A comparative study of concrete strength prediction using artificial neural network, multigene programming and model tree

In the current study 28 day strength of Recycled Aggregate Concrete (RAC) and Fly ash (class F) based concrete is predicted using Artificial Neural Network (ANN), Multigene Genetic Programming (MGGP) and Model Tree (MT). Four sets of models were designed for per cubic proportions of materials, Properties of materials and non-dimensional parameters as input parameters. The study shows that the predicted 28 day strength is in good agreement with the observed data and also generalize well to untrained data. ANN outperforms MGGP and MT in terms of model performance. Output of the developed models can be presented in terms of trained weights and biases in ANN, equations in MGGP and in the form of series of equations in MT. ANN, MGGP and MT can grasp the influence of input parameters which can be seen through Hinton diagrams in ANN, input frequency distribution in MGGP and coefficients of input parameters in MT. The study shows that these data driven techniques can be used for developing model/s to predict strength of concrete with an acceptable performance

Machine Learning Prediction of Mechanical and Durability Properties of Recycled Aggregates Concrete

Whilst recycled aggregate (RA) can alleviate the environmental footprint of concrete production and the landfilling of colossal amounts of demolition waste, there need for robust predictive tools for its effects on mechanical and durability properties. In this thesis, state-of-the-art machine learning (ML) models were deployed to predict properties of recycled aggregate concrete (RAC). A systematic review was performed to analyze pertinent ML techniques previously applied in the concrete technology field. Accordingly, three different ML methods were selected to determine the compressive strength of RAC and perform mixture proportioning optimization. Furthermore, a gradient boosting regression tree was used to study the effects of RA and several types of binders on the carbonation depth of RAC. The ML models developed in this study demonstrated robust performance to predict diverse properties of RAC

Evaluation of the Mechanical Properties of Normal Concrete Containing Nano-MgO under Freeze–Thaw Conditions by Evolutionary Intelligence

In this research, different amounts of nano-MgO were added to normal concrete samples, and the effect of these particles on the durability of the samples under freeze and thaw conditions was investigated. The compressive and tensile strength as well as the permeability of concrete containing nanoparticles were measured and compared to those of plain samples (without nanoparticles). The age of concrete samples, percentage of nanoparticles, and water-to-binder ratio are the variables of the current research. Based on the results, the addition of 1% nano-MgO to the normal concrete with a water-to-binder ratio of 0.44 can reduce the permeability up to 63% and improve the compressive and tensile strengths by 9.12% and 10.6%, respectively. Gene Expression Programming (GEP) is applied, and three formulations are derived for the prediction of mechanical properties of concrete containing nano-MgO. In this method, 80% of the dataset is used randomly for the training process and 20% is utilized for testing the formulation. The results obtained by GEP showed acceptable accuracy

Predictive model to the bond strength of FRP-to-concrete under direct pullout using gene expression programming

Gene expression programming (GEP) is used in this research to develop an empirical model that predicts the bond strength between the concrete surface and carbon fiber reinforced polymer (CFRP) sheets under direct pull out. Therefore, a large and reliable database containing 770 test specimens is collected from the literature. The gene expression programming model is developed using eight parameters that predominantly control the bond strength. These parameters are concrete compressive strength, maximum aggregate size, fiber reinforced polymer (FRP) tensile strength, FRP thickness, FRP modulus of elasticity, adhesive tensile strength, FRP length, and FRP width. The model is validated using the experimental results and a statistical assessment is implemented to evaluate the performance of the proposed GEP model. Furthermore, the predicted bond results, obtained using the GEP model, are compared to the results obtained from several analytical models available in the literature and a parametric study is conducted to further ensure the consistency of the model by checking the trends between the input parameters and the predicted bond strength. The proposed model can reasonably predict the bond strength that is most fitting to the experimental database compared to the analytical models and the trends of the GEP model are in agreement with the overall trends of the analytical models and experimental tests. First published online 30 August 201

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

M5' and Mars Based Prediction Models for Properties of Self-Compacting Concrete Containing Fly Ash

The main purpose of this paper is to predict the properties (mechanical and rheological) of the self-compacting concrete (SCC) containing fly ash as cement replacement by using two decision tree algorithms: M5′ and Multivariate adaptive regression splines (Mars). The M5′ algorithm as a rule based method is used to develop new practical equations while the MARS algorithm besides its high predictive ability is used to determine the most important parameters. To achieve this purpose, a data set containing 114 data points related to effective parameters affect on SSC properties is used. A gamma test is employed to determine the most effective parameters in prediction of the compressive strength at 28 days, the V-funnel time, the slump flow, and the L-box ratio of SCC. The results from this study suggests that tree based models perform remarkably well in predicting the properties of the self-compacting concrete containing fly ash as cement replacement.&nbsp

Machine Learning Prediction of Shear Capacity of Steel Fiber Reinforced Concrete

The use of steel fibers for concrete reinforcement has been growing in recent years owing to the improved shear strength and post-cracking toughness imparted by fiber inclusion. Yet, there is still lack of design provisions for steel fiber-reinforced concrete (SFRC) in building codes. This is mainly due to the complex shear transfer mechanism in SFRC. Existing empirical equations for SFRC shear strength have been developed with relatively limited data examples, making their accuracy restricted to specific ranges. To overcome this drawback, the present study suggests novel machine learning models based on artificial neural network (ANN) and genetic programming (GP) to predict the shear strength of SFRC beams with great accuracy. Different statistical metrics were employed to assess the reliability of the proposed models. The suggested models have been benchmarked against various soft-computing models and existing empirical equations. Sensitivity analysis has also been conducted to identify the most influential parameters to the SFRC shear strength

