Tree Based Approaches for Predicting Concrete Carbonation Coefficient

Londhe, Shreenivas; Kulkarni, P. S.; Dixit, Pradnya; Silva, Ana; Neves, R.; Brito, Jorge de

doi:10.3390/app12083874

Cited by 5 publications

(4 citation statements)

References 38 publications

(77 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The timestep extended up to the service life of the bridge. For instance, a bridge with a service life of 30 years was created with an input size of (15,30), with 15 features and 30 timesteps. However, because the service life for each dataset was different, the size of the input dataset differed for each bridge.…”

Section: Lstm-based Methodology For Generating the Carbonation Modelmentioning

confidence: 99%

See 1 more Smart Citation

Long Short-Term Memory-Based Methodology for Predicting Carbonation Models of Reinforced Concrete Slab Bridges: Case Study in South Korea

et al. 2022

View full text Add to dashboard Cite

Reinforced concrete slab (RCS) bridges deteriorate because of exposure to environmental factors over time, resulting in reduced durability. Particularly, the carbonation of RCS bridges corrodes the rebars and reduces the strength. However, carbonation models derived from short-term experiments exhibit low reliability with respect to existing bridges. Therefore, a long short-term memory (LSTM)-based methodology was developed in this study for generating carbonation models using existing bridge inspection reports. The proposed methodology trains the LSTM model by combining data extracted from reports and local environmental data. The learning process uses padding and masking methods to consider the history of environmental data. A case study was performed to validate the proposed method in three different regions of Korea. The results verified that the coefficient of determination of the proposed method was higher than those of the existing carbonation models and other regression analyses. Therefore, the developed methodology can be used for predicting regional carbonation models using the data from existing bridges.

show abstract

Section: Lstm-based Methodology For Generating the Carbonation Modelmentioning

confidence: 99%

“…Sci. 2022, 12, 12470 3 of 15 on Fick's second law, which states that the depth of concrete carbonization is proportional to the square root of time, as indicated in Equation (1) [28][29][30].…”

Section: Concrete Carbonation Modelmentioning

confidence: 99%

Long Short-Term Memory-Based Methodology for Predicting Carbonation Models of Reinforced Concrete Slab Bridges: Case Study in South Korea

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Wu et al [86] developed a prediction model using the RF algorithm, established training and testing sets based on raw data, and successfully predicted concrete carbonation depth. Similarly, Londhe et al [87] employed Model Tree, RF, and MGGP methods to predict the carbonation coefficient of concrete. These studies demonstrate the efficacy of the RF algorithm as a powerful tool for machine learning and its application in predicting concrete-related properties.…”

Section: Dt-based Prediction Modelmentioning

confidence: 99%

A Review of Concrete Carbonation Depth Evaluation Models

Wang,

Yang,

Peng

et al. 2024

Coatings

View full text Add to dashboard Cite

Carbonation is one of the critical issues affecting the durability of reinforced concrete. Evaluating the depth of concrete carbonation is of great significance for ensuring the quality and safety of construction projects. In recent years, various prediction algorithms have been developed for evaluating concrete carbonation depth. This article provides a detailed overview of the existing prediction models for concrete carbonation depth. According to the data processing methods used in the model, the existing prediction models can be divided into mathematical curve models and machine learning models. The machine learning models can be further divided into the following categories: artificial neural network model, decision tree model, support vector machine model, and combined models. The basic idea of the mathematical curve model is to directly establish the relationship between the carbonation depth and age of concrete by using certain function curves. The advantage of the mathematical curve model is that only a small amount of experimental data is needed for curve fitting, which is very convenient for engineering applications. The limitation of the curve model is that it can only consider the influence of some factors on the carbonation depth of concrete, and the prediction accuracy cannot be guaranteed. The advantage of using the machine learning model to predict the carbonation depth of concrete is that many factors can be considered at the same time. When there are sufficient experimental data, the trained machine learning model can give more accurate prediction results than the mathematical curve model. The main defect of the machine learning model is that it needs a lot of experimental data as training samples, so it is not as convenient as the mathematical curve model in engineering applications. A future research direction may be to combine a machine learning model with a mathematical curve model to evaluate the carbonation depth of concrete more accurately.

show abstract

“…It can be seen that in Set 1-1 and 1-3, all the parameters are considered in the equation also depicting their importance and respective contribution as MGGP has a unique characteristic in which the parameter that contribute significantly to prediction are considered while other is removed (as seen in Fig. analysis chart) in predicting the DO [48]. Hence both models 1-1 and 1-3 shown a higher performance and good correlation of all parameters with DO and thus has been considered in the study.…”

Section: Do Modelsmentioning

confidence: 99%

Modelling dissolved oxygen and biochemical oxygen demand using data-driven techniques

Sahu¹,

Londhe²,

Kulkarni³

2022

Environmental Engineering Research

View full text Add to dashboard Cite

Precise quantification of Biochemical oxygen demand (BOD) and Dissolved oxygen (DO) are critically important for water quality assessment as well as for development of various management policies. To calculate BOD and DO for any water sample, standard technique Winkler-Azide method is used which is cumbersome and prone to measurement error. Therefore, there is a need to device alternate Data Driven Technique (DDT). In the present study, three different DDT: Artificial Neural Network (ANN), Multi Gene Genetic Programming (MG-GP) and M5 Model Tree (M5T) have been used for DO as well as BOD prediction for 3 separate stretches of Mula-Mutha River situated in Pune, India. Additionally, attempt has been made to predict BOD using modelled DO; which shows possibility of using modelled parameter in development of another model. Performance of the models was assessed through, root mean square error (RMSE); mean absolute relative error (MARE) and coefficient of correlation (R). Results based on 3 stations indicate that ANN and MGGP both outperformed with R above 0.85 and RMSE below 1 mg/L for 2 stations out of 3. MGGP and M5T can grasp the influence parameter which can be seen from the input frequency distribution in MGGP and coefficient of input parameters in M5T.

show abstract

Tree Based Approaches for Predicting Concrete Carbonation Coefficient

Cited by 5 publications

References 38 publications

Long Short-Term Memory-Based Methodology for Predicting Carbonation Models of Reinforced Concrete Slab Bridges: Case Study in South Korea

Long Short-Term Memory-Based Methodology for Predicting Carbonation Models of Reinforced Concrete Slab Bridges: Case Study in South Korea

A Review of Concrete Carbonation Depth Evaluation Models

Modelling dissolved oxygen and biochemical oxygