Predicition of Compressive Strength in Light-Weight Self-Compacting Concrete by ANFIS Analytical Model

© 2015 by B. Vakhshouri, S. Nejadi. Light-weight Self-Compacting Concrete (LWSCC) might be the answer to the increasing construction requirements of slenderer and more heavily reinforced structural elements. However there are limited studies to prove its ability in real construction projects. In conjunction with the traditional methods, artificial intelligent based modeling methods have been applied to simulate the non-linear and complex behavior of concrete in the recent years. Twenty one laboratory experimental investigations on the mechanical properties of LWSCC; published in recent 12 years have been analyzed in this study. The collected information is used to investigate the relationship between compressive strength, elasticity modulus and splitting tensile strength in LWSCC. Analytically proposed model in ANFIS is verified by multi factor linear regression analysis. Comparing the estimated results, ANFIS analysis gives more compatible results and is preferred to estimate the properties of LWSCC

Neuro-fuzzy application for concrete strength prediction using combined non-destructive tests

The application of the neuro-fuzzy inference system to predict the compressive strength of concrete is presented in this study. The adaptive neuro-fuzzy inference system (ANFIS) is introduced for training and testing the data sets consisting of various parameters. To investigate the influence of various parameters which affect the compressive strength, 1551 data pairs are collected from the technical literature. These data sets cover early and late compressive strengths from 3 to 365 days and low and high strength in the range 6·3–107·7 MPa. To reflect the effects of other uncertain parameters and in situ conditions, the results of non-destructive tests (NDTs) such as ultrasonic pulse velocity (UPV) and rebound hammer test are also included as input parameters, in addition to mix proportion and curing histories. For the testing of trained ANFIS models, 20 cube specimens and 210 cylinders are prepared, and compressive test and NDTs are conducted. For the comparative study of the applicability of ANFIS models combined with NDT results, four ANFIS models are developed. Depending on whether the input parameters of ANFIS models include NDT results or not, these are distinguished from each other. Among the four models, the ‘ANFIS-UR' model having the parameters for both UPV and rebound hammer test results shows the best accuracy in the prediction of compressive strength

Prediction of rutting potential of dense bituminous mixtures with polypropylene fibers via repeated creep testing by using neuro-fuzzy approach

This study investigates the potential use of the neuro-fuzzy (NF) approach to model the rutting prediction by the aid of repeated creep testing results for polypropylene modified asphalt mixtures. Marshall specimens, fabricated with M-03 type polypropylene fibers at optimum bitumen content have been tested in order to predict their rutting potential under different load values and loading patterns at 50°C. Throughout the testing phase, it has been clearly shown that the addition of polypropylene fibers results in improved Marshall stabilities and decrease in the flow values, providing an eminent increase of the service life of samples under repeated creep testing. The performance of the accuracy of proposed neuro-fuzzy model is observed to be quite satisfactory. In addition, to obtain the main effects plot, a wide range of detailed two and three dimensional parametric studies have been performed

Application of Adaptive Neuro-Fuzzy Inference System in High Strength Concrete

Adaptive Neuro-Fuzzy Inference System is growing to predict nonlinear behaviour of construction materials. However due to wide variety of parameters in this type of artificial intelligent machine, selecting the proper optimization methods together with the best fitting membership functions strongly affect the accuracy of prediction. In this study the nonlinear relation between splitting tensile strength and modulus of elasticity with compressive strength of high strength concrete is modelled and the effect of different effective parameters of Adaptive Neuro-Fuzzy Inference System is investigated on these models. To specify the best arrangements of parameters in the System to utilize in high strength concrete properties, different combinations of optimization methods and membership functions in the Sugeno system have been applied on more than 300 previously conducted experimental datasets. Both the grid partition and sub-clustering methods have been applied to models and compared to get the best combination of parameters

Application of artificial intelligence to evaluate the fresh properties of self-consolidating concrete

