Application of new information technology on concrete: an overview
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paper_title: Applications of Computers and Information Technology

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This chapter focuses on the recent developments in computers and information technology that influence the direction of research on the material science and technology of concrete. A major advance in concrete science and technology has already resulted from the development, application, and integration of computer-based simulation models, databases, and artificial intelligence decision-support systems. These advances in computers and communications have enormous implications in concrete science and technology in areas such as (1) prediction of concrete performance through fundamental computational models resulting in more reliable service life designs of concrete; (2) collaboration in research and in problem-solving efforts in the concrete field through sharing of data, information, and knowledge among geographically dispersed colleagues, research organizations, manufacturing companies, and contractors; (3) development of computer-integrated knowledge systems that have the potential for representing virtually all scientific and engineering knowledge of concrete and making the knowledge readily available to those who need it. A database system is defined as a computerized recordkeeping system, while a database is a collection of computerized data files with persistent data.
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paper_title: Computing and Information Technology (IT) Research in Civil Engineering—Self-Fulfilling or Industry Transforming?

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Numerous exciting research projects on various aspects of computing and information technology IT in civil engineering have been undertaken over the last twenty years. Note that Computing and IT are used broadly to cover both software and hardwarerelated projects. The outcomes of many of these projects have been widely published in the Journal of Computing in Civil Engineering. Many of these projects have resulted in proof-ofconcept prototype IT systems that tend to have very little or no use beyond the end of the project. In light of this, there are some critical questions that must be asked with regard to both the quality of the research undertaken and the publications associated with them: • How many of these computing and IT research projects have constituted stepping stones for other researchers in the domain? • How many of the resulting prototype systems have evolved into successful commercial systems over time? • How many of the outcomes of these projects have been adopted by the industry, especially at the field/jobsite level? • How many of these have moved computing and IT in civil engineering forward in a meaningful way? These questions challenge the long-term utility of proof-ofconcept prototype systems, the underlying research and the associated publications. It may be rightly argued that research prototypes do not need to address all the above issues; but it could also be argued that those that do not address any of these questions are of dubious value. This may be a harsh conclusion, but it is one that funding agencies are increasingly being required to explore. The first question relates to the scientific merit of the research undertaken. Surely, if the computing/IT model, prototype or system developed is sound and underpinned by excellent and rigorous research, then other researchers will build on it without having to reinvent the wheel. We recognize that there are legal and other considerations that limit the extent to which research results are exploited by other researchers. However, there are very few instances of researchers being denied access to results and systems on those bases. The lack of use of previous research may be because the prototypes and other outputs are not as robust as we claim in our publications, and often keel over under close examination. It could also be that we are not as confident about
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paper_title: European Research on Intelligent Computing in Civil Engineering

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Over the past decade there have been strengthening links between the ASCE research community in civil engineering information technology ~IT! and the European Group for Intelligent Computing in Engineering ~EG-ICE!. The links have resulted in both communities inviting personnel to their workshops and, as one would expect, there is now a growing trend toward collaborative research. This has to be welcomed and can only be beneficial. Also, as a research community working in IT with its inherent usage of networking, we should be at the forefront of international collaboration. As a part of this ongoing relationship, ASCE invited EG-ICE to submit a series of papers for a special issue. A total of 10
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paper_title: Modelling creep and shrinkage of concrete by means of effective stresses

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A novel model of mechanical performance of concrete at early ages and beyond, and in particular, evolution of its strength properties (aging) and deformations (shrinkage and creep strains), described in terms of effective stress is briefly presented. This model reproduces such␣ phenomena known from experiments like drying creep or some additional strains, as compared to pure shrinkage, which appear during autogenous deformations of a maturing, sealed concrete sample. Creep is described by means of the modified microprestress-solidification theory with some modifications to take into account the effects of temperature and relative humidity on concrete aging. Shrinkage strains are modelled by using effective stresses giving a good agreement with experimental data also for low values of relative humidity. Results of four numerical examples based on the real experimental tests are solved to validate the model. They demonstrate its possibilities to analyze both autogenous deformations in maturing concrete, and creep and shrinkage phenomena, including drying creep, in concrete elements of different age, sealed or drying, exposed to external load or without any load.
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paper_title: Principles underlying the steam curing of concrete at atmospheric pressure

