In addition a boosting based ensemble machine learning algorithm namely the gradient boosting regression tree GBRT algorithm is proposed for predicting the compressive strength of concrete 1030 sets of concrete compressive strength test data were collected as the dataset of which 60% were used to train the model 20% to validate the
The observations we can make from this plot Compressive strength increases as the amount of cement increases as the dots move up when we move towards right on the x axis ; Compressive strength increases with age as the size of dots represents the age this not the case always but can be up to an extent ; Cement with less age requires more cement for
The database is gathered by experimental testing concrete cube specimens in the concrete technology laboratory of the Indian Institute of Technology Bhubaneswar for 188 different concrete mixes Several concrete mix proportions are tested for predicting the compressive strength of recycled aggregates concrete RCA For most of the mixes coarse
Testing the compressive strength of concrete using machine learning approaches has high importance for civil learning approaches provides high accuracy with reduced cost and time However such approaches require concrete composition data detailing the type and quantitative ratio of different materials such as water cement and
The concrete compressive strength is a highly nonlinear function of age and ingredients These ingredients include cement blast furnace slag fly ash water superplasticizer coarse aggregate and fine aggregate Partition the data into training and testing set 70 30 #Splitting data into train and test set inTrain
The methods were developed and tested against a data set derived from 17 concrete strength test laboratories The cross validation of unbiased estimates of the prediction models for performance comparison purposes indicated that multiple additive regression tree MART was superior in prediction accuracy training time and aversion to
Concrete compressive strength CCS is one of the most important parameters to determine the performance of concrete during service conditions The training set contains 834 data entries and the testing set contains 226 data entries The reason for splitting the dataset is because it tells how the trained model will perform on samples that
Analysis of the Concrete Compressive Strength dataset¶ A complete description of the dataset can be found here The objective of this notebook is to show how to use a pipeline to screen through the predictive power of out of the box machine learning algorithms Read and examine the data Preprocessing
Accurate prediction of concrete compressive strength based on explainable features using deep learning ：Ziyue Zeng（Key Laboratory of Advanced Civ…
A robust and reliable method to estimate the strength of concrete materials based on their mix parameters is required considering their extensive use in construction over the last few decades Consequently the relationship between the compressive strength of the concrete and its mixed components is highly nonlinear In this study artificial intelligence
A combination of careful experimental design proper sample preparation and standardized testing procedures is required for a reliable estimate of the compressive strength of concrete Compressive strength of concrete depends upon several factors including its age and proportion of different ingredients used to prepare it There is a complex
The compressive strength of concrete is extremely dependent on its mixing compositions [1] The amount of cement and supplementary cementitious materials SCMs the ratio of water to cement W/C the particle size of fine and coarse aggregates and the type and quantity of chemical admixtures are factors that affect the concrete compressive strength
Predicting the strength of concrete A material used in daily construction Predicting the strength of concrete A material used in daily construction Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic Learn more OK Got it Something went wrong and this page crashed
In general the compressive strength prediction was undertaken for several type of concrete ordinary concrete 8 10 high performance concrete 25 27 28 29 30 ultra high performance concrete
Analysis of the Concrete Compressive Strength dataset¶ A complete description of the dataset can be found here The objective of this notebook is to show how to use a pipeline to screen through the predictive power of out of the box machine learning algorithms Read and examine the data Preprocessing
The machine learning framework for predicting concrete strength comprises five fundamental stages as illustrated in Fig the data collection process involves preparing a series of
Scikit LearnUCI/ A Very Simple Machine Learning Case Based on Scikit Learn and UCI Concrete Compressive Strength Dataset kfs196/SimpleMLDemo Concrete
Concrete is one of the most common construction materials used all over the world Estimating the strength properties of concrete traditionally demands extensive laboratory experimentation However researchers have increasingly turned to predictive models to streamline this process This review focuses on predicting the compressive strength of self
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Fiber reinforced recycled aggregate concrete FR RAC has recently gained more popularity because of its advantages high strength eco friendliness and cost effectiveness This study uses an advanced machine learning technique for forecasting the compressive strength of FR RAC In this study an experimental database that contained pertinent data from
The process of concrete production involves mixing cement water and other materials The quantity of each of these materials results in a performance that is particularly estimated in terms of compressive or flexural strength It has been observed that the final performance of concrete has a high variance and that traditional formulation methods do not
Data set Nine already published multivariate variables namely cement blast furnace slag fly ash superplasticizer water coarse aggregate fine aggregate age and concrete compressive strength have been used The achieved prediction for the concrete compressive strength data by SVM against the observed data is presented in Fig 5
Engineers employ compressive strength a crucial criterion for concrete building construction This measurement method is frequently established by costly laboratory tests wasting time and money
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