Prediction of concrete strength using ultrasonic pulse velocity and artificial neural networks

Ultrasonic pulse velocity technique is one of the most popular non-destructive techniques used in the assessment of concrete properties. However, it is very difficult to accurately evaluate the concrete compressive strength with this method since the ultrasonic pulse velocity values are affected by a number of factors, which do not necessarily influence the concrete compressive strength in the same way or to the same extent. This paper deals with the analysis of such factors on the velocity-strength relationship. The relationship between ultrasonic pulse velocity, static and dynamic Young's modulus and shear modulus was also analyzed. The influence of aggregate, initial concrete temperature, type of cement, environmental temperature, and w/c ratio was determined by our own experiments. Based on the experimental results, a numerical model was established within the Matlab programming environment. The multilayer feed-forward neural network was used for this purpose. The paper demonstrates that artificial neural networks can be successfully used in modelling the velocity-strength relationship. This model enables us to easily and reliably estimate the compressive strength of concrete by using only the ultrasonic pulse velocity value and some mix parameters of concrete. (C) 2008 Elsevier B.V. All rights reserved

New numerical procedure for the prediction of temperature development in early age concrete structures

A new numerical model for the prediction of temperature development in young concrete structures is briefly presented. With the pre-program. adiabatic hydration curves, which are used to determine the internal heat generation, are calculated. An artificial neural networks approach is used for this purpose. Adiabatic hydration curves, which were included in the learning set, were determined by our own experiments, using the adiabatic calorimeter which uses air as the coupling media. The main program is implemented in the finite element code. This program allows concrete structure designers and contractors to quantify and evaluate the effects of some concrete initial parameters on the adiabatic hydration curves and corresponding temperature development at an arbitrary point in the concrete element. Some examples are also presented and discussed. (C) 2009 Elsevier B.V. All rights reserve

Case-based reasoning approach to estimating the strength of sustainable concrete

Continuing from previous studies of sustainable concrete containing environmentally friendly materials and existing modeling approach to predicting concrete properties, this study developed an estimation methodology to predicting the strength of sustainable concrete using an advanced case-based reasoning approach. It was conducted in two steps: (i) establishment of a case database and (ii) development of an advanced case-based reasoning model. Through the experimental studies, a total of 144 observations for concrete compressive strength and tensile strength were established to develop the estimation model. As a result, the prediction accuracy of the A-CBR model (i.e., 95.214% for compressive strength and 92.448% for tensile strength) performed superior to other conventional methodologies (e.g., basic case-based reasoning and artificial neural network models). The developed methodology provides an alternative approach in predicting concrete properties and could be further extended to the future research area in durability of sustainable concrete

Using mathematical models and artificial neural networks for predicting the compressive strength of concrete with steel fibers exposed to high temperatures

In this study mathematical methods and artificial neural network (ANN) model are used to predict the compressive strength of concrete with steel fibers exposed at high temperatures. The data used in the models construction were obtained from laboratory experiments. The compressive strength was experimentally determined for specimens containing three volume fractions of steel fibers 0.19%,0.25%, 0.5% were used and two different water/cement ratios (w/c of 0.35 and 0.45). Specimens were subjected to various heating-cooling cycles from the room temperature to 200, 400, and 600°C. The inputs models of ANN were temperature, w/c, percentage of porosity, ultrasonic pulse velocity; percentage of steel fibers and percentage superplasticizer, the output was the compressive strength of concrete. Four mathematical models were development to predict the compressive strength. Three mathematical models including a number of functions to express the strength-porosity relationship, and other model used to establish the relationships between strength and ultrasonic pulse velocity. Mathematical methods, artificial neural network and their results were evaluated and compared. The results show that ANN has good potential to be used as a tool for predicting the compressive strength of concrete with steel fibers exposed to high temperatures

A Machine Learning-Assisted Numerical Predictor for Compressive Strength of Geopolymer Concrete Based on Experimental Data and Sensitivity Analysis

Geopolymer concrete offers a favourable alternative to conventional Portland concrete due to its reduced embodied carbon dioxide (CO2) content. Engineering properties of geopolymer concrete, such as compressive strength, are commonly characterised based on experimental practices requiring large volumes of raw materials, time for sample preparation, and costly equipment. To help address this inefficiency, this study proposes machine learning-assisted numerical methods to predict compressive strength of fly ash-based geopolymer (FAGP) concrete. Methods assessed included artificial neural network (ANN), deep neural network (DNN), and deep residual network (ResNet), based on experimentally collected data. Performance of the proposed approaches were evaluated using various statistical measures including R-squared (R2), root mean square error (RMSE), and mean absolute percentage error (MAPE). Sensitivity analysis was carried out to identify effects of the following six input variables on the compressive strength of FAGP concrete: sodium hydroxide/sodium silicate ratio, fly ash/aggregate ratio, alkali activator/fly ash ratio, concentration of sodium hydroxide, curing time, and temperature. Fly ash/aggregate ratio was found to significantly affect compressive strength of FAGP concrete. Results obtained indicate that the proposed approaches offer reliable methods for FAGP design and optimisation. Of note was ResNet, which demonstrated the highest R2 and lowest RMSE and MAPE values