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Summary: This paper summarizes the conclulions drawn from experimental work carried out at the Cement and Concrete Association Research Station and elsewhere concerning the principles underlying steam curing at atmospheric pressure. It is shown that if the temperature gradient of the concrete after the time of mixing does not exceed a certain value, the concrete gains strength during and after treatment in relation to its “maturity” (reckoned in temperature-time) approximately in accordance with the same law as holds for normally cured concrete. Concrete which is raised in temperature more rapidly is shown not to obey this law, and to be adversely affected in strength at a later age. The use of the too rapid early temperature rises often employed in practice introduces various opposing variables which suggest optimum temperatures, delayed treatments and other arrangements of the curing cycle; such expediencies are unnecessary, however, if a slow initial temperature gradient is used. The paper contains tab...
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paper_title: Employing Inductive Databases in Concrete Applications

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In this paper we present the application of the inductive database approach to two practical analytical case studies: Web usage mining in Web logs and financial data. As far as concerns the Web domain, we have considered the enriched XML Web logs, that we call conceptual logs, produced by specific Web applications. These ones have been built by using a conceptual model, namely WebML, and its accompanying CASE tool, WebRatio. The Web conceptual logs integrate the usual information about user requests with meta-data concerning the Web site structure. As far as concerns the analysis of financial data, we have considered the trade stock exchange index Dow Jones and studied its component stocks from 1997 to 2002 using the so-called technical analysis. Technical analysis consists in the identification of the relevant (graphical) patterns that occur in the plot of evolution of a stock quote as time proceeds, often adopting different time granularities. On the plots the correlations between distinctive variables of the stocks quote are pointed out, such as the quote trend, the percentage variation and the volume of the stocks exchanged. In particular we adopted candle-sticks, a figurative pattern representing in a condensed diagram the evolution of the stock quotes in a daily stock exchange. In technical analysis, candle-sticks have been frequently used by practitioners to predict the trend of the stocks quotes in the market. ::: ::: We then apply a data mining language, namely MINE RULE, to these data in order to identify different types of patterns. As far as Web data is concerned, recurrent navigation paths, page contents most frequently visited, and anomalies such as intrusion attempts or a harmful usage of the resources are among the most important patterns. As far as concerns the financial domain, we searched for the sets of stocks which frequently exhibited a positive daily exchange in the same days, so as to constitute a collection of quotes for the constitution of the customers' portfolio, or the candle-sticks frequently associated to certain stocks, or finally the most similar stocks, in the sense that they mostly presented in the same dates the same typology of candle-stick, that is the same behaviour in time. ::: ::: The purpose of this paper is to show that the exploitation of the nuggets of information embedded in the data and of the specialised mining constructs provided by the query languages, enables the rapid customization of the mining procedures following to the users' need. Given our experience, we also claim that the use of queries in advanced languages, as opposed to ad-hoc heuristics, eases the specification and the discovery of a large spectrum of patterns.
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paper_title: Principles of Distributed Database Systems

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This third edition of a classic textbook can be used to teach at the senior undergraduate and graduate levels. The material concentrates on fundamental theories as well as techniques and algorithms. The advent of the Internet and the World Wide Web, and, more recently, the emergence of cloud computing and streaming data applications, has forced a renewal of interest in distributed and parallel data management, while, at the same time, requiring a rethinking of some of the traditional techniques. This book covers the breadth and depth of this re-emerging field. The coverage consists of two parts. The first part discusses the fundamental principles of distributed data management and includes distribution design, data integration, distributed query processing and optimization, distributed transaction management, and replication. The second part focuses on more advanced topics and includes discussion of parallel database systems, distributed object management, peer-to-peer data management, web data management, data stream systems, and cloud computing. New in this Edition: New chapters, covering database replication, database integration, multidatabase query processing, peer-to-peer data management, and web data management. Coverage of emerging topics such as data streams and cloud computing Extensive revisions and updates based on years of class testing and feedback Ancillary teaching materials are available.
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paper_title: Prediction of Concrete Strength Using Neural-Expert System

