Machine learning and interactive GUI for concrete compressive strength

Similarly a study by Nguyen et al 32 introduced four distinct machine learning models to anticipate the compressive and tensile strength of HPC highlighting the superior output accuracy of

Compressive Strength Prediction of High

We have analyzed the Compressive Strength Data and used Machine Learning to Predict the Compressive Strength of Concrete We have used Linear Regression and its variations Decision Trees Random Forests SVM KNN XGBooster ANN to make predictions and compared their performance XGBooster Regressor has the lowest RMSE and is a good

Machine learning based compressive strength prediction for

Then the compressive strength of the concrete was obtained through a typical compressive test procedure of the cylinder specimens with height 150 mm Obviously there are 9 parameters in total in the experimental data sets Machine learning in concrete strength simulations multi nation data analytics Constr Build Mater 73 2014 pp

Compressive strength prediction of high strength oil palm

The demand and cost of construction materials are increasing due to the world s rapidly growing population Shadmani et al 2018 The ever growing demand for natural resources to meet the demand of the market and economic growth has resulted in a detrimental impact on the environment and a lack of raw materials Saberian et al 2021 In some countries

Evaluating the Sensitivity of Machine Learning Models to

This study rigorously examines the impact of various data preprocessing techniques on the accuracy of machine learning models in predicting concrete s compressive strength It develops ten regression models under nine distinct preprocessing scenarios including normalization standardization principal component analysis PCA and polynomial features

Compressive strength of concrete with recycled aggregate; a machine

Considering the numerical compressive strength reported in Table 4 the 3 and 7 day strength values vary in the range of % and respectively while 28 day strength alters in the range of and % On the other hand for the concrete with fine recycled aggregate the reduction in compressive strength of 3 day

Compressive Strength Prediction of Fly Ash Concrete Using Machine

It is time consuming and uneconomical to estimate the strength properties of fly ash concrete using conventional compression experiments For this reason four machine learning models—extreme learning machine random forest original support vector regression SVR and the SVR model optimized by a grid search algorithm—were proposed to predict the

Support vector machine for prediction of the compressive strength

The results showed that after optimization process both models are applicable for prediction purposes with similar high qualities of estimation and generalization norms; however it was indicated that optimization and modeling with SVM is very rapid than ANN models The sensitivity of compressive strength of no slump concrete to its ingredient materials and proportions

Machine learning techniques to predict the compressive strength

Conventional concrete is the most common material used in civil construction and its behavior is highly nonlinear mainly because of its heterogeneous characteristics Compressive strength is one of the most critical parameters when designing concrete structures and it is widely used by engineers This parameter is usually determined through expensive

Machine learning prediction of compressive strength of

Machine learning techniques can predict the compressive strength of cement based materials with good accuracy and learning capacity Traditional compressive strength prediction according to machine learning techniques such as the support vector machine SVM decision tree and Gaussian regression are normally based on the mix proportion of concrete

Compressive strength prediction of high strength concrete

Concrete is the most extensively used construction material and cement is its main component Hybrid machine learning models attract researchers in building materials due to their high applications and prediction accuracy Hybrid machine learning model interpretability is crucial to apply to the interest of field experts Therefore this research study proposes to

Compressive Strength Testing Machine ToniZEM Model

Compression test machine ToniZEM Model 1547 Compact design for standard compliant compressive strength tests on cement and other binding materials Prisms 40 x 40 x 160 mm accord to EN 196 and ISO 679 Cubes 50 mm 2 inch edge length accord to ASTM C 109 Cubes mm edge length accord to BS 1881 Accuracy Quality Class 1 / EN ISO 7500 1

PDF Machine learning based compressive strength

A total of 1030 sets of concrete compressive strength tests is collected to train and test the learners in which the concrete mixture components coarse/fine aggregates cement water

Prediction of Compressive Strength of Concrete Machine

The compressive strength is the most important property of a concrete mix Abrams law of 1919 gives an inverse proportionality between the compressive strength of hardened concrete and the water to cement ratio w/c used in the concrete mix measured by volume [] It hence implies that the strengths of distinct but comparable concrete are identical

Compressive Strength Testing Machines Toni Technik

Compressive Strength Testing Machines / Compression Test Machines Quality made in Germany Developed and manufactured by Toni Technik in Berlin Germany

Frontiers Prediction of compressive strength of recycled

The RAC compressive strength prediction model s core is to use Python to access the database of RAC compressive strength train the XGBoost RF KNN SVR and GBDT models and then use cross validation and the Hyperparameter configuration method GS RS BO TPE BO GPR to optimize the hyperparameters in the specified hyperparameter space to

Investigation on compressive strength of coral aggregate

The curing process and compressive strength testing procedure for CAC specimens were conducted in accordance with the relevant provisions specified in the standard [47] Since the dimensions of the specimens are non standard the calculated compressive strength values should be multiplied by a dimension conversion factor of [48 49]

Predicting the compressive strength of ultra high Springer

The main objective of this research is to propose two ensemble machine learning models namely random forest RF and adaptive gradient boosting AGB for predicting the compressive strength of ultra high performance concrete UHPC These models are developed based on a total of 810 experimental results of the compressive strength of UHPC collected

High performance Concrete Compressive Strength Prediction using

Compressive strength is a mechanical property critical to measuring HPC quality [4] [34] Twenty eighth day compressive strength is the most widely used objective function in the mixture design The term weighted support vector machines wSVMs was proposed by Fan and Ramamohanarao [18] as a synonym for Fuzzy Support Vector Machines

Machine Learning the Concrete Compressive Strength From

Empirical results have shown that the concrete compressive strength CCS is strongly influenced by the water to cement ratio but the amount of other individual constituents also plays a significant role in the concrete workability and final mechanical strength [3 4] FAs are one of the residues generated by coal combustion which can be siliceous or calcareous

Concrete Compressive Strength Testing Machine Made in

Our Concrete Compressive Strength Testing Machine offers exceptional quality and style within the Test Machine test machines from China offers competitive pricing a wide range of product selections and access to advanced technology China is known for its manufacturing expertise and suppliers offer customization options to

Compressive strength analysis of fly ash based geopolymer

Fly ash is the most commonly used source binder material for producing geopolymer concrete [7] due to its low cost wide availability and increased potential for preparing coal burning waste products fly ash based geopolymer concrete FA GPC is an adequate substitute that can reduce carbon dioxide emissions by 25% 45% [17] The type and

Comparative study of machine learning methods to predict compressive

Concrete compressive strength CCS is one of the most important parameters to determine the performance of concrete during service conditions To accurately predict the compressive strength of the entire concrete system makes it a great challenge for a sustainable built environment and future generations since the materials are randomly distributed materials