Compressive Strength Prediction of Self-Compacting Concrete Incorporating Silica Fume Using Artificial Intelligence Methods

This paper investigates the capability of utilizing Multivariate Adaptive Regression Splines (MARS) and Gene Expression Programing (GEP) methods to estimate the compressive strength of self-compacting concrete (SCC) incorporating Silica Fume (SF) as a supplementary cementitious materials. In this regards, a large experimental test database was assembled from several published literature, and it was applied to train and test the two models proposed in this paper using the mentioned artificial intelligence techniques. The data used in the proposed models are arranged in a format of seven input parameters including water, cement, fine aggregate, specimen age, coarse aggregate, silica fume, super-plasticizer and one output. To indicate the usefulness of the proposed techniques statistical criteria are checked out. The results testing datasets are compared to experimental results and their comparisons demonstrate that the MARS (R2=0.98 and RMSE= 3.659) and GEP (R2=0.83 and RMSE= 10.362) approaches have a strong potential to predict compressive strength of SCC incorporating silica fume with great precision. Performed sensitivity analysis to assign effective parameters on compressive strength indicates that age of specimen is the most effective variable in the mixture

Numerıcal Modelıng And Experımental Evaluatıon Of Shrınkage Of Concretes Incorporatıng Fly Ash And Sılıca Fume

Rötre genellikle sertleşmiş betonun önemli bir özelliği olarak ele alınır. Kuruma sürecinde boşluk yapısında bulunan serbest ve emilmiş su kaybedilir. Betonun rötresi kısıtlandığı zaman betonda olşan gerilmelere bağlı olarak çatlak oluşumu gözlenir. Bu çatlaklardan zararlı maddelerin geçmesiyle betonun dayanım ve dayanıklılıgında azalma olur. Bu çalışman ilk aşamasinda genetik programlama ve yapay sinir ağları yöntemleri kullanılarak rötre tahmin modelleri geliştirilmiştir. Modellerin eğitimi ve test edilmesi için literatürden veri toplanmıştır. Çalışmanın ikinci aşamasında ise uçucu kül ve silis dumanı içeren betonlar hazırlanarak kırk günlük kuruma sürecinde rötreleri ölçülmüştür. En yüksek rötre değerleri en çok mineral katkı içeren betonlarda gözlenmiştir. Bunların yanı sıra deneysel çalışmada elde edilen sonuçlar tahmin modellerinin verdikleriyle karşılaştırılmışlardır. YSA ile elde edilen değerlerin GP ile elde edilenlere göre gerçeğe daha yakın oldukları görülmüştür

Determination of compressive strength of perlite-containing slag-based geopolymers and its prediction using artificial neural network and regression-based methods

This study has two main objectives: (i) to investigate the parameters affecting the compressive strength (CS) of perlite-containing slag-based geopolymers and (ii) to predict the CS values obtained from experimental studies. In this regard, 540 cubic geopolymer samples incorporating different raw perlite powder (RPP) replacement ratios, different sodium hydroxide (NaOH) molarity, different curing time, and different curing temperatures for a total of 180 mixture groups were produced and their CS results were experimentally determined. Then conventional regression analysis (CRA), multivariate adaptive regression splines (MARS), and TreeNet methods, as well as artificial neural network (ANN) methods, were used to predict the CS results of geopolymers using this experimentally obtained data set. Root mean square error (RMSE), mean absolute error (MAE), scatter index (SI) and Nash-Sutcliffe (NS) performance statistics were used to evaluate the CS prediction capabilities of the methods. As a result, it was determined that the optimum molarity, curing time, and curing temperature were 14 M, 24 h, and 110 celcius, respectively and 48 h of heat curing did not have a significant effect on increasing the CS of the geopolymers. The highest performances in regression-based models were obtained from the MARS method. However, the ANN method showed higher prediction performance than the regression-based methods. Considering the RMSE values, it was seen that the ANN method made improvements by 24.7, 2.1, and 13.7 %, respectively, compared to the MARS method for training, validation, and test sets

Concrete Mix Design Using Artificial Neural Network

Concrete mix design is a process based on sound technical principles for proportioning of ingredients in right quantities. This paper demonstrates the applicability of Artificial Neural Networks (ANN) Model for approximate proportioning of concrete mixes. For ANN a trained back propagation neural network is integrated in the model to learn experimental data pertaining to predict 7, 14 and 28-day compressive strength which have been loaded into a model, containing 55 concrete mixtures. The ANN model proposed is based on 5 input parameters such as cement, sand, coarse aggregate, and water and fineness modulus. The proposed concrete mix proportion design is expected to reduce the number of trials in laboratory as well as field, saves cost of material as well as labor and also saves time as it provides higher accuracy. The concrete designed is expected to have higher durability and hence is economical

Use of artificial neural networks to predict concrete compression strength / Uso de redes neurais artificiais na predição da resistência à compressão do concreto

Concrete is one of the most widely used building materials, being composed of different components with different properties, which makes the task of dosing and strength determination complex. Artificial Neural Networks is a tool that has the ability to generalize and learn from previous experiences that are provided by a previously built database. This work aims the implementation of RNA in determining the compressive strength of concrete of various ages. The input data is the material quantities and the output is the compressive strength. The results obtained are satisfactory and promising from the point of view of civil engineering.