Machine learning strategies for systems with invariance properties

Substantial progress has been made in the past 25 years in the area of fiber reinforced polymer (FRP) composites as viable structural materials in bridge construction. The technologies developed to date have moved rapidly from their initial stage of concept validation, into full-scale prototype construction and field implementations. They have been used either on their own in bridge applications or being combined with other conventional structural materials such as concrete, to provide economical solutions to many critical bridge problems such as deficiency. This paper presents a state-of-the-art review of these developments on typical bridge components (i.e. decks and girders) as well as bridge systems that have been available from the early 1980s to the beginning of 2006.

New bridge systems using FRP composites and concrete: a state-of-the-art review

Learning constitutive relations using symmetric positive definite neural networks

Machine learning models are increasingly used in many engineering fields thanks to the widespread digital data, growing computing power, and advanced algorithms. The most popular machine learning model in recent years is artificial neural networks (ANN). Although many ANN models are used in the constitutive modeling of composite materials, there are still some unsolved issues that hinder the acceptance of ANN models in the practical design and analysis of composite materials and structures. Moreover, the emerging machine learning techniques are posing new opportunities and challenges in the data-based design paradigm. This paper aims to give a state-of-the-art literature review of ANN models in the constitutive modeling of composite materials, focusing on discovering unknown constitutive laws and accelerating multiscale modeling. This review focuses on the general frameworks, benefits, and challenges and opportunities of ANN models to the constitutive modeling of composite materials. Moreover, potential applications of ANN-based constitutive models in composite materials and structures are also discussed. This review is intended to initiate discussion of future research scope and new directions to enable efficient, robust, and accurate data-driven design and analysis of composite materials and structures.

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

Learning constitutive relations from indirect observations using deep neural networks

Rubber hyperelasticity is characterized by a strain energy function. The strain energy functions fall primarily into two categories: one based on statistical thermodynamics, the other based on the phenomenological approach of treating the material as a continuum. This work is focused on the phenomenological approach. To determine the constants in the strain energy function by this method, curve fitting of rubber test data is required. A review of the available strain energy functions based on the phenomenological approach shows that it requires much effort to obtain a curve fitting with good accuracy. To overcome this problem, a novel method of defining rubber strain energy function by Feedforward Backpropagation Neural Network is presented. The calculation of strain energy and its derivatives by neural network is explained in detail. The preparation of the neural network training data from rubber test data is described. Curve fitting results are given to show the effectiveness and accuracy of th...

Neural Network Based Constitutive Model for Rubber Material

A three-dimensional finite element model was built to study V-ribbed belt pulley contact mechanics. The model consists of a pulley and a segment of V-ribbed belt in contact with the pulley. A material model for the belt, including the rubber compound and the reinforcing cord is developed. Rubber is modeled as hyperelastic material. The hyperelastic strain energy function is approximated by neural network trained by rubber test data. Reinforcing cord is modeled as elastic rebar. The material model developed is implemented in the commercial finite element code ABAQUS to simulate the V-ribbed belt-pulley system. A study is then conducted to investigate the effect of belt pulley system parameters on the contact mechanics. The effects of temperature and aging on belt materials are also investigated. The information gained from the analysis can be applied to optimize V-ribbed belt and pulley design.

Finite element analysis of V-ribbed belts using neural network based hyperelastic material model

Finite Element Simulation and Experimental Validation of V-Ribbed Belt Tracking

Accessory Serpentine Belt Stress Analysis Using Hyperelastic Model

Fiber reinforced polymer (FRP) composite materials have many advantages over traditional building materials. They are becoming more and more popular in civil engineering applications. FRP pultruded tubes are easy to fabricate and can be easily assembled for different applications. In this paper, FRP tube assemblies of variable size, fiber material, tube wall thickness, and different installation are studied using finite element analysis. The deflection and stress distribution are listed for each case. When compared to experimental results, the numerical analysis shows good correlation. Data gained in this study can be used for design and optimization of FRP tube assemblies for bridge deck construction and similar applications in civil engineering.

Yuelin Shen

Papers

Neural Network Based Constitutive Model for Rubber Material

Finite element analysis of V-ribbed belts using neural network based hyperelastic material model

Finite Element Simulation and Experimental Validation of V-Ribbed Belt Tracking

Accessory Serpentine Belt Stress Analysis Using Hyperelastic Model

Finite element analysis of FRP tube assemblies for bridge decks