This paper numerically investigates the required superplasticizer (SP) demand for self-consolidating concrete (SCC) as a valuable information source to obtain a durable SCC. In this regard, an adaptive neuro-fuzzy inference system (ANFIS) is integrated with three metaheuristic algorithms to evaluate a dataset from non-destructive tests. Hence, five different non-destructive testing methods, including J-ring test, V-funnel test, U-box test, 3 min slump value and 50 min slump (T50) value were performed. Then, three metaheuristic algorithms, namely particle swarm optimization (PSO), ant colony optimization (ACO) and differential evolution optimization (DEO), were considered to predict the SP demand of SCC mixtures. To compare the optimization algorithms, ANFIS parameters were kept constant (clusters = 10, train samples = 70% and test samples = 30%). The metaheuristic parameters were adjusted, and each algorithm was tuned to attain the best performance. In general, it was found that the ANFIS method is a good base to be combined with other optimization algorithms. The results indicated that hybrid algorithms (ANFIS-PSO, ANFIS-DEO and ANFIS-ACO) can be used as reliable prediction methods and considered as an alternative for experimental techniques. In order to perform a reliable analogy of the developed algorithms, three evaluation criteria were employed, including root mean square error (RMSE), Pearson correlation coefficient (r) and determination regression coefficient (R2). As a result, the ANFIS-PSO algorithm represented the most accurate prediction of SP demand with RMSE = 0.0633, r = 0.9387 and R2 = 0.9871 in the testing phase

Prediction of shear strength of reinforced concrete beams using adaptive neuro-fuzzy inference system and artificial neural network

AbstractIn this paper, the Artificial Neural Network (ANN) and the Adaptive Neuro-Fuzzy Inference System (ANFIS) are used to predict the shear strength of Reinforced Concrete (RC) beams, and the models are compared with American Concrete Institute (ACI) and Iranian Concrete Institute (ICI) empirical codes. The ANN model, with Multi-Layer Perceptron (MLP), using a Back-Propagation (BP) algorithm, is used to predict the shear strength of RC beams. Six important parameters are selected as input parameters including: concrete compressive strength, longitudinal reinforcement volume, shear span-to-depth ratio, transverse reinforcement, effective depth of the beam and beam width. The ANFIS model is also applied to a database and results are compared with the ANN model and empirical codes. The first-order Sugeno fuzzy is used because the consequent part of the Fuzzy Inference System (FIS) is linear and the parameters can be estimated by a simple least squares error method. Comparison between the models and the empirical formulas shows that the ANN model with the MLP/BP algorithm provides better prediction for shear strength. In adition, ANN and ANFIS models are more accurate than ICI and ACI empirical codes in prediction of RC beams shear strength

PRIMJENA METODOLOGIJA MEKOGA RAČUNARSTVA U PREDVIĐANJU 28-DNEVNE TLAČNE ČVRSTOĆE MLAZNOGA BETONA: KOMPARATIVNA USPOREDBA INDIVIDUALNOGA I HIBRIDNOGA MODELA

Shotcreting is a popular construction technique with wide-ranging applications in mining and civil engineering. Compressive strength is a primary mechanical property of shotcrete with particular importance for project safety, which highly depends on its mix design. But in practice, there is no reliable and accurate method to predict this strength. In this study, existing experimental data related to shotcretes with 59 different mix designs are used to develop a series of soft computing methodologies, including individual artificial neural network, support vector regression, and M5P model tree and their hybrids with the fuzzy c-means clustering algorithm so as to predict the 28-day compressive strength of shotcrete. Analysis of the results shows the superiority of the hybrid model over the individual models in predicting the compressive strength of shotcrete. Overall, data clustering prior to use of machine learning techniques leads to certain improvement in their performance and reliability and generalizability of their results. In particular, the M5P model tree exhibits excellent capability in anticipating the compressive strength of shotcrete.Mlazni beton popularna je konstrukcijska tehnika široke uporabe u rudarstvu i građevinarstvu. Tlačna čvrstoća primarno je mehaničko svojstvo mlaznoga betona s posebnom važnošću za sigurnost projekta, ovisno o sastavu betona. U praksi ne postoji pouzdana i točna metoda za predviđanje toga svojstva. Ovdje su prikazani eksperimentalni podatci za 59 različitih sastava mlaznoga betona, na kojima je razvijen niz metodologija temeljem mekoga računarstva, uključujući pojedinačnu umjetnu neuronsku mrežu, podržanu vektorskom regresijom, stablastim dijagramima, njihovim hibridima na temelju klastera vrijednosti c-sredina, a s ciljem predviđanja promjene tlačne čvrstoće mlaznoga betona tijekom 28 dana. Općenito su klasteri podataka već prije uporabe strojnoga učenja znatno pomogli u kvaliteti, pouzdanosti i općenitosti rezultata. Posebno je istaknut stablasti model M5P kao onaj koji izvrsno predviđa tlačnu čvrstoću mlaznoga betona