New hybrid artificial intelligence model called integrative support vector regression with firefly optimization algorithm (SVR-FFA) for shear strength prediction of steel fiber-reinforced concrete beam (SFRCB) is attempted.
Abstract: Recent developments on shear strength (Vf) of steel fiber-reinforced concrete beam (SFRCB) simulation have been shifted to the implementation of the computer aid advancements. The current study is attempted to explore new hybrid artificial intelligence (AI) model called integrative support vector regression with firefly optimization algorithm (SVR-FFA) for shear strength prediction of SFRCB. The developed hybrid predictive model is constructed using laboratory experimental data set gathered from the literature and belongs to the shear failure capacity. The related beam dimensional and concrete properties are utilized as input attributes to predict Vf. The proposed SVR-FFA model is validated against classical SVR model and eight empirical formulations obtained from published researches. The attained results of the proposed hybrid AI model exhibited a reliable resultant performance in terms of prediction accuracy. Based on the examined root-mean-square error (RMSE) and the correlation coefficient (R2) over the testing phase, SVR-FFA achieved (RMSE ≈ 0.25 MPa) and (R2 ≈ 0.96).
Author's H-index: 10