Maneek Kumar

Prediction of Compressive Strength of Concrete Using Artificial Neural Network and Genetic Programming

An effort has been made to develop concrete compressive strength prediction models with the help of two emerging data mining techniques, namely, Artificial Neural Networks (ANNs) and Genetic Programming (GP). The data for analysis and model development was collected at 28-, 56-, and 91-day curing periods through experiments conducted in the laboratory under standard controlled conditions. The developed models have also been tested on in situ concrete data taken from literature. A comparison of the prediction results obtained using both the models is presented and it can be inferred that the ANN model with the training function Levenberg-Marquardt (LM) for the prediction of concrete compressive strength is the best prediction tool.

Vehicular traffic noise prediction using soft computing approach.

A new approach for the development of vehicular traffic noise prediction models is presented. Four different soft computing methods, namely, Generalized Linear Model, Decision Trees, Random Forests and Neural Networks, have been used to develop models to predict the hourly equivalent continuous sound pressure level, Leq, at different locations in the Patiala city in India. The input variables include the traffic volume per hour, percentage of heavy vehicles and average speed of vehicles. The performance of the four models is compared on the basis of performance criteria of coefficient of determination, mean square error and accuracy. 10-fold cross validation is done to check the stability of the Random Forest model, which gave the best results. A t-test is performed to check the fit of the model with the field data.

Effect of Initial Stress Levels on Strength Parameters of Reinforced Concrete Beams Retrofitted Using Ferrocement Jackets

Ferrocement jackets and sheets can be used for local rehabilitation and retrofitting of structures due to their high tensile strength, light weight, overall economy, water tightness, and ease of application. In the present paper the effects of number of layers of wire mesh in the ferrocement jackets, type of section (balanced or under reinforced) and initial stress level on the strength of retrofitted stressed reinforced cement concrete beams have been studied. The results show that there is higher increase in the maximum load, safe load carrying capacity, ductility and toughness for beams with three layers woven wire mesh ferrocement jackets as compared to beams reinforced with two layers of woven wire mesh. It is further seen that the percentage improvement in above properties decreases with increase in initial stress level and change in type of section from under reinforced to balanced. Subsequently an analytical model, based on principles of compatibility of strain and equilibrium of forces, has been presented to predict the safe and maximum load carrying capacity. It is found that predicted safe and maximum load carrying capacities are within 5 % of the experimental one.

Comparison of Machine Learning Techniques for the Prediction of Compressive Strength of Concrete

A comparative analysis for the prediction of compressive strength of concrete at the ages of 28, 56, and 91 days has been carried out using machine learning techniques via “R” software environment. R is digging out a strong foothold in the statistical realm and is becoming an indispensable tool for researchers. The dataset has been generated under controlled laboratory conditions. Using R miner, the most widely used data mining techniques decision tree (DT) model, random forest (RF) model, and neural network (NN) model have been used and compared with the help of coefficient of determination (R2) and root-mean-square error (RMSE), and it is inferred that the NN model predicts with high accuracy for compressive strength of concrete.

Artificial Neural Networks for the Prediction of Compressive Strength of Concrete

In the paper, an artificial neural network (ANN) model is proposed to predict the compressive strength of concrete. For developing the ANN model the data bank on concrete compressive strength has been taken from the experiments conducted in the laboratory under standard conditions. The data set is of two types; in one dataset 15% cement is replaced with fly ash and the other one is without any replacement. Several training algorithms, like Quasi-Newton algorithm with Broyden, Fletcher, Goldfarb, and Shanno (BFGS) update (BFG), Fletcher-reeves conjugate gradient algorithm (CGF), Polak-Ribiere conjugate gradient algorithm (CGP),Powell-Beale conjugate gradient algorithm (CGB), Levenberg–Marquardt (LM), Resilient backpropagation (RP), Scaled conjugate gradient backpropagation (SCG), One step Secant backpropagation (OSS) along with various network architectural parameters are experimentally investigated to arrive at the most suitable model for predicting the compressive strength of concrete. It is found that Levenberg- Marquardt (LM) with tan-sigmoid activation function is best for the prediction of compressive strength of concrete. In-situ concrete compressive strength data, based on varying mix proportions, have been taken from one of the research paper present in literature for the validation of the model. It is also recommended that ANN model with the training function, Levenberg- Marquardt (LM) for the prediction of compressive strength of concrete is one of the best possible tool for the purpose.

Experimental Study on Retrofitting of Stressed RC Beams using GFRP Jackets

In the present study, the effect of fiber orientation and stress level on the strength of stressed beams retrofitted with GFRP jackets is studied. The beams were initially stressed up to 60, 75, and 90% of their safe load and then retrofitted with GFRP jackets with fibers in different orientations. The results show that the beams retrofitted using GFRP jackets with fibers at 45° to the longitudinal axis yield a higher increase in the maximum load carrying capacity, i.e., approximately 30—35% in case of under-reinforced sections and 13—17% in case of balanced sections as compared to beams retrofitted using fibers at 0° to the longitudinal axis. A considerable increase in the ductility ratio is also observed for both the fiber orientations.

