Comparative Evaluation of Infiltration Models

Alireza Sepah Vand,Parveen Sihag,Balraj Singh,Mehran Zand 대한토목학회 2018 KSCE JOURNAL OF CIVIL ENGINEERING Vol.22 No.10

Infiltration models are very helpful in designing and evaluating surface irrigation systems. The main purpose of this study is to compare infiltration models which are used to evaluate infiltration rates of Davood Rashid, Kelat and Honam in Iran. Field infiltration tests were carried out at sixteen different locations comprising of 155 observations by use of double ring infiltrometer. The potential of four conventional infiltration models (Kostiakov, Modified Kostiakov, Novel and Philip’s models) were evaluated by least–square fitting to observed infiltration data. Three statistical comparison criteria including coefficient of correlation (C.C), coefficient of determination (R2) and root mean square error (RMSE) were used to determine the best performing infiltration models. The novel infiltration model suggests improved performance out of other three models. Further a Multi-linear Regression (MLR) equation has been developed using field infiltration data and compare with Support Vector Machine and Gaussian Process based regression with two kernels (Pearson VII and radial basis) modeling. Results suggest that Pearson VII based SVM works well than other modeling approaches in estimating the infiltration rate of soils. Sensitivity analysis concludes that the parameter, time, plays the most significant role in the estimation of infiltration rate. Comparison of results suggests that there is no significant difference between conventional and soft-computing based infiltration models.

Evaluating flexural strength of concrete with steel fibre by using machine learning techniques

Sharma, Nitisha,Thakur, Mohindra S.,Upadhya, Ankita,Sihag, Parveen Techno-Press 2021 Composite materials and engineering Vol.3 No.3

In this study, potential of three machine learning techniques i.e., M5P, Support vector machines and Gaussian processes were evaluated to find the best algorithm for the prediction of flexural strength of concrete mix with steel fibre. The study comprises the comparison of results obtained from above-said techniques for given dataset. The dataset consists of 124 observations from past research studies and this dataset is randomly divided into two subsets namely training and testing datasets with (70-30)% proportion by weight. Cement, fine aggregates, coarse aggregates, water, super plasticizer/ high-range water reducer, steel fibre, fibre length and curing days were taken as input parameters whereas flexural strength of the concrete mix was taken as the output parameter. Performance of the techniques was checked by statistic evaluation parameters. Results show that the Gaussian process technique works better than other techniques with its minimum error bandwidth. Statistical analysis shows that the Gaussian process predicts better results with higher coefficient of correlation value (0.9138) and minimum mean absolute error (1.2954) and Root mean square error value (1.9672). Sensitivity analysis proves that steel fibre is the significant parameter among other parameters to predict the flexural strength of concrete mix. According to the shape of the fibre, the mixed type performs better for this data than the hooked shape of the steel fibre, which has a higher CC of 0.9649, which shows that the shape of fibers do effect the flexural strength of the concrete. However, the intricacy of the mixed fibres needs further investigations. For future mixes, the most favorable range for the increase in flexural strength of concrete mix found to be (1-3)%.

Comparison of machine learning algorithms to evaluate strength of concrete with marble powder

Sharma, Nitisha,Upadhya, Ankita,Thakur, Mohindra S.,Sihag, Parveen Techno-Press 2022 Advances in materials research Vol.11 No.1

In this paper, functionality of soft computing algorithms such as Group method of data handling (GMDH), Random forest (RF), Random tree (RT), Linear regression (LR), M5P, and artificial neural network (ANN) have been looked out to predict the compressive strength of concrete mixed with marble powder. Assessment of result suggests that, the overall performance of ANN based model gives preferable results over the different applied algorithms for the estimate of compressive strength of concrete. The results of coefficient of correlation were maximum in ANN model (0.9139) accompanied through RT with coefficient of correlation (CC) value 0.8241 and minimum root mean square error (RMSE) value of ANN (4.5611) followed by RT with RMSE (5.4246). Similarly, other evaluating parameters like, Willmott's index and Nash-sutcliffe coefficient value of ANN was 0.9458 and 0.7502 followed by RT model (0.8763 and 0.6628). The end result showed that, for both subsets i.e., training and testing subset, ANN has the potential to estimate the compressive strength of concrete. Also, the results of sensitivity suggest that the water-cement ratio has a massive impact in estimating the compressive strength of concrete with marble powder with ANN based model in evaluation with the different parameters for this data set.

Combined effect of glass and carbon fiber in asphalt concrete mix using computing techniques

Upadhya, Ankita,Thakur, M.S.,Sharma, Nitisha,Almohammed, Fadi H.,Sihag, Parveen Techno-Press 2022 Advances in computational design Vol.7 No.3

This study investigated and predicted the Marshall stability of glass-fiber asphalt mix, carbon-fiber asphalt mix and glass-carbon-fiber asphalt (hybrid) mix by using machine learning techniques such as Artificial Neural Network (ANN), Support Vector Machine (SVM) and Random Forest(RF), The data was obtained from the experiments and the research articles. Assessment of results indicated that performance of the Artificial Neural Network (ANN) based model outperformed applied models in training and testing datasets with values of indices as; coefficient of correlation (CC) 0.8492 and 0.8234, mean absolute error (MAE) 2.0999 and 2.5408, root mean squared error (RMSE) 2.8541 and 3.3165, relative absolute error (RAE) 48.16% and 54.05%, relative squared error (RRSE) 53.14% and 57.39%, Willmott's index (WI) 0.7490 and 0.7011, Scattering index (SI) 0.4134 and 0.3702 and BIAS 0.3020 and 0.4300 for both training and testing stages respectively. The Taylor diagram also confirms that the ANN-based model outperforms the other models. Results of sensitivity analysis show that Carbon fiber has a major influence in predicting the Marshall stability. However, the carbon fiber (CF) followed by glass-carbon fiber (50GF:50CF) and the optimal combination CF + (50GF:50CF) are found to be most sensitive in predicting the Marshall stability of fibrous asphalt concrete.

A study of glass and carbon fibers in FRAC utilizing machine learning approach

Ankita Upadhya,M. S. Thakur,Nitisha Sharma,Fadi H. Almohammed,Parveen Sihag Techno-Press 2024 Advances in materials research Vol.13 No.1

Asphalt concrete (AC), is a mixture of bitumen and aggregates, which is very sensitive in the design of flexible pavement. In this study, the Marshall stability of the glass and carbon fiber bituminous concrete was predicted by using Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF), and M5P Tree machine learning algorithms. To predict the Marshall stability, nine inputs parameters i.e., Bitumen, Glass and Carbon fibers mixed in 100:0, 75:25, 50:50, 25:75, 0:100 percentage (designated as 100GF:0CF, 75GF:25CF, 50GF:50 CF, 25GF:75CF, 0GF:100CF), Bitumen grade (VG), Fiber length (FL), and Fiber diameter (FD) were utilized from the experimental and literary data. Seven statistical indices i.e., coefficient of correlation (CC), mean absolute error (MAE), root mean squared error (RMSE), relative absolute error (RAE), root relative squared error (RRSE), Scattering index (SI), and BIAS were applied to assess the effectiveness of the developed models. According to the performance evaluation results, Artificial neural network (ANN) was outperforming among other models with CC values as 0.9147 and 0.8648, MAE values as 1.3757 and 1.978, RMSE values as 1.843 and 2.6951, RAE values as 39.88 and 49.31, RRSE values as 40.62 and 50.50, SI values as 0.1379 and 0.2027 and BIAS value as -0.1 290 and -0.2357 in training and testing stage respectively. The Taylor diagram (testing stage) also confirmed that the ANN-based model outperforms the other models. Results of sensitivity analysis showed that the fiber length is the most influential in all nine input parameters whereas the fiber combination of 25GF:75CF was the most effective among all the fiber mixes in Marshall stability.