4.7 Article

Experimental assessment of the efficiency of deep learning method in predicting the mechanical properties of polymer concretes and composites

Related references

Note: Only part of the references are listed.
Article Engineering, Mechanical

Evaluation of fracture toughness properties of polymer concrete composite using deep learning approach

Mostafa Hassani Niaki et al.

Summary: This paper applies deep learning method to predict and model the fracture toughness of polymer concrete composites, considering seven important variables. The accuracy of the model is evaluated using statistical criteria and the sensitivity of each input variable is analyzed.

FATIGUE & FRACTURE OF ENGINEERING MATERIALS & STRUCTURES (2023)

Article Multidisciplinary Sciences

Machine learning enabled orthogonal camera goniometry for accurate and robust contact angle measurements

Hossein Kabir et al.

Summary: Characterization of surface wettability is important, but conventional fitting algorithms are not accurate for hydrophilic surfaces due to optical distortions. We propose an original setup with Convolutional Neural Networks (CNN) for accurate Contact Angle (CA) estimation. The algorithm is trained on 3375 ground truth images, less sensitive to drop edges, and remains stable for blurred images. Our automated orthogonal camera goniometer outperforms existing techniques with lower standard deviation and enables wettability assessment of non-spherical drops on heterogeneous surfaces.

SCIENTIFIC REPORTS (2023)

Review Engineering, Mechanical

Fracture mechanics of polymer concretes: A review

Mostafa Hassani Niaki

Summary: One of the main advantages of polymer concrete (PC) composites is their higher fracture toughness and fracture energy compared to ordinary cement concretes. This review discusses the standard test methods and parameters affecting the fracture properties of PC, including material composition and specimen parameters. It also examines the destructive effects of exposure to various environmental conditions on the fracture properties, and provides a comprehensive understanding of the fracture mechanics of polymer concretes. The review concludes by highlighting research gaps and suggesting future directions.

THEORETICAL AND APPLIED FRACTURE MECHANICS (2023)

Review Computer Science, Interdisciplinary Applications

Advances in Computational Intelligence of Polymer Composite Materials: Machine Learning Assisted Modeling, Analysis and Design

A. Sharma et al.

Summary: This article summarizes the potential applications of machine learning in the field of polymer composites and provides recommendations. Machine learning, as a predictive tool, can be used for data-driven multi-physical modeling in different areas, making it of great significance in the study of composite materials' properties.

ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING (2022)

Article Materials Science, Composites

A material-independent deep learning model to predict the tensile strength of polymer concrete

Mostafa Hassani Niaki et al.

Summary: This study predicts the tensile strength of polymer concrete composites using a deep neural network method. A database with 9 variables and 281 experimental data is prepared, and the model performance is evaluated using statistical criteria. The sensitivity of each input variable on the tensile strength is explored through a partial dependence plot analysis.

COMPOSITES COMMUNICATIONS (2022)

Article Materials Science, Composites

Mechanical behaviour and microscopic analysis of epoxy and E-glass reinforced banyan fibre composites with the application of artificial neural network and deep neural network for the automatic prediction of orientation

Suraj Shyam et al.

Summary: This study investigates the tensile and flexural behavior of epoxy-reinforced natural fiber composites using Banyan fibers. The research explores the impact of different fiber orientations on the mechanical properties of the composite, highlighting the potential for Banyan fiber composites to replace conventional materials in real-world applications.

JOURNAL OF COMPOSITE MATERIALS (2021)

Review Engineering, Multidisciplinary

A review of artificial neural networks in the constitutive modeling of composite materials

Xin Liu et al.

Summary: Machine learning models, especially artificial neural networks, are increasingly used in engineering fields. However, there are still unsolved issues hindering the acceptance of ANN models in the practical design and analysis of composite materials. The emerging machine learning techniques pose both new opportunities and challenges in the data-based design paradigm.

COMPOSITES PART B-ENGINEERING (2021)

Article Construction & Building Technology

Development of deep neural network model to predict the compressive strength of rubber concrete

Hai-Bang Ly et al.

Summary: This study successfully predicts the compressive strength of rubber concrete through an innovative DNN model development process, demonstrating high accuracy and performance. By carefully constructing a database and conducting in-depth analysis, the DNN model outperforms other neural network structures, showing potential for wide applications.

