An integrated multiobjective harmony search with artificial neural networks anns is proposed to reduce the high computing time required for the finiteelement analysis and the increment in conflicting objectives. Application of artificial neural networks to predict. Prediction of compressive strength of recycled aggregate concrete using artificial neural networks prediction of compressive strength of recycled aggregate concrete using artificial neural networks duan, z. The purpose of using lwc is the reduction of the structures weight, as well as the reduction of thermal conductivity index.
Therefore, in recent years, artificial neural networks ann have been used for the purpose of modelling different properties of concrete, such as drying shrinkage 5, concrete durability 6, compressive strength o f normal concrete and high performance concrete 712, workability of concrete with metakaolin and fly ash. Reinforced concrete beam, cost optimization, artificial neural networks, generalized reduced gradient. Based on the simulating durability model built using trained neural networks, the optimum cement content for designing hpc in terms of durability is in the range of 450500 kgm 3. A comparison of model selection methods for compressive strength prediction of high performance concrete using neural networks. The results also revealed that the durability of concrete expressed in terms of total charge passed over a 6h period can be significantly improved by using at least 20% fly ash to replace cement. Application of artificial neural networks in static structural analysis where further examples will be shown on how ai and ann are effective solutions for providing efficiencies on construction projects from the initial concept stages, and enabling. Supplementary cementitious materials, artificial neural network, multiple regression analysis. Both concrete strength and durability should play an essential role in the concrete mix design. Artificial neural network ann as a multilayer perceptron normal feed forward network was integrated to. This paper presents machine learning algorithms based on backpropagation neural network bpnn that employs sequential feature selection sfs for predicting the compressive strength of ultrahigh performance concrete uhpc. Based on the simulated total charge passed model, built.
We should use a composition which allows us to achieve the best possible concrete performance. Highperformance concrete is a highly complex material, which makes modeling its behavior a very difficult task. Anticipating the compressive strength of hydrated lime. Artificial intelligence ai frontiers in construction.
A simple model of predicting the degree of hydration of. The data for analysis and model development was collected at 28, 56, and 91day curing periods through experiments conducted in the laboratory under standard controlled conditions. The investigations were done on 84 sifcon mixes, and specimens were cast and tested after 28 days curing. Analysis of durability of high performance concrete using artificial neural networks article in construction and building materials 232. A method of optimizing highperformance concrete mix proportioning for a given workability and compressive strength using artificial neural networks and nonlinear programming is described.
Prediction of compressive strength of high performance. Investigation of the parameters influencing progress of. Modeling slump of ready mix concrete using genetically. Guide for selecting proportions for highstrength concrete using portland cement and other cementitious materials. Icheng yeh, modeling of strength of high performance concrete using artificial neural networks, cement and. A mathematical model for the prediction of compressive strength of high performance concrete was performed using statistical analysis for the concrete data obtained from experimental work done in this study. Analysis of durability of high performance concrete using artificial neural networks free download as pdf file. Prediction of combined effects of fibers and nanosilica on the mechanical properties of selfcompacting concrete using artificial neural network. Also explore the seminar topics paper on analysis of durability of high performance concrete using artificial neural networks with abstract or synopsis, documentation on advantages and disadvantages, base paper presentation slides for. Founded in 1904 and headquartered in farmington hills, michigan, usa, the american concrete institute is a leading authority and resource worldwide for the development, dissemination, and adoption of its consensusbased standards, technical resources, educational programs, and proven expertise for individuals and organizations involved in concrete design.
Concrete compressive strength analysis using a combined classification and regression technique. Prediction of longterm strength of concrete based on artificial neural network p. Neural network analysis has been used in modeling chloride diffusion in concrete by. Prediction of compression strength of high performance concrete using artificial neural networks. Compressive strength, high performance concrete, industrial by.
Vishnuramc adepartment of civil engineering, vlb janakiammal college of engineering and technology, kovaipudur, coimbatore641 042, india. Artificial neural networks ann is a new alternative, capable of solving complex problems using an artificial reasoning system constructed with basis on the human brain. The methods of designing the composition of lwc with the assumed density and compressive strength are used most commonly. Predicting mixing power using artificial neural network. Lightweight concrete lwc is a group of cement composites of the defined physical, mechanical, and chemical performance. Predicting performance of lightweight concrete with. Selfcompactable high performance concrete in japan. Modeling of strength of highperformance concrete using.
A comparative analysis of mix proportioning for prescriptive and performancebased specifications for sustainability, verifi llc, peter c. Jalali, durability of low cost high performance fly ash concrete. In this research work, the levernberg marquardt back propagation neural network was adequately trained to understand the relationship between the 28 th day compressive strength values of hydrated lime cement concrete and their corresponding mix ratios with respect to curing age. Mariuszs research is the basis of his talk at bim show live 2019, on thursday 28 february. Song, multiaxial tensilecompressive strengths and failure criterion of plain highperformance concrete before. These computational tools were inspired by the analysis. Pdf modeling of strength of highperformance concrete. Pdf prediction of concrete strength using artificial. This paper is aimed at demonstrating the possibilities of adapting artificial neural networks ann to predict the compressive strength of highperformance concrete. Analysis of durability of high performance concrete using artificial neural networks. Prediction of combined effects of fibers and nanosilica on. Research articles challenge journal of concrete research. Zhang, predicting the shear strength of reinforced concrete beams using artificial neural networks.
Prediction of degree of hydration of concrete is very important on research of crackresistance capability and durability of the structure. Anns to durability analysis of high performance concretes. Compressive strength prediction of highstrength concrete. Anns are trained through the results of previous bridge performance evaluations.