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Over the years, many methods have been developed to predict the concrete strength. In recent years, artificial neural networks (ANNs) have been applied to many civil engineering problems with some degree of success. In the present paper, ANN is used as an attempt to obtain more accurate concrete strength prediction based on parameters like concrete mix design, size and shape of specimen, curing technique and period, environmental conditions, etc. A total of 864 concrete specimens were cast for compressive strength measurement and verification through the ANN model. The back propagation-learning algorithm is employed to train the network for extracting knowledge from training examples. The predicted strengths found by employing ANN are compared with the actual values. The results indicate that ANN is a useful technique for predicting the concrete strength. Further, an effort to build an expert system for the problem is described in this paper. To overcome the bottleneck of intricate knowledge acquisition, an expert system is used as a mechanism to transfer engineering experience into usable knowledge through rule-based knowledge representation techniques.
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paper_title: An expert system for mix design of high performance concrete

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This paper describes a prototype expert system called HPCMIX that provides proportion of trial mix of High Performance Concrete (HPC) and recommendations on mix adjustment. The knowledge was acquired from various textual sources and human experts. The system was developed using hybrid knowledge representation technique. It is capable of selecting proportions of mixing water, cement, supplementary cementitious materials, aggregates and superplasticizer, considering the effects of air content as well as water contributed by superplasticizer and moisture conditions of aggregates. Similar to most expert systems, this system has explanation facilities, can be incrementally expanded, and has an easy to understand knowledge base. The system was tested using a sample project. The system's selection of mix proportions and recommendations regarding mix adjustment were compared favourably with those of experts. The system is user-friendly and can be used as an educational tool.
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paper_title: Quality Control Expert System of Pump Concrete Based onTechnology of Data Mining

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This paper introduced the whole process that how to apply the technology of data mining into the design of the system of Pump Concrete Expert System. Owing to the problems existing in the technologies and the environment of construction, the application of data mining was imperative under the situation. Firstly the technology of data mining was analyzed and then the statistic analysis and decision trees were used successfully in the system based of the actual requirement. The model of data mining and the whole framework founded on the quality control were provided at the same time. The system was triumphantly applied into the factual production, which was worth mentioning.
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paper_title: Appraisal of long-term effects of fly ash and silica fume on compressive strength of concrete by neural networks

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Abstract This study focuses on studying the effects of fly ash and silica fume replacement content on the strength of concrete cured for a long-term period of time by neural networks (NNs). Applicability of NNs to evaluate the effects of FA and SF for a long period of time is investigated. The investigations covered concrete mixes at different water cementitious materials ratio, which contained low and high volumes of FA, and with or without the additional small amount of SF. 24 different mixes with 144 different samples were gathered form the literature for this purpose. These samples consist concretes that were cured for 3, 7, 28, 56 and 180 days. A NN model is constructed trained and tested using these data. The data used in the NN model are arranged in a format of eight input parameters that cover the fly ash replacement ratio (FA), silica fume replacement ratio (SF), total cementitious material (TCM), fine aggregate (ssa), coarse aggregate (ca), water content (W), high rate water reducing agent (HRWRA) and age of samples (AS) and an output parameter which is compressive strength of concrete ( f c ). A NN program was devised in MATLAB and the NN model was constructed in this program. The results showed that NNs have strong potential as a feasible tool for evaluation of the effect of cementitious material on the compressive strength of concrete. It was found that FA content contributed little at early ages but much at later ages to the strength of concrete. It can also be concluded that the enhancement effect of low content of SF on compressive strength was not significant.
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paper_title: Neural networks surrogate models for simulating payment risk in pavement construction

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A common provision in quality control/quality assurance (QC/QA) highway pavement construction contracts is the adjustment of the pay that a contractor receives on the basis of the quality of the construction. It is important to both the contractor and the contracting agency to examine the amount of pay that the contractor can expect to receive for a given level of construction quality. Previous studies have shown that computer simulations can provide a better, more de- tailed examination of the pay schedule than is possible by simply determining the expected pay. In particular, the simula- tion process can provide an indication of the variability of pay at various quality levels and can identify the factors most responsible for pay adjustments. Stochastic simulation models are very useful in estimating and analyzing payment risk in highway pavement construction. However, such models are constrained by their computational requirements, and it is of- ten necessary to couple them with simpler models to speed up the process of decision-making. This paper investigates the use of Neural Networks (NN) to build surrogate models for a pavement construction payment-risk prediction model. The results show that although the average error associated with the NN predictions are acceptable; in some particular cases the errors may be unacceptably high.
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paper_title: Neural network prediction of concrete degradation by sulphuric acid attack