CONSTRUCTION AND BUILDING MATERIALS (2021)

Article Computer Science, Artificial Intelligence

Feature selection in image analysis: a survey

Veronica Bolon-Canedo et al.

ARTIFICIAL INTELLIGENCE REVIEW (2020)

Article Construction & Building Technology

Optimal design for epoxy polymer concrete based on mechanical properties and durability aspects

Wahid Ferdous et al.

CONSTRUCTION AND BUILDING MATERIALS (2020)

Article Engineering, Civil

Deep learning model for estimating the mechanical properties of concrete containing silica fume exposed to high temperatures

Harun Tanyildizi et al.

FRONTIERS OF STRUCTURAL AND CIVIL ENGINEERING (2020)

Article Construction & Building Technology

Effect of polymer content and temperature on mechanical properties of lightweight polymer concrete

Fatemeh Heidarnezhad et al.

CONSTRUCTION AND BUILDING MATERIALS (2020)

Article Engineering, Environmental

A novel hybrid intelligent model of support vector machines and the MultiBoost ensemble for landslide susceptibility modeling

Binh Thai Pham et al.

BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT (2019)

Article Materials Science, Multidisciplinary

Machine learning for composite materials

Chun-Teh Chen et al.

MRS COMMUNICATIONS (2019)

Article Physics, Applied

Deep neural network method for predicting the mechanical properties of composites

Sang Ye et al.

APPLIED PHYSICS LETTERS (2019)

Article Construction & Building Technology

Mechanical properties of epoxy/basalt polymer concrete: Experimental and analytical study

Mostafa Hassani Niaki et al.

STRUCTURAL CONCRETE (2018)

Article Materials Science, Multidisciplinary

Effect of basalt, silica sand and fly ash on the mechanical properties of quaternary polymer concretes

M. Hassani Niaki et al.

BULLETIN OF MATERIALS SCIENCE (2018)

Article Computer Science, Artificial Intelligence

A survey of deep neural network architectures and their applications

Weibo Liu et al.

NEUROCOMPUTING (2017)

Article Engineering, Multidisciplinary

A short review on basalt fiber reinforced polymer composites

Vivek Dhand et al.

COMPOSITES PART B-ENGINEERING (2015)

Article Construction & Building Technology

Use of basalt fibers for concrete structures

Cory High et al.

CONSTRUCTION AND BUILDING MATERIALS (2015)

Article Construction & Building Technology

A novel polymer concrete made with recycled glass aggregates, fly ash and metakaolin

Kou Shi-Cong et al.

CONSTRUCTION AND BUILDING MATERIALS (2013)

Article Materials Science, Multidisciplinary

Effect of fly ash on the behaviour of polymer concrete with different types of resin

Weena Lokuge et al.

MATERIALS & DESIGN (2013)

Article Engineering, Multidisciplinary

Optimization of the polymer concrete used for manufacturing bases for precision tool machines

Header Haddad et al.

COMPOSITES PART B-ENGINEERING (2012)

Article Materials Science, Multidisciplinary

Fabrication and mechanical properties of clay/epoxy nanocomposite and its polymer concrete

Mahmood M. Shokrieh et al.

MATERIALS & DESIGN (2012)

Article Construction & Building Technology

Effects of thermal cycles on mechanical properties of an optimized polymer concrete

M. M. Shokrieh et al.

CONSTRUCTION AND BUILDING MATERIALS (2011)

Article Construction & Building Technology

Comparison of Mechanical Properties for Polymer Concrete with Different Types of Filler

Marinela Barbuta et al.

JOURNAL OF MATERIALS IN CIVIL ENGINEERING (2010)

Article Materials Science, Multidisciplinary

Effect of Textile Waste on the Mechanical Properties of Polymer Concrete

Joao Marciano Laredo dos Reis

MATERIALS RESEARCH-IBERO-AMERICAN JOURNAL OF MATERIALS (2009)

Article Construction & Building Technology

Mechanical properties of nano-MMT reinforced polymer composite and polymer concrete

Byung-Wan Jo et al.

CONSTRUCTION AND BUILDING MATERIALS (2008)

Article Construction & Building Technology

Properties of polymer concrete using fly ash

KS Rebeiz et al.

JOURNAL OF MATERIALS IN CIVIL ENGINEERING (2004)

Article Statistics & Probability

Greedy function approximation: A gradient boosting machine

JH Friedman

ANNALS OF STATISTICS (2001)