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Regular use of artificial neural networks (ANN) analysis for predicting the vertical swelling percentage of expansive clays may lead to inappropriate results in terms of their geophysical behavior. This paper presents two new ANN Models derived from a two-stage procedure. The models were estimated using the same data set from the previous paper, and their statistical fit was clearly found to be superior in comparison to the previous models. Furthermore, one of these two models exhibited the expected geophysical behavior. As this new ANN Model yields higher predicted swelling-percentage values, it can definitely be regarded as a preferable one in the sense of enlarging the safety margin in heave calculations. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The general swelling model has recently been updated in Israel by applying the Excel-solver command (ESC) analysis to new local test results from 897 undisturbed specimens. In this analysis, the goodness-of-fit statistics obtained classify the category of their associated regression only as fair. Thus, it seems necessary to explore the possibility of enhancing the outputs of this regression analysis by applying the artificial neural networks (ANN) methodology to the same 897 undisturbed specimens. However, it is shown that the use of the ANN outputs should be accompanied by an additional check to ensure that they follow the expected physical swelling behavior, as characterized by the index properties of the soil. The ANN methodology applied in this paper is similar to previous studies in geotechnical engineering. Different models were tested using the same database (i.e., the same 897 undisturbed specimens). The statistical fit of the ANN models were clearly found to be superior to the ESC models. However, in the sense of the required physical behavior, as characterized by the index properties of the soil, the ANN models did not predict swelling values as well as ESC models did, in particular values ranging near (or outside) the data set boundaries. Thus, the former ESC models still remain preferable. [ABSTRACT FROM AUTHOR]

The spectral analysis of surface waves (SASW) method is a nondestructive testing method of pavements based on the dispersive characteristic of seismic surface waves. The method can provide the thickness and stiffness of pavement layers. One of the more complex aspects of the SASW method is an iterative process to estimate the pavement parameters, called the inversion procedure. In this paper, the feasibility of completely automating the inversion process and substituting it with artificial neural network (ANN) models was explored. A number of different ANN models were developed using various ANN training strategies. To improve the performance of some ANN models, a sequential modeling technique was implemented. In the sequential modeling, some pavement parameters are estimated first from an initial set of ANN models, which is then the input to subsequent ANN models to estimate other parameters of interest. Furthermore, the performance of the ANN models was evaluated using a number of well-characterized pavement sections. The results illustrated that ANN models could estimate the upper layers’ parameters so well that they could replace the inversion process. The ANN models for other layers were capable of generating robust initial estimates for a well-constrained formal inversion that can be readily automated. [ABSTRACT FROM AUTHOR]

Over the past few years, many regression-based and artificial neural network (ANN)-based models have been developed to estimate the dynamic modulus of hot mix asphalt (HMA). These models use the gradation of aggregates and the volumetric properties of compacted samples as input variables to the model. However, none of these models use aggregate shape parameters (i.e., angularity, texture, form, and sphericity) in the development of the model. Recently, researchers have expressed concerns that the shape parameters of aggregates need to be considered in the estimation of dynamic modulus. The primary objective of this study was to develop an ANN-based model for the estimation of dynamic modulus of HMA using aggregate shape parameters. The dynamic modulus of 20 different HMA mixes composed of various sources, sizes, types of aggregates, and different volumetric properties were measured in the laboratory. The shape parameters of different sizes of coarse and fine aggregates were measured with an automated aggregate image measurement system (AIMS). An ANN-based model was developed to consider the following input variables: aggregate shape parameters (i.e., angularity, texture, form, and sphericity), frequency, asphalt viscosity, and air voids of compacted samples. A sensitivity analysis of each model parameter was conducted by correlating these parameters with dynamic modulus. It is expected that this study will be helpful in predicting the dynamic modulus of HMA using aggregate shape parameters. [ABSTRACT FROM AUTHOR]

