Construction cost estimation of reinforced and prestressed concrete bridges using machine learning

Kovačević, Miljan; Despotović, Vladimir; Petronijević, Predrag

doi:10.14256/jce.2738.2019

Cited by 14 publications

(11 citation statements)

References 17 publications

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“…In the implementation of RF, it is recommended that the number of randomly selected variables be approximately equal to one-third of the total number of variables, which in most problems should lead to a satisfactory model. Such assumptions have been adopted in the program implementation in the MATLAB 2020a program (default settings) [21,25]. In this case, it would mean the adoption of models which generate regression trees using two or three variables out of the seven variables, which is not fully accepted in this study.…”

Section: Resultsmentioning

confidence: 99%

“…Splits in the model are only performed on these variables. If p is the total number of input variables or predictors in the training data, the algorithm will narrow the selection to m = p / 3 variables when selecting a new branching variable [21,25]. For each branching, a new random sample of variables is taken into consideration.…”

Section: Regression Tree Ensemblesmentioning

confidence: 99%

“…The optimal number of neurons in the hidden layer depends on many factors, including the number of inputs and outputs, the number of training pairs, the size of the noise in the training pairs, the complexity of the error function, network architecture, and training algorithm. One of the recommendations for adopting an upper limit for the number of hidden layer neurons N H is the following expression [16,25]:…”

Section: Artificial Neural Network (Anns)mentioning

confidence: 99%

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Application of Artificial Intelligence Methods for Predicting the Compressive Strength of Self-Compacting Concrete with Class F Fly Ash

Kovačević¹,

Lozančić

Nyarko

et al. 2022

Materials

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Replacing a specified quantity of cement with Class F fly ash contributes to sustainable development and reducing the greenhouse effect. In order to use Class F fly ash in self-compacting concrete (SCC), a prediction model that will give a satisfactory accuracy value for the compressive strength of such concrete is required. This paper considers a number of machine learning models created on a dataset of 327 experimentally tested samples in order to create an optimal predictive model. The set of input variables for all models consists of seven input variables, among which six are constituent components of SCC, and the seventh model variable represents the age of the sample. Models based on regression trees (RTs), Gaussian process regression (GPR), support vector regression (SVR) and artificial neural networks (ANNs) are considered. The accuracy of individual models and ensemble models are analyzed. The research shows that the model with the highest accuracy is an ensemble of ANNs. This accuracy expressed through the mean absolute error (MAE) and correlation coefficient (R) criteria is 4.37 MPa and 0.96, respectively. This paper also compares the accuracy of individual prediction models and determines their accuracy. Compared to theindividual ANN model, the more transparent multi-gene genetic programming (MGPP) model and the individual regression tree (RT) model have comparable or better prediction accuracy. The accuracy of the MGGP and RT models expressed through the MAE and R criteria is 5.70 MPa and 0.93, and 6.64 MPa and 0.89, respectively.

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Section: Resultsmentioning

confidence: 99%

Section: Regression Tree Ensemblesmentioning

confidence: 99%

Section: Artificial Neural Network (Anns)mentioning

confidence: 99%

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Application of Artificial Intelligence Methods for Predicting the Compressive Strength of Self-Compacting Concrete with Class F Fly Ash

Kovačević¹,

Lozančić

Nyarko

et al. 2022

Materials

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“…Over time, both nationally and internationally, the construction of bridges has steadily increased. On the eastern and southern branches of highway Corridor X in Serbia, contracts for the construction of about 200 reinforced concrete bridges totaling more than 100 million euros were signed from 2009 to 2015 [2,3].…”

Section: Introductionmentioning

confidence: 99%

Decision-Support System for Estimating Resource Consumption in Bridge Construction Based on Machine Learning

Kovačević¹,

Ivanišević

Stević³

et al. 2022

Axioms

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The paper presents and analyzes the state-of-the-art machine learning techniques that can be applied as a decision-support system in the estimation of resource consumption in the construction of reinforced concrete and prestressed concrete road bridges. The formed database on the consumption of concrete in the construction of bridges, along with their project characteristics, was the basis for the formation of the assessment model. The models were built using information from 181 reinforced concrete bridges in the eastern and southern branches of Corridor X in Serbia, with a value of more than 100 million euros. The application of artificial neural network models (ANNs), models based on regression trees (RTs), models based on support vector machines (SVM), and Gaussian processes regression (GPR) were analyzed. The accuracy of each model is determined by multi-criterion evaluation against four accuracy criteria root mean square error (RMSE), mean absolute error (MAE), Pearson’s linear correlation coefficient (R), and mean absolute percentage error (MAPE). According to all established criteria, the model based on GPR demonstrated the greatest accuracy in calculating the concrete consumption of bridges. According to the study, using automatic relevance determination (ARD) covariance functions results in the most accurate and optimal models and also makes it possible to see how important each input variable is to the model’s accuracy.

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“…To offer guidance on establishing the upper limit for neurons in the hidden layer, the research provides recommended expressions ( 16) and ( 17), with a preference indicated for smaller values. These recommendations (24), (25) serve as valuable insights for the meticulous optimization of the neural network architecture, ensuring its efficacy in predicting compressive strength [37,42]. The value of the parameter λ k (Mu) 0.005 1.00 × 10 10…”

mentioning

confidence: 99%

Application of Artificial Intelligence Methods for Predicting the Compressive Strength of Green Concretes with Rice Husk Ash

Kovačević,

Hadzima-Nyarko,

Grubeša

et al. 2023

Mathematics

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To promote sustainable growth and minimize the greenhouse effect, rice husk fly ash can be used instead of a certain amount of cement. The research models the effects of using rice fly ash as a substitute for regular Portland cement on the compressive strength of concrete. In this study, different machine-learning techniques are investigated and a procedure to determine the optimal model is provided. A database of 909 analyzed samples forms the basis for creating forecast models. The derived models are assessed using the accuracy criteria RMSE, MAE, MAPE, and R. The research shows that artificial intelligence techniques can be used to model the compressive strength of concrete with acceptable accuracy. It is also possible to evaluate the importance of specific input variables and their influence on the strength of such concrete.

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Construction cost estimation of reinforced and prestressed concrete bridges using machine learning

Cited by 14 publications

References 17 publications

Application of Artificial Intelligence Methods for Predicting the Compressive Strength of Self-Compacting Concrete with Class F Fly Ash

Application of Artificial Intelligence Methods for Predicting the Compressive Strength of Self-Compacting Concrete with Class F Fly Ash

Decision-Support System for Estimating Resource Consumption in Bridge Construction Based on Machine Learning

Application of Artificial Intelligence Methods for Predicting the Compressive Strength of Green Concretes with Rice Husk Ash

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