PREDVIĐANJE VISINE SLOMA PRIMJENOM PROGRAMIRANJA GENSKE EKSPRESIJE U AUSTRALSKIM ŠIROKOČELNIM OTKOPIMA, KOMPARATIVNA STUDIJA

The caving and subsidence developments above a longwall panel usually result in fractures of the overburden, which decrease the strength of the rock mass and its function. The height of fracturing (HoF) includes the caved and continuous fractured zones affected by a high degree of bending. Among the various empirical models, Ditton’s geometry and geology models are widely used in Australian coalfields. The application of genetic programming (GP) and gene expression programming (GEP) in longwall mining is entirely new and original. This work uses a GEP method in order to predict HoF. The model variables, including the panel width (W), cover depth (H), mining height (T), unit thickness (t), and its distance from the extracted seam (y), are selected via the dimensional analysis and Buckingham’s P-theorem. A dataset involving 31 longwall panels is used to present a new nonlinear regression function. The statistical estimators, including the coefficient of determination (R2), the average error (AE), the mean absolute percentage error (MAPE), and the root mean square error (RMSE), are used to compare the performance of the discussed models. The R2 value for the GEP model (99%) is considerably higher than the corresponding values of Ditton’s geometry (61%) and geology (81%) models. Moreover, the maximum values of the statistical error estimators (AE, MAPE, and RMSE) for the GEP model are 12%, 14%, and 16%, respectively, of the corresponding values of Ditton’s models.Razvoj kaverni i slijeganja iznad otkopa širokoga čela obično rezultira slomovima jalovinskih slojeva, što smanjuje čvrstoću stijenske mase. Visina sloma (HoF) uključuje udubljene i kontinuirane zone sloma zahvaćene visokim stupnjem savijanja. Među raznim empirijskim modelima Dittonovi geometrijski i geološki modeli široko se koriste u australskim ugljenokopima. Primjena genetskoga programiranja (GP) i programiranja ekspresije gena (GEP) u širokočelnim metodama posve je nova i originalna. U ovome radu primjenjuje se GEP metoda kako bi se predvidio HoF. Varijable modela, uključujući širinu čela (W), debljinu nadsloja (H), visinu čela (T), debljinu sloja (t) i njegovu udaljenost od otkopanoga sloja (y), odabiru se dimenzionalnom analizom i Buckinghamovim P teoremom. Skup podataka koji uključuje 31 širokočelni otkop koristi se za predstavljanje nove funkcije nelinearne regresije. Statistički procjenitelji, uključujući koeficijent determinacije (R2), prosječnu pogrešku (AE), srednju apsolutnu postotnu pogrešku (MAPE) i srednju kvadratnu pogrešku (RMSE), koriste se za usporedbu učinkovitosti razmatranih modela. Vrijednost (R2) za GEP model (99 %) znatno je veća od odgovarajućih vrijednosti Dittonove geometrije (= 61 %) i geologije (= 81 %). Štoviše, maksimalne vrijednosti procjenitelja statističkih pogrešaka (AE, MAPE i RMSE) za GEP model iznose 12 %, 14 % odnosno 16 % odgovarajućih vrijednosti Dittonovih modela

Rheological and mechanical performance evaluation of high performance mortar based natural pozzolan

This paper presents an experimental study on the rheological and mechanical properties of High Performance Mortar (HPM) based on natural pozzolan (NP). The specific surface BET of NP was 370 m²/kg used with different contents by weight of cement (5, 10, 15 and 20%). Two (w/b) water-binder ratios (0.35and 0.40) were used, the dosage of Superplasticizer (Sp) was kept constant (0.32 by weight of cement). The experimental results show that rheological properties of HPM increased with increasing NP content when w/b kept constant, but the increasing of (w/b) ratio led to decrease of both yield stress and plastic viscosity of mixtures.The mechanical characteristics were improved with increasing NP content when w/b kept constant, but the increasing of (w/b) ratio led to decrease of both compressive strength. The optimal percentage substitution was 15% of NP, reducing CO2 emission by 20% for each cubic meter of mortar production

Rheological and mechanical performance evaluation of high performance mortar based natural pozzolan

This paper presents an experimental study on the rheological and mechanical properties of High Performance Mortar (HPM) based on natural pozzolan (NP). The specific surface BET of NP was 370 m²/kg used with different contents by weight of cement (5, 10, 15 and 20%). Two (w/b) water-binder ratios (0.35and 0.40) were used, the dosage of Superplasticizer (Sp) was kept constant (0.32 by weight of cement). The experimental results show that rheological properties of HPM increased with increasing NP content when w/b kept constant, but the increasing of (w/b) ratio led to decrease of both yield stress and plastic viscosity of mixtures.The mechanical characteristics were improved with increasing NP content when w/b kept constant, but the increasing of (w/b) ratio led to decrease of both compressive strength. The optimal percentage substitution was 15% of NP, reducing CO2 emission by 20% for each cubic meter of mortar production