Prediction of the Compressive Strength of Environmentally Friendly Concrete Using Artificial Neural Network

Kulisz, Monika; KUJAWSKA, Justyna; AUBAKIROVA, Zulfiya; ZHAIRBAEVA, Gulnaz; WAROWNY, Tomasz

doi:10.35784/acs-2022-29

Cited by 3 publications

(2 citation statements)

References 31 publications

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“…In paper [11] authors used the ANN to prediction of FRP confned compressive strength of concrete and the results shows very good agreement. Similarly, in a recent study [12], an ANNs was effectively utilized to predict the compressive strength of environmentally friendly concrete containing recycled concrete aggregate, showcasing the versatility and effectiveness of ANNss models in various construction material applications. ANNs model has been also used by Malik et al [13] to predict low velocity impact against composite plates.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Buckling Behaviour of Composite Plate Element Using Artificial Neural Networks

Falkowicz,

Kulisz

2024

Adv. Sci. Technol. Res. J.

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This article presents the use of artificial neural networks (ANNs) to analysis of the composite plate elements with cut-outs which can work as a spring element. The analysis were based on results from numerical approach. ANNs models have been developed utilizing the obtained numerical data to predict the composite plate's flexuraltorsional form of buckling as natural form for different cut-outs and angels configurations. The ANNs models were trained and tested using a large dataset, and their accuracy is evaluated using various statistical measures. The developed ANNs models demonstrated high accuracy in predicting the critical force and buckling form of thin-walled plates with different cut-out and fiber angels configurations under compression. The combination of numerical analyses with ANNs models provides a practical and efficient solution for evaluating the stability behaviour of composite plates with cut-outs, which can be useful for design optimization and structural monitoring in engineering applications.

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Section: Introductionmentioning

confidence: 99%

Prediction of Buckling Behaviour of Composite Plate Element Using Artificial Neural Networks

Falkowicz,

Kulisz

2024

Adv. Sci. Technol. Res. J.

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“…Information technologies, particularly those utilizing machine learning, are becoming increasingly significant in the field of manufacturing control. Dynamic development focuses on methods like artificial neural networks, elastic net, support vector machine, LSTM, and other related techniques [15,16]. This study aims to develop a new predictive model based on machine learning to optimize the selection of RFSSW process parameters for achieving maximum shear load capacity in the joint [17,18].…”

Section: Introductionmentioning

confidence: 99%

The Use of Artificial Intelligence for Quality Assessment of Refill Friction Stir Spot Welded Thin Joints

Kubit,

Kłosowski,

Berezowski

2024

Adv. Sci. Technol. Res. J.

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This paper presents a machine learning and image segmentation based advanced quality assessment technique for thin refill friction stir spot welded (RFSSW) joints. In particular, the research focuses on developing a predictive support vector machines (SVM) model. The purpose of this model is to facilitate the selection of RFSSW process parameters in order to increase the shear load capacity of joints. In addition, an improved weld quality assessment algorithm based on optical analysis was developed. The research methodology includes specimen preparation stages, mechanical tests, and algorithmic analysis, culminating in a machine learning model trained on experimental data. The results demonstrate the effectiveness of the model in selecting welding process parameters and assessing weld quality, offering significant improvements compared to standard techniques. The optimized SVM model, employing the radial basis function (RBF) kernel, achieved a lower root mean square error of 257.9 and a high correlation coefficient of 0.95, indicating a strong linear relationship between the predicted and actual shear load capacities. This research not only proposes a novel approach to optimizing welding parameters but also facilitates automatic quality assessment, potentially revolutionizing and spreading the application of the RFSSW technique in various industries.

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Modeling the Optimal Measurement Time With a Probe on the Machine Tool Using Machine Learning Methods

JÓZWIK,

ZAWADA-MICHAŁOWSKA,

KULISZ

et al. 2024

Appl. Comput. Sci.

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This paper explores the application of various machine learning techniques to model the optimal measurement time required after machining with a probe on CNC machine tools. Specifically, the research employs four different machine learning models: Elastic Net, Neural Networks, Decision Trees, and Support Vector Machines, each chosen for their unique strengths in addressing different aspects of predictive modeling in an industrial context. The study examines as input parameters such as material type, post-processing wall thickness, cutting depth, and rotational speed over measurement time. This approach ensures that the models account for the variables that significantly affect CNC machine operations. Regression value, mean square error, root mean square error, mean absolute percentage error, and mean absolute error were used to evaluate the quality of the obtained models. As a result of the analyses, the best modeling results were obtained using neural networks. Their ability to accurately predict measurement times can significantly increase operational efficiency by optimizing schedules and reducing downtime in machining processes.

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Prediction of the Compressive Strength of Environmentally Friendly Concrete Using Artificial Neural Network

Cited by 3 publications

References 31 publications

Prediction of Buckling Behaviour of Composite Plate Element Using Artificial Neural Networks

Prediction of Buckling Behaviour of Composite Plate Element Using Artificial Neural Networks

The Use of Artificial Intelligence for Quality Assessment of Refill Friction Stir Spot Welded Thin Joints

Modeling the Optimal Measurement Time With a Probe on the Machine Tool Using Machine Learning Methods

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