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Microbiologically induced corrosion is a leading cause of the deterioration of wastewater collection, transmission and treatment infrastructure around the world. This paper examines the feasibility of using artificial neural networks (ANNs) to predict the compressive strength of concrete and its degradation under exposure to sulphuric acid of various concentrations. A database incorporating 78 concrete mixtures performed by the authors was developed to train and test the ANN models. Data were arranged in a patterned format in such a manner that each pattern contains input variables (concrete mixture parameters) and the corresponding output vector (weight loss of concrete by H2SO4 attack and compressive strength at different ages). Results show that the ANN model I successfully predicted the weight loss of concrete specimens subjected to sulphuric acid attack, not only for mixtures used in the training process, but also for new mixtures unfamiliar to the ANN model designed within the practical range of the...
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paper_title: Using neural networks to predict workability of concrete incorporating metakaolin and fly ash

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This paper details the development of neural network models that provide effective predictive capability in respect of the workability of concrete incorporating metakaolin (MK) and fly ash (FA). The predictions produced reflect the effect of graduated variations in pozzolanic replacement in Portland cement (PC) of up to 15% MK and 40% FA. The results show that the models are reliable and accurate and illustrate how neural networks can be used to beneficially predict the workability parameters of slump, compacting factor and Vebe time across a wide range of PC-FA-MK compositions.
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paper_title: Prediction of elastic modulus of normal and high strength concrete by artificial neural networks

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Abstract In the present paper, application of artificial neural networks (ANNs) to predict elastic modulus of both normal and high strength concrete is investigated. The paper aims to show a possible applicability of ANN to predict the elastic modulus of both high and normal strength concrete. An ANN model is built, trained and tested using the available test data gathered from the literature. The ANN model is found to predict elastic modulus of concrete well within the ranges of the input parameters considered. The average value of the experimental elastic modulus to the predicted elastic modulus ratio is found to be 1.00. The elastic modulus results predicted by ANN are also compared to those obtained using empirical results of the buildings codes and various models. These comparisons show that ANNs have strong potential as a feasible tool for predicting elastic modulus of both normal and high strength within the range of input parameters considered.
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paper_title: Predicting the compressive strength of ground granulated blast furnace slag concrete using artificial neural network

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In this study, an artificial neural networks study was carried out to predict the compressive strength of ground granulated blast furnace slag concrete. A data set of a laboratory work, in which a total of 45 concretes were produced, was utilized in the ANNs study. The concrete mixture parameters were three different water-cement ratios (0.3, 0.4, and 0.5), three different cement dosages (350, 400, and 450kg/m^3) and four partial slag replacement ratios (20%, 40%, 60%, and 80%). Compressive strengths of moist cured specimens (22+/-2^oC) were measured at 3, 7, 28, 90, and 360 days. ANN model is constructed, trained and tested using these data. The data used in the ANN model are arranged in a format of six input parameters that cover the cement, ground granulated blast furnace slag, water, hyperplasticizer, aggregate and age of samples and, an output parameter which is compressive strength of concrete. The results showed that ANN can be an alternative approach for the predicting the compressive strength of ground granulated blast furnace slag concrete using concrete ingredients as input parameters.
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paper_title: Analysis of durability of high performance concrete using artificial neural networks

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Abstract 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). To achieve this, an ANNs model is developed to predict the durability of high performance concrete which is expressed in terms of chloride ions permeability in accordance with ASTM C1202-97 or AASHTO T277. The model is developed, trained and tested by using 86 data sets from experiments as well as previous researches. To verify the model, regression equations are carried out and compared with the trained neural network. The results indicate that the developed model is reliable and accurate. 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 450–500 kg/m3. The results also revealed that the durability of concrete expressed in terms of total charge passed over a 6-h period can be significantly improved by using at least 20% fly ash to replace cement. Furthermore, it can be concluded that increasing silica fume results in reducing the chloride ions penetrability to a higher degree than fly ash. This study also illustrates how ANNs can be used to beneficially predict durability in terms of chloride ions permeability across a wide range of mix proportion parameters of HPC.
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paper_title: Modeling of hydration reactions using neural networks to predict the average properties of cement paste

