Multiobjective design of posttensioned concrete road. Pdf modeling of strength of highperformance concrete. Predicting the strength properties of slurry infiltrated. 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. This paper investigates the oscillatory behavior of the solutions for a threenode neural network with discrete and distributed delays. Performance engineered mixtures for concrete pavements in the us, peter c. These computational tools were inspired by the analysis. 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. Prediction of compressive strength of recycled aggregate concrete using artificial neural networks. Fly ash fa and silica fume sf are the familiar types of pozzolanic materials and it is highly used in the producing of hpc. High performance concrete hpc is a complex composite material, and a model of its compressive strength must be highly nonlinear. 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. 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. Prediction of degree of hydration of concrete is very important on research of crackresistance capability and durability of the structure.
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. 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. Data used for the study were generated experimentally. Highperformance concrete is a highly complex material, which makes modeling its behavior a very difficult task. Based on the simulated total charge passed model, built. Concrete performance is characterised by several features, from which the most significant are compressive strength and durability. Predicting mixing power using artificial neural network. 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. Artificial neural network, compressive strength, durability, ingredients of concrete. Jalali, durability of low cost high performance fly ash concrete.
The obtained experimental data are trained using ann which consists of 4 input parameters like percentage of fiber pf, aspect ratio. And then, based on the results, the optimum percent of mwcnt has been determined. 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. Prediction of compressive strength of concrete using. 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. Journal of materials in civil engineering, 2008, 20 9, pp 628633. A comparative study on the compressive strength prediction. Prediction of compressive strength of recycled aggregate concrete using artificial neural networks article pdf available in construction and building materials 40.
Nimityongskul, analysis of durability of high performance concrete using artificial neural networks, construction and building materials, vol. Analysis of durability of high performance concrete using artificial neural networks. Pdf prediction of compressive strength of recycled. Reinforced concrete beam, cost optimization, artificial neural networks, generalized reduced gradient.
Applications of artificial neural networks for using high. Selfcompactable high performance concrete in japan. 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 artificial neural network p. 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. Analysis of durability of high performance concrete using artificial neural networks free download as pdf file. 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. 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. Enhanced soft computing for ensemble approach to estimate. Artificial neural network ann as a multilayer perceptron normal feed forward network was integrated to. Two theorems are provided to determine the conditions for oscillating solutions of the model. Consequently, new modelling techniques like artificial neural networks nns are. Analysis of durability of high performance concrete using. Modeling slump of ready mix concrete using genetically.
Research articles challenge journal of concrete research. Anticipating the compressive strength of hydrated lime. 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. Application of artificial neural networks to predict. Analysis of durability of high performance concrete using artificial neural networks article in construction and building materials 232. Supplementary cementitious materials, artificial neural network, multiple regression analysis. Predicting performance of lightweight concrete with. Rapid analysis of externally reinforced concrete beams using neural networks. Prediction of longterm strength of concrete based on. The investigations were done on 84 sifcon mixes, and specimens were cast and tested after 28 days curing. Also explore the seminar topics paper on analysis of.
Her research interest includes design of reinforced concrete elements and cost. This paper is aimed at adapting artificial neural networks ann to predict the strength properties of sifcon containing different minerals admixture. Prediction of compressive strength of high performance. 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. 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. Compressive strength, high performance concrete, industrial by.
Use of artificial neural network in design of fly ash. Song, multiaxial tensilecompressive strengths and failure criterion of plain highperformance concrete before. We should use a composition which allows us to achieve the best possible concrete performance. 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. 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. 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. A comparison of model selection methods for compressive strength prediction of high performance concrete using neural networks. Vishnuramc adepartment of civil engineering, vlb janakiammal college of engineering and technology, kovaipudur, coimbatore641 042, india. High performance of stone chippings concrete with high fine content p.
Lightweight concrete lwc is a group of cement composites of the defined physical, mechanical, and chemical performance. To determine the amount of cement hydration products thermogravimetric analysis was used. Predicting the impact of multiwalled carbon nanotubes on. A database, containing 110 points and eight material constituents, was collected from the literature for the development of models using machine learning techniques. 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. Neural network analysis has been used in modeling chloride diffusion in concrete by. The purpose of using lwc is the reduction of the structures weight, as well as the reduction of thermal conductivity index. Pdf prediction of concrete strength using artificial. Hpc supplies advance either or both strength properties concrete and long term of concrete durability 56. Analysis of durability of high performance concrete using artificial.
Compressive strength prediction of highstrength concrete. Prediction of combined effects of fibers and nanosilica on. A simple model of predicting the degree of hydration of. Anns are trained through the results of previous bridge performance evaluations. Zhang, predicting the shear strength of reinforced concrete beams using artificial neural networks.
The methods of designing the composition of lwc with the assumed density and compressive strength are used most commonly. 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. 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. Guide for selecting proportions for highstrength concrete using portland cement and other cementitious materials. Both concrete strength and durability should play an essential role in the concrete mix design. Anns to durability analysis of high performance concretes. Artificial intelligence ai frontiers in construction. 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. Applicability of artificial neural networks to predict. Durability performance of concrete made with fine recycled concrete aggregates. This study aims to explore the capability of artificial neural networks anns in predicting the durability of high performance concrete, which is. Mariuszs research is the basis of his talk at bim show live 2019, on thursday 28 february. This paper is aimed at demonstrating the possibilities of adapting artificial neural networks ann to predict the compressive strength of highperformance concrete.
Modeling of strength of highperformance concrete using. Prediction of combined effects of fibers and nanosilica on the mechanical properties of selfcompacting concrete using artificial neural network. 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. 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. 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. 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. Prediction of compression strength of high performance concrete using artificial neural networks. 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. Icheng yeh, modeling of strength of high performance concrete using artificial neural networks, cement and. Request pdf analysis of durability of high performance concrete using artificial neural networks this study aims to determine the influence of the content of. 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. 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. 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. 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.