Estimation of volumetric water content during imbibition in porous building material using real time neutron radiography and artificial neural network

Nazemi, Ehsan; Dinca, M.; Amir, Mohammad; Rokrok, B.; Dastjerdi, M.H. Choopan

doi:10.1016/j.nima.2019.06.052

Cited by 13 publications

(4 citation statements)

References 28 publications

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“…Some of the most widely adopted are Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Decision Trees (DT) based. The idea behind ANN is that it reacts the same way as the neural networks of human brain, with its abilities spanning to applications such as classification, regression, learning and generalization [ 64 ]. SVM models are utilized for classification, while its regression counterpart is the Support Vector Regression (SVR) model [ 28 ].…”

Section: Machine Learningmentioning

confidence: 99%

Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives

Angelis

Sofos

Karakasidis

2023

Arch Computat Methods Eng

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Symbolic regression (SR) is a machine learning-based regression method based on genetic programming principles that integrates techniques and processes from heterogeneous scientific fields and is capable of providing analytical equations purely from data. This remarkable characteristic diminishes the need to incorporate prior knowledge about the investigated system. SR can spot profound and elucidate ambiguous relations that can be generalizable, applicable, explainable and span over most scientific, technological, economical, and social principles. In this review, current state of the art is documented, technical and physical characteristics of SR are presented, the available programming techniques are investigated, fields of application are explored, and future perspectives are discussed.

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Section: Machine Learningmentioning

confidence: 99%

Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives

Angelis

Sofos

Karakasidis

2023

Arch Computat Methods Eng

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“…The use of neural networks in building materials to predict the characteristics of building materials, for example, for studying the effect of two types of materials including micro-silica and also calcium inosilicate minerals on the compressive strength of mortars [24], for estimating the volumetric water content in different times and positions during the water imbibition inside the porous building materials [25], for predicting performance of lightweight concrete with granulated expanded glass and ash aggregate [26], for designing the composition of cement stabilized rammed earth [27], for studying on adiabatic temperature rise reflecting hydration degree of concrete [28], for predicting the compressive strength of cement-based materials exposed to sulfate attack [29], for prediction of chloride diffusion in cement mortar [30] , etc. [31,32].…”

Section: Construction Materialsmentioning

confidence: 99%

Applying Artificial Neural Networks In Construction

Doroshenko

2020

E3S Web Conf.

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Currently, artificial neural networks (ANN) are used to solve the following complex problems: pattern recognition, speech recognition, complex forecasts and others. The main applications of ANN are decision making, pattern recognition, optimization, forecasting, data analysis. This paper presents an overview of applications of ANN in construction industry, including energy efficiency and energy consumption, structural analysis, construction materials, smart city and BIM technologies, structural design and optimization, application forecasting, construction engineering and soil mechanics.

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“…The supervised approaches rely on labelled data to train the ML algorithm effectively. This is the most common category, with wide applicability in science and technology [4][5][6][7]. Unsupervised ML involves extracting features from high-dimensional data sets without the need for pre-labelled training data.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, the need for more robust methods has emerged, and DL has come to the fore [18]. The idea of DL is based on the multi-layer perceptron, the artificial neural network (ANN) architecture that can be seen as the corresponding artificial mechanism that mimics the biological functions of the neural networks of the human brain [6].…”

Section: Introductionmentioning

confidence: 99%

Can Artificial Intelligence Accelerate Fluid Mechanics Research?

Drikakis

Sofos

2023

Fluids

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The significant growth of artificial intelligence (AI) methods in machine learning (ML) and deep learning (DL) has opened opportunities for fluid dynamics and its applications in science, engineering and medicine. Developing AI methods for fluid dynamics encompass different challenges than applications with massive data, such as the Internet of Things. For many scientific, engineering and biomedical problems, the data are not massive, which poses limitations and algorithmic challenges. This paper reviews ML and DL research for fluid dynamics, presents algorithmic challenges and discusses potential future directions.

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Estimation of volumetric water content during imbibition in porous building material using real time neutron radiography and artificial neural network

Cited by 13 publications

References 28 publications

Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives

Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives

Applying Artificial Neural Networks In Construction

Can Artificial Intelligence Accelerate Fluid Mechanics Research?

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