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This paper presents a hydration model that describes the evolution of cement paste microstructure as a function of the changing composition of the hydration products. The hydration model extends an earlier version by considering the reduction in the hydration rate that occurs due to the reduction of free water and the reduction of the interfacial area of contact between the free water and the hydration products. The BP Neural Network method is used to determine the coefficients of the model. Using the proposed model, this paper predicts the following properties of hardening cement paste: the degree of hydration, the rate of heat evolution, the relative humidity and the total porosity. The agreement between simulation and experimental results proves that the new model is quite effective and potentially useful as a component within larger-scale models designed to predict the performance of concrete structures.
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paper_title: A new way of prediction elastic modulus of normal and high strength concrete : fuzzy logic

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In this paper, the theory of fuzzy sets, especially fuzzy modeling is discussed to determine elastic modulus of both normal and high-strength concrete. A fuzzy logic algorithm has been devised for estimating elastic modulus from compressive strength of concrete. The main advantage of fuzzy models is their ability to describe knowledge in a descriptive human-like manner in the form of simple rules using linguistic variables only. On the other hand, many parameters will be effected and elastic modulus can be taken into account easily by using the proposed fuzzy model.
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paper_title: Neural Networks in Civil Engineering. I: Principles and Understanding

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This is the first of two papers providing a discourse on the understanding, usage, and potential for application of artificial neural networks within civil engineering. The present paper develops an understanding of how these devices operate and explains the main issues concerning their use. A simple structural‐analysis problem is solved using the most popular form of neural‐networking system—a feedforward network trained using a supervised scheme. A graphical interpretation of the way in which neural networks operate is first presented. This is followed by discussions of the primary concepts and issues concerning their use, including factors affecting their ability to learn and generalize, the selection of an appropriate set of training patterns, theoretical limitations of alternative network configurations, and network validation. The second paper demonstrates the ways in which different types of civil engineering problems can be tackled using neural networks. The objective of the two papers is to ensur...
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paper_title: Prediction of compressive strength of SCC and HPC with high volume fly ash using ANN

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An artificial neural network (ANN) is presented to predict a 28-day compressive strength of a normal and high strength self compacting concrete (SCC) and high performance concrete (HPC) with high volume fly ash. The ANN is trained by the data available in literature on normal volume fly ash because data on SCC with high volume fly ash is not available in sufficient quantity. Further, while predicting the strength of HPC the same data meant for SCC has been used to train in order to economise on computational effort. ::: The compressive strengths of SCC and HPC as well as slump flow of SCC estimated by the proposed neural network are validated by experimental results.
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paper_title: A Concrete Mix Proportion Design Algorithm Based on Artificial Neural Networks

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Abstract The concepts of five parameters of nominal water–cement ratio, equivalent water–cement ratio, average paste thickness, fly ash–binder ratio, grain volume fraction of fine aggregates and Modified Tourfar's Model were introduced. It was verified that the five parameters and the mix proportion of concrete can be transformed each other when Modified Tourfar's Model is applied. The behaviors (strength, slump, et al.) of concrete primarily determined by the mix proportion of concrete now depend on the five parameters. The prediction models of strength and slump of concrete were built based on artificial neural networks (ANNs). The calculation models of average paste thickness and equivalent water–cement ratio can be obtained by the reversal deduction of the two prediction models, respectively. A concrete mix proportion design algorithm based on a way from aggregates to paste, a least paste content, Modified Tourfar's Model and ANNs was proposed. The proposed concrete mix proportion design algorithm is expected to reduce the number of trial and error, save cost, laborers and time. The concrete designed by the proposed algorithm is expected to have lower cement and water contents, higher durability, better economical and ecological effects.
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paper_title: Prediction of compressive strength of concretes containing metakaolin and silica fume by artificial neural networks

paper_content:

Neural networks have recently been widely used to model some of the human activities in many areas of civil engineering applications. In the present paper, the models in artificial neural networks (ANN) for predicting compressive strength of concretes containing metakaolin and silica fume have been developed at the age of 1, 3, 7, 28, 56, 90 and 180days. For purpose of building these models, training and testing using the available experimental results for 195 specimens produced with 33 different mixture proportions were gathered from the technical literature. The data used in the multilayer feed forward neural networks models are arranged in a format of eight input parameters that cover the age of specimen, cement, metakaolin (MK), silica fume (SF), water, sand, aggregate and superplasticizer. According to these input parameters, in the multilayer feed forward neural networks models are predicted the compressive strength values of concretes containing metakaolin and silica fume. The training and testing results in the neural network models have shown that neural networks have strong potential for predicting 1, 3, 7, 28, 56, 90 and 180days compressive strength values of concretes containing metakaolin and silica fume.
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paper_title: Prediction of Cement Degree of Hydration Using Artificial Neural Networks