In the analysis or design process of reinforced concrete structures, the peak stress and strain in plain concrete under triaxial stress are critical. However, the nonlinear behavior of concrete under triaxial stresses is very complicated; modeling its behavior is therefore a complicated task. In the present study, several radial basis function neural network (RBFN) models have been developed for predicting peak stress and strain in plain concrete under triaxial stress. For the purpose of constructing the RBFN models, 56 records including normal- and high-strength concretes under triaxial loads were retrieved from literature for analysis. The K-means clustering algorithm and the pseudoinverse technique were employed to train the network for extracting knowledge from training examples. Besides, the performance of the developed RBFN models was estimated by the method of three-way data splits and K-fold cross-validation. On the other hand, a comparative study between the RBFN models and existing regression models was made. The results demonstrate the versatility of RBFN in constructing relationships among multiple variables of nonlinear behavior of concrete under triaxial stresses. Moreover, the results also show that the RBFN models provided better accuracy than the existing parametric models, both in terms of root-mean-square error and correlation coefficient. [ABSTRACT FROM AUTHOR]

Artificial neural network (ANN) and regression models are developed for the estimation of concrete slump using concrete constituent data. The concrete mix constituent and slump data from laboratory tests have been employed to develop all models. The results obtained in this study demonstrate the superiority of the ANN models. It was found that combining one or more concrete mix constituents and treating them as an independent input variable is not advantageous when using regression but can be very useful when using ANNs for modeling concrete slump. Sensitivity analyses based on the ANN models were carried out to evaluate the impact of different concrete mix constituents on the slump values. It was found that the slump attains a minimum value at the critical levels of mortar and coarse aggregates, and tends to increase with paste content and decrease with sand content in the concrete mix. [ABSTRACT FROM AUTHOR]

The compressive strength of concrete is a commonly used criterion in producing concrete. However, the tests on the compressive strength are complicated and time consuming. More importantly, it is too late to make improvements even if the test result does not satisfy the required strength, since the test is usually performed on the 28th day after the placement of concrete at the construction site. Therefore, accurate and realistic strength estimation before the placement of concrete is very important. This study presents the probabilistic technique for predicting the compressive strength of concrete on the basis of concrete mix proportions. The estimation of the strength is performed using the probabilistic neural network which is an effective tool for the pattern classification problem and provides a probabilistic viewpoint as well as a deterministic classification result. Application of probabilistic neural networks in the compressive strength estimation of concrete is performed using the mix proportion data and test results of two concrete companies. It has been found that the present methods are very efficient and reasonable in predicting the compressive strength of concrete probabilistically. [ABSTRACT FROM AUTHOR]

In this study, a four-layer feed-forward neural network is constructed and applied to determine a mapping associating mix design and testing factors of asphalt concrete samples with their performance in conductance to flow or permeability. To generate data for the neural network model, a total of 100 field cores from 50 different mixes (two replicate cores per mix) are tested in the laboratory for permeability and mix volumetric properties. The significant factors that affect asphalt permeability are identified using simple and multiple regression analysis. The analyses results show that permeability of an asphalt concrete is affected mainly by five factors: (1) air void (Va); (2) the grain size through which 10% materials pass (d10); (3) the grain size through which 30% materials pass (d30); (4) saturation, or the CoreLok Infiltration Coefficient (CIC); and (5) effective asphalt to dust ratio (Pbe/P0.075). The significant factors are then used to define the domain of a neural network. Regardless of the significant factors included in defining the domain of such a mapping, a principle component analysis is performed to ascertain the most significant of these factors. The network is trained using the Levenberg-Marquardt algorithm. Using randomly generated synaptic weights to initialize the training algorithm, histograms are compiled and outputs are estimated. Excellent agreement is observed between simulation and laboratory data. It is believed that the developed NN model will be a useful tool in the study of asphalt pavement construction and maintenance. [ABSTRACT FROM AUTHOR]

The uniaxial compressive strength of concrete is the most widely used criterion in producing concrete. Although testing of the uniaxial compressive strength of concrete specimens is done routinely, it is performed on the 28th day after concrete placement. At this point, it is too late to make improvements if the test result does not satisfy the required strength. Therefore, the strength estimation before the placement of concrete is highly desirable. This study presents the first effort in applying neural network-based system identification techniques to predict the compressive strength of concrete based on concrete mix proportions. Back-propagation neural networks were developed, trained, and tested using actual data sets of concrete mix proportions provided by two ready-mixed concrete companies. The compressive strengths estimated by the neural networks were verified by laboratory testing results. This study demonstrated that the neural network techniques are effective in estimating the compressive strength of concrete based on the mix proportions. Application of these techniques will contribute significantly to the concrete quality assurance. [ABSTRACT FROM AUTHOR]