Honking noise contribution to road traffic noise prediction

The implementation of Road Traffic Noise predictive Models (RTNMs) is crucial in order to be able to predict noise in urban areas strongly affected by vehicular traffic. These RTNMs can have in input a small or large number of inputs, according to the implemented function. Among these inputs, honking cannot be neglected in some specific areas in which drivers are used to horn in traffic jam or in proximity of intersections or other vehicles. In this paper, starting from a field measurement campaign in India, the authors highlight the shortcomings of standard RTNMs, that are not able to include random noisy events such as low or high pressure honking. Once the differences will be evaluated, the contribution of honking will be estimated and added to the predictions, to achieve a new model that is able to provide results in good agreement with field measurements.The implementation of Road Traffic Noise predictive Models (RTNMs) is crucial in order to be able to predict noise in urban areas strongly affected by vehicular traffic. These RTNMs can have in input a small or large number of inputs, according to the implemented function. Among these inputs, honking cannot be neglected in some specific areas in which drivers are used to horn in traffic jam or in proximity of intersections or other vehicles. In this paper, starting from a field measurement campaign in India, the authors highlight the shortcomings of standard RTNMs, that are not able to include random noisy events such as low or high pressure honking. Once the differences will be evaluated, the contribution of honking will be estimated and added to the predictions, to achieve a new model that is able to provide results in good agreement with field measurements.

Strengthening of RCC Beams in Shear by Using SBR Polymer-Modified Ferrocement Jacketing Technique

There is a common phenomenon of shear failure in RCC beams, especially in old buildings and bridges. Any possible strengthening of such beams is needed to be explored that could strengthen and make them fit for serviceable conditions. The present research has been made to determine the performance of predamaged beams strengthened with three-layered wire mesh polymer-modified ferrocement (PMF) with 15% styrene-butadiene-rubber latex (SBR) polymer. Forty-eight shear-designed and shear-deficient real-size beams were used in this experimental work. Ultimate shear load-carrying capacity of control beams was found at two different shear-span (a/d) ratios 1 and 3. The sets of remaining beams were loaded with different predetermined damage levels of 45%, 75%, and 95% of the ultimate load values and then strengthened with 20 mm thick PMF. The strengthened beams were then again tested for ultimate load-carrying capacity by conducting the shear load test at a/d = 1 and 3. As a result, the PMF-strengthened beams showed restoration and enhancement of ultimate shear load-carrying capacity by 5.90% to 12.03%. The ductility of strengthened beams was improved, and hence, the corresponding deflections were prolonged. On the other hand, the cracking pattern of PMF-strengthened beams was also improved remarkably.

Mycoherbicidal Potential of Phaeoacremonium italicum, A New Pathogen of Eichhornia crassipes Infesting Harike Wetland, India

Abstract Mycoherbicides are exclusive biotechnology products which offer a non-chemical solution to control noxious weeds on the land as well as aquatic in systems, viz a viz saving environment from hazardous impact of synthetic chemicals. The present paper highlights the mycobiota associated with Eichhornia crassipes infesting Harike wetland area of Punjab and evaluation of their pathogenic potential for futuristic application as a mycoherbicide. Of the 20 isolates tested by leaf detached assay and whole plant bioassays, only one isolate (#8 BJSSL) caused 100% damage to E. crassipes. Further, the culture filtrate of this isolate also exhibited a similar damage to the leaves in an in vitro detached leaf assay. The potential isolate was identified as Phaeoacremonium italicum using classical and modern molecular methods. This is the first report of P. italicum as a pathogen of E. crassipes and of its potential use as a biological control agent for the management of water hyacinth.

Predicting Compressive Strength of Concrete

This paper deals with development of regression models for prediction of compressive strength of concrete. The compressive strength of concrete is predicted using four variables, namely, water-binder ratio, fine aggregate-binder ratio, coarse aggregate-binder ratio and binder content. Linear regression models have been developed for variations in fly ash replacements (0 and 15 percent), Zones of aggregates (A, B and C) and curing ages (28, 56 and 91 days). An effort has also been made to modify the linear models using a two step approach. First step is to develop quadratic models (termed as full models) by identifying the suitable combinations of the four variables described above. The second step is to select the best minimal subset of the predictors in full models using Mallows Cp statistic. The proposed quadratic regression models yielded coefficient of determination R 2 ≥ 0.99 in almost every case except for concrete with Zones A and B of aggregates without fly ash for 91 days curing period and for concrete with Zone C of aggregates without fly ash for 56 days curing period. Concrete is a strong building material composed of sand, gravel, cement and water. Additional admixtures are also added sometimes to enhance certain properties of fresh or hardened concrete. In the construction industry, among the various properties of the concrete determination of compressive strength has received a large amount of attention because concrete is primarily meant to withstand compressive stresses. The compressive strength of concrete is controlled by proportioning of cement, coarse and fine aggregates, water and various admixtures. Prediction of concrete compressive strength has been an active area of research and a considerable number of studies have been carried out in this direction. Many attempts have been made to develop a suitable mathematical model which is capable of predicting compressive strength of concrete at various ages with acceptable accuracy. Multiple regression models have been used by researchers to improve accuracy of predictions of concrete compressive strength. Nipatsat and Tangtermsirikul (1) developed equations for estimating compressive strength of concrete containing fly ash with curing ages from 3 days up to 1 year. Kazberuk and Lelusz (2) proposed regression models to predict compressive strength of concrete with fly ash replacement percentages up to 30%. Namyong et al. (3) proposed the regression equations for predicting compressive strength of in-situ concrete. They have selected