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This paper presents the development of a computer model for the prediction of cement degree of hydration. The model is established by incorporating large experimental data sets using the neural networks (NNs) technology. NNs are computational paradigms, primarily based on the structural formation and the knowledge processing faculties of the human brain. Initially, the degree of hydration was estimated in the laboratory by preparing portland cement paste with the water-cement ratio ranging from 0.2 to 0.6, curing times from 0.25 days to 90 days, and subjected to curing temperatures from 3 deg C (37 deg F) to 43 deg C (109 deg F). A total of 390 specimens were tested, producing 195 data points divided into five sets. The networks were trained using data in Sets 1, 2, and 3. Once the NNs were fully trained, verification of the performance was carried out using Sets 4 and 5 of the experimental data. Results indicate that the NNs are very efficient in predicting concrete degree of hydration with great accuracy using minimal processing of data.
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paper_title: Exploring Concrete Slump Model Using Artificial Neural Networks

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Fly ash and slag concrete (FSC) is a highly complex material whose behavior is difficult to model. This paper describes a method of modeling slump of FSC using artificial neural networks. The slump is a function of the content of all concrete ingredients, including cement, fly ash, blast furnace slag, water, superplasticizer, and coarse and fine aggregate. The model built was examined with response trace plots to explore the slump behavior of FSC. This study led to the conclusion that response trace plots can be used to explore the complex nonlinear relationship between concrete components and concrete slump.
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paper_title: A new approach to determination of compressive strength of fly ash concrete using fuzzy logic

paper_content:

In this study, determination of effect of fly ash (FA) content on the compressive strength of concrete depending on water/cement ratio and concrete age was investigated by use of fuzzy logic (FL) approach. In the approach of modelling with FL, compressive strength values of various sample of concrete that produced by replacement of cement by F class of FA by ratio of 0 (control), 10%, 20% and 30% were used. Water/binder ratio of these concrete samples was varied between 0.27-0.60 in six different values. Experimental compressive strength values of the concrete specimens at 3, 7, 28, 90, 180 and 365 days compared with FL values obtained using the fuzzy sets. Optimum FA content and water/binder ratio for the best compressive strength for early age and hardened concrete can be obtained with FL.
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paper_title: Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic

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Abstract In this study, artificial neural networks and fuzzy logic models for predicting the 7, 28 and 90 days compressive strength of concretes containing high-lime and low-lime fly ashes have been developed. For purpose of constructing these models, 52 different mixes with 180 specimens were gathered from the literature. The data used in the artificial neural networks and fuzzy logic models are arranged in a format of nine input parameters that cover the day, Portland cement, water, sand, crushed stone I (4–8 mm), crushed stone II (8–16 mm), high range water reducing agent replacement ratio, fly ash replacement ratio and CaO, and an output parameter which is compressive strength of concrete. In the models of the training and testing results have shown that artificial neural networks and fuzzy logic systems have strong potential for predicting 7, 28 and 90 days compressive strength of concretes containing fly ash.
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paper_title: Prediction of mechanical properties of recycled aggregate concretes containing silica fume using artificial neural networks and fuzzy logic

paper_content:

Artificial neural networks and fuzzy logic have been widely used in many areas in civil engineering applications. In this study, the models in artificial neural networks and fuzzy logic systems for predicting compressive and splitting tensile strengths of recycled aggregate concretes containing silica fume have been developed at the age of 3, 7, 14, 28, 56 and 90 days. For purpose of constructing these models, experimental results for 210 specimens produced with 35 different mixture proportions were gathered from the literature. The data used in the artificial neural networks and fuzzy logic models are arranged in a format of eight input parameters that cover the age of specimen, cement, water, sand, aggregate, recycled aggregate, superplasticizer and silica fume. According to these input, in the artificial neural networks and fuzzy logic models are predicted the compressive and splitting tensile strengths values from mechanical properties of recycled aggregate concretes containing silica fume. In the models of the training and testing results have shown that artificial neural networks and fuzzy logic systems have strong potential for predicting 3, 7, 14, 28, 56 and 90 days compressive and splitting tensile strengths values of recycled aggregate concretes containing silica fume.
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paper_title: Contractor prequalification model using fuzzy sets