Finally, based on the tga analysis, the effect of mwcnt on the amount of cement hydration products and on improving the quality of cement hydration products microstructures of cement paste has been modeled by using artificial neural networks ann. Performance engineered mixtures for concrete pavements in the us, peter c. Pdf prediction of compressive strength of recycled. Also explore the seminar topics paper on analysis of. High performance of stone chippings concrete with high fine content p. Rapid analysis of externally reinforced concrete beams using neural networks. Concrete performance is characterised by several features, from which the most significant are compressive strength and durability. Consequently, new modelling techniques like artificial neural networks nns are. An effort has been made to develop concrete compressive strength prediction models with the help of two emerging data mining techniques, namely, artificial neural networks anns and genetic programming gp. Fly ash fa and silica fume sf are the familiar types of pozzolanic materials and it is highly used in the producing of hpc.
Multiple regression model for compressive strength. The compressive strength predicted for different types of concrete composites using artificial neural networks have been compared with the results obtained from several other prediction techniques, like nonlinear regression, model tree, statistical analysis, fuzzy logic, anfis, genetic based algorithms and factorial design. This study aims to determine the influence of the content of water and cement, water binder ratio, and the replacement of fly ash and silica fume on the durability of high performance concrete hpc by using artificial neural networks anns. Investigation of the parameters influencing progress of concrete carbonation depth by using artificial neural networks carbonation is a deleterious concrete durability problem which may alter concrete microstructure and yield initiation of corrosion in reinforcing steel bars.
A comparative study on the compressive strength prediction models for high performance concrete containing nano silica and copper slag using regression analysis and artificial neural networks. Explore analysis of durability of high performance concrete using artificial neural networks with free download of seminar report and ppt in pdf and doc format. Dias and pooliyadda 2001 used back propagation neural networks to predict the strength and slump of ready mixed concrete and high strength concrete, in which. Many studies have tried to develop accurate and effective predictive models for hpc compressive strength, including linear regression lr, artificial neural networks anns, and support vector regression svr. This paper is aimed at adapting artificial neural networks ann to predict the strength properties of sifcon containing different minerals admixture. Two theorems are provided to determine the conditions for oscillating solutions of the model. Journal of materials in civil engineering, 2008, 20 9, pp 628633.
Read a comparative study on the compressive strength prediction models for high performance concrete containing nano silica and copper slag using regression analysis and artificial neural networks, construction and building materials on deepdyve, the largest online rental service for scholarly research with thousands of academic publications available at your fingertips. This study aims to explore the capability of artificial neural networks anns in predicting the durability of high performance concrete, which is. Data used for the study were generated experimentally. Applications of artificial neural networks for using high. Hpc supplies advance either or both strength properties concrete and long term of concrete durability 56. Nimityongskul, analysis of durability of high performance concrete using artificial neural networks, construction and building materials, vol. The multiple nonlinear regression model yielded excellent correlation coefficient for the prediction of compressive strength at different ages 3, 7, 14, 28 and 91 days. And then, based on the results, the optimum percent of mwcnt has been determined. A comparative study on the compressive strength prediction. Study on electrochemical behavior of prestressed reinforcement in simulated concrete solution. Predicting the strength properties of slurry infiltrated. Enhanced soft computing for ensemble approach to estimate. Predicting the impact of multiwalled carbon nanotubes on. Applicability of artificial neural networks to predict.
This article studied the relationship between degree of hydration and strength of concrete based on a large number of references, the results show that the compressive strength of concrete is closely related with the degree of hydration, and the. Analysis of durability of high performance concrete using. To determine the amount of cement hydration products thermogravimetric analysis was used. Multiobjective design of posttensioned concrete road. Use of artificial neural network in design of fly ash. Prediction of compressive strength of recycled aggregate concrete using artificial neural networks. To make the results applicable, the artificial neural network ann method was used to predict the compressive strength of concrete based on the evaluated concrete mix parameters and ultrasonic. Alfin ashmita, a comparative study on the compressive strength prediction models for high performance concrete containing nano silica and copper slag using regression analysis and artificial neural networks, construction and building materials, vol. This study aims to determine the influence of the content of water and cement, waterbinder ratio, and the replacement of fly ash and silica fume on the durability of high performance concrete hpc by using artificial neural networks anns. High performance concrete hpc is a complex composite material, and a model of its compressive strength must be highly nonlinear.
Our study is aimed at modeling the effect of three contributory factors, namely aspect ratio, water cement ratio and cement content on the water intakeabsorption, compressive strength, flexural strength, split tensile strength and slump properties of steel fiber reinforced concrete. The obtained experimental data are trained using ann which consists of 4 input parameters like percentage of fiber pf, aspect ratio. In this study the feasibility of using the artificial neural networks modeling in predicting the effect of mwcnt on amount of cement hydration products and improving the quality of cement hydration products microstructures of cement paste was investigated. Prediction of longterm strength of concrete based on. Her research interest includes design of reinforced concrete elements and cost. Prediction of compressive strength of concrete using.
Artificial neural network, compressive strength, durability, ingredients of concrete. Prediction of compressive strength of recycled aggregate concrete using artificial neural networks article pdf available in construction and building materials 40. Introduction highperformance concrete hpc refers to the type of concrete mixture which has adequate workability, develops high strength and possesses excellent durability properties throughout its intended service life. Analysis of durability of high performance concrete using artificial.
This paper investigates the oscillatory behavior of the solutions for a threenode neural network with discrete and distributed delays. Their results indicated that the ann models can be used to efficiently predict the chloride ions permeability across a wide range of ingredients of hpc. A database, containing 110 points and eight material constituents, was collected from the literature for the development of models using machine learning techniques. Request pdf analysis of durability of high performance concrete using artificial neural networks this study aims to determine the influence of the content of.
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