An Optimized Neuro-Bee Algorithm Approach to Predict the FRP-Concrete Bond Strength of RC Beams

Kumar, Aman; Arora, Harish Chandra; Mohammed, Mazin Abed; Kumar, Krishna; Nedoma, Jan

doi:10.1109/access.2021.3140046

Cited by 30 publications

(16 citation statements)

References 82 publications

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“…The normalization process reduces the undesired feature scaling effects and provides higher computational stability. All parameters were normalized in the range from 0 to 0.9 using Equation (1) [41].…”

Section: Collection Of Lwc Datamentioning

confidence: 99%

“…It is a non-parametric prediction model for a given dataset function. GPR is defined as D = {(x i , t i ), i = 1, 2,..., N}, where x i is an input variable and t i is a target variable [41]. A distribution function, called "Gaussian process regression," can be as expressed as shown in Equation ( 8), used for the Bayesian regression.…”

Section: Gaussian Processes Regressionmentioning

confidence: 99%

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Compressive Strength Prediction of Lightweight Concrete: Machine Learning Models

et al. 2022

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Concrete is the most commonly used construction material. The physical properties of concrete vary with the type of concrete, such as high and ultra-high-strength concrete, fibre-reinforced concrete, polymer-modified concrete, and lightweight concrete. The precise prediction of the properties of concrete is a problem due to the design code, which typically requires specific characteristics. The emergence of a new category of technology has motivated researchers to develop mechanical strength prediction models using Artificial Intelligence (AI). Empirical and statistical models have been extensively used. These models require a huge amount of laboratory data and still provide inaccurate results. Sometimes, these models cannot predict the properties of concrete due to complexity in the concrete mix design and curing conditions. To conquer such issues, AI models have been introduced as another approach for predicting the compressive strength and other properties of concrete. This article discusses machine learning algorithms, such as Gaussian Progress Regression (GPR), Support Vector Machine Regression (SVMR), Ensemble Learning (EL), and optimized GPR, SVMR, and EL, to predict the compressive strength of Lightweight Concrete (LWC). The simulation approaches of these trained models indicate that AI can provide accurate prediction models without undertaking extensive laboratory trials. Each model’s applicability and performance were rigorously reviewed and assessed. The findings revealed that the optimized GPR model (R = 0.9803) used in this study had the greatest accuracy. In addition, the optimized SVMR and GPR model showed good performance, with R-values 0.9777 and 0.9740, respectively. The proposed model is economic and efficient, and can be adopted by researchers and engineers to predict the compressive strength of LWC.

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Section: Collection Of Lwc Datamentioning

confidence: 99%

Section: Gaussian Processes Regressionmentioning

confidence: 99%

Compressive Strength Prediction of Lightweight Concrete: Machine Learning Models

et al. 2022

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“…A total of 4808 randomly selected datasets were chosen by the computer itself for training, the rest of the 2060 datasets were further split into two equal parts of 1030 datasets in each group; used for testing and validation. Similarly, in another study, Kumar et al [68] divided the data into three parts. 70% of the data is used for training, 15% for testing, and the remaining 15% is used for validation in ANN and ABC-ANN.…”

Section: Filtration Of Datamentioning

confidence: 99%

Transmission Probability of SARS-CoV-2 in Office Environment Using Artificial Neural Network

Kapoor

Kumar

et al. 2022

IEEE Access

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In this paper, curve-fitting and an artificial neural network (ANN) model were developed to predict R-Event. Expected number of new infections that arise in any event occurring over a total time in any space is termed as R-Event. Real-time data for the office environment was gathered in the spring of 2022 in a naturally ventilated office room in Roorkee, India, under composite climatic conditions. To ascertain the merit of the proposed ANN and curve-fitting models, the performances of the ANN approach were compared against the curve fitting model regarding conventional statistical indicators, i.e., correlation coefficient, root mean square error, mean absolute error, Nash-Sutcliffe efficiency index, mean absolute percentage error, and a20-index. Eleven input parameters namely indoor temperature (T In ), indoor relative humidity (RH In ), area of opening (A O ), number of occupants (O), area per person (A P ), volume per person (V P ), CO 2 concentration (CO 2 ), air quality index (AQI), outer wind speed (W S ), outdoor temperature (T Out ), outdoor humidity (RH Out ) were used in this study to predict the R-Event value as an output. The primary goal of this research is to establish the link between CO 2 concentration and R-Event value; eventually providing a model for prediction purposes. In this case study, the correlation coefficient of the ANN model and curve-fitting model were 0.9992 and 0.9557, respectively. It shows the ANN model's higher accuracy than the curvefitting model in R-Event prediction. Results indicate the proposed ANN prediction performance (R=0.9992, RMSE=0.0018708, MAE=0.0006675, MAPE=0.8643816, NS=0.9984365, and a20-index=0.9984300) is reliable and highly accurate to predict the R-event for offices.

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“…Data normalization was performed to decrease undesirable feature scaling effects and increase computational stability. In this work, data was normalized in the range of 0 and 1 using equation ( 8) [29].…”

Section: Evaluation Criteriamentioning

confidence: 99%

Machine Learning-Based CO2 Prediction for Office Room: A Pilot Study

Kapoor

Kumar

et al. 2022

Wireless Communications and Mobile Computing

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Air pollution is increasing profusely in Indian cities as well as throughout the world, and it poses a major threat to climate as well as the health of all living things. Air pollution is the reason behind degraded indoor air quality (IAQ) in urban buildings. Carbon dioxide (CO2) is the main contributor to indoor pollution as humans themselves are one of the generating sources of this pollutant. The testing and monitoring of CO2 consume cost and time and require smart sensors. Thus, to solve these limitations, machine learning (ML) has been used to predict the concentration of CO2 inside an office room. This study is based on the data collected through real-time measurements of indoor CO2, number of occupants, area per person, outdoor temperature, outer wind speed, relative humidity, and air quality index used as input parameters. In this study, ten algorithms, namely, artificial neural network (ANN), support vector machine (SVM), decision tree (DT), Gaussian process regression (GPR), linear regression (LR), ensemble learning (EL), optimized GPR, optimized EL, optimized DT, and optimized SVM, were used to predict the concentration of CO2. It has been found that the optimized GPR model performs better than other selected models in terms of prediction accuracy. The result of this study indicated that the optimized GPR model can predict the concentration of CO2 with the highest prediction accuracy having R , RMSE, MAE, NS, and a20-index values of 0.98874, 4.20068 ppm, 3.35098 ppm, 0.9817, and 1, respectively. This study can be utilized by the designers, researchers, healthcare professionals, and smart city developers to analyse the indoor air quality for designing air ventilation systems and monitoring CO2 level inside the buildings.

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An Optimized Neuro-Bee Algorithm Approach to Predict the FRP-Concrete Bond Strength of RC Beams

Cited by 30 publications

References 82 publications

Compressive Strength Prediction of Lightweight Concrete: Machine Learning Models

Compressive Strength Prediction of Lightweight Concrete: Machine Learning Models

Transmission Probability of SARS-CoV-2 in Office Environment Using Artificial Neural Network

Machine Learning-Based CO2 Prediction for Office Room: A Pilot Study

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