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Abstract Contractor prequalification makes it possible to admit for tendering only competent contractors. The undertaken decisions demand taking into consideration many criteria, including among others, experience and financial standing of the candidates. It is often difficult to be quantified. The objectives of the construction owner in a given project are also meaningful. All these factors cause difficulties in working out a mathematical prequalification model. In the paper a model based on fuzzy sets theory is proposed. It takes into consideration both different criteria, objectives and evaluations of numerous decision ‐ makers. To illustrate the model operation a simple numerical example is presented.
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paper_title: A new way of prediction elastic modulus of normal and high strength concrete : fuzzy logic

paper_content:

In this paper, the theory of fuzzy sets, especially fuzzy modeling is discussed to determine elastic modulus of both normal and high-strength concrete. A fuzzy logic algorithm has been devised for estimating elastic modulus from compressive strength of concrete. The main advantage of fuzzy models is their ability to describe knowledge in a descriptive human-like manner in the form of simple rules using linguistic variables only. On the other hand, many parameters will be effected and elastic modulus can be taken into account easily by using the proposed fuzzy model.
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paper_title: Fuzzy probability analysis of the fatigue resistance of steel structural members under bending

paper_content:

Abstract The paper is aimed at the fuzzy probabilistic analysis of fatigue resistance due to uncertainty of input parameters. The fatigue resistance of the steel member is evaluated by linear fracture mechanics as the number of cycles leading to the propagation of initial cracks into a critical crack resulting in brittle fracture. When the histogram of stress range is known, the fatigue resistance is a random variable. In the event that the histogram is unknown or was acquired from a small number of experiments, another source of uncertainty is of an epistemic origin. Two basic approaches, which make provision for uncertainty of input histograms of stress range, are illustrated in the paper. Uncertainty of histograms of stress range is taken into account by the variability of equivalent stress range in the first stochastic approach. Input histograms as considered as members of a fuzzy set in the second approach.
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paper_title: GENETIC ALGORITHM IN MIX PROPORTIONING OF HIGH-PERFORMANCE CONCRETE

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High-performance concrete is defined as concrete that meets special combinations of performance and uniformity requirements that cannot always be achieved routinely using conventional constituents and normal mixing, placing, and curing practices. Ever since the term high-performance concrete was introduced into the industry, it has been widely used in large-scale concrete construction that demands high strength, high flowability, and high durability. To obtain such performances that cannot be obtained from conventional concrete and by the current method, a large number of trial mixes are required to select the desired combination of materials that meets special performance. Therefore, in this paper, using genetic algorithm that is a global optimization technique modeled on biological evolutionary process-natural selection and natural genetics-and can be used to find a near optimal solution to a problem that may have many solutions, the new design method for high-performance concrete mixtures is suggested to reduce the number of trial mixtures with desired properties in the field test. Experimental and analytic investigations were carried out to develop the design method for high-performance concrete mixtures and to verify the proposed mix design.
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paper_title: Prediction of Concrete Strength Using Neural-Expert System

paper_content:

Over the years, many methods have been developed to predict the concrete strength. In recent years, artificial neural networks (ANNs) have been applied to many civil engineering problems with some degree of success. In the present paper, ANN is used as an attempt to obtain more accurate concrete strength prediction based on parameters like concrete mix design, size and shape of specimen, curing technique and period, environmental conditions, etc. A total of 864 concrete specimens were cast for compressive strength measurement and verification through the ANN model. The back propagation-learning algorithm is employed to train the network for extracting knowledge from training examples. The predicted strengths found by employing ANN are compared with the actual values. The results indicate that ANN is a useful technique for predicting the concrete strength. Further, an effort to build an expert system for the problem is described in this paper. To overcome the bottleneck of intricate knowledge acquisition, an expert system is used as a mechanism to transfer engineering experience into usable knowledge through rule-based knowledge representation techniques.
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paper_title: Computer modeling of the replacement of " coarse " cement particles by inert fillers in low w / c ratio concretes Hydration and strength

paper_content:

Abstract In concretes with water-to-cement (w/c) ratios below about 0.38, a portion of the cement particles will always remain unhydrated due to space limitations within the material. Thus, in many of the high-performance concretes currently being produced, cement clinker is in effect being wasted. This communication examines the possibility of replacing the coarser fraction of a cement powder by an inert filler, to conserve cement without sacrificing material performance. Using the NIST CEMHYD3D cement hydration model, it is demonstrated that for “initial” w/c ratios of 0.25 and 0.30, a portion of the coarser cement particles can be replaced by inert fillers with little projected loss in compressive strength development. Of course, the optimal replacement fraction depends on the initial w/c ratio, suggesting that blended portland/inert filler cements need to be produced with the end concrete mixture proportions in mind. This further implies that a cement/inert mixture of specific proportions will only perform optimally in a limited range of concrete mixture proportions.
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paper_title: Influence of silica fume on diffusivity in cement-based materials. II. Multi-scale modeling of concrete diffusivity

paper_content:

Based on a set of multi-scale computer models, an equation is developed for predicting the chloride ion diffusivity of concrete as a function of water-to-cement (w/c) ratio, silica fume addition, degree of hydration and aggregate volume fraction. Silica fume influences concrete diffusivity in several ways: (1) densifying the microstructure of the interfacial transition zone (ITZ) regions, (2) reducing the overall (bulk and ITZ) capillary porosity for a fixed degree of cement hydration, and (3) producing a pozzolanic C-S-H gel with a relative diffusivity about 25 times less than that of the C-S-H gel produced from conventional cement hydration. According to the equation and in agreement with results from the literature, silica fume is most efficient for reducing diffusivity in lower w/c ratio concretes (w/c <0.4). In these systems, for moderate additions of silica fume (e.g., 10%), the reduction in concrete diffusivity may be a factor of 15 or more, which may substantially increase the service life of steel-reinforced concrete exposed in a severe corrosion environment.
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paper_title: An argument for using coarse cements in high-performance concretes

paper_content:

Abstract The NIST cement hydration microstructural model and experimental studies are used to investigate the effects of particle size distribution and water-to-cement ratio on hydration kinetics. Cement hydration is limited by the availability of water at the cement particle surfaces. Because a reaction layer forms around each cement particle during hydration, this availability is controlled by two factors: the overall water-to-cement ratio and the particle size distribution of the cement. As the water-to-cement ratio is decreased, the effects of particle size distribution on ultimate degree of hydration become less significant. Thus, in the new generation, high-performance concretes, more coarsely ground cements may provide good performance, resulting in substantial energy savings due to a reduction in grinding time. This implies that cement fineness should be optimized based on the mixture proportions of the concrete in which it will be used. Published by Elsevier Science Ltd. All rights reserved.
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Title: Application of new information technology on concrete: an overview

Section 1: Introduction
Description 1: This section should introduce the rapid development of computers and information technology in concrete technology, highlighting key techniques and their impact on the field.

Section 2: Brief history of computer applications in concrete technology
Description 2: This section provides a historical overview of computer applications in concrete technology, including early simulation models and collaborative research efforts.

Section 3: Simulation models for cement and concrete based materials
Description 3: Discuss various simulation models developed for cement and concrete characterization, hydration, compressive strength, shrinkage, and durability.

Section 4: Databases
Description 4: Explain the use and development of databases in concrete technology, including distributed databases and online resources.

Section 5: Artificial Intelligence
Description 5: Describe the application of various AI techniques such as Expert Systems, Artificial Neural Networks, Fuzzy Logic, and Genetic Algorithms in concrete technology.

Section 6: Applications of Expert systems (ESs)
Description 6: Provide details on the development and use of expert systems in civil engineering and concrete technology for tasks such as diagnosis, design, and control.

Section 7: Application of Artificial Neural Networks (ANNs)
Description 7: Outline the use of ANNs for modeling and predicting various properties of concrete, discussing the training and validation phases.

Section 8: Application of Fuzzy Logic (FL)
Description 8: Explain how FL is applied to handle imprecision in concrete technology through numerical computation and linguistic fuzzy rules.

Section 9: Application of Genetic Algorithms (GAs)
Description 9: Discuss the use of GAs for optimization in concrete mix proportioning and their integration with other AI techniques.

Section 10: Technology of Integrated System
Description 10: Describe Computer Integrated Knowledge Systems (CIKS) and their applications in concrete technology, including prototypes and virtual systems.

Section 11: Virtual Cement and Concrete Test Laboratory (VCCTL)
Description 11: Detail the development and objectives of VCCTL, including its software modules and predictive capabilities for cement and concrete properties.

Section 12: Conclusions
Description 12: Summarize the impact of information technology on concrete technology, stressing the need for universally valid systems and further validation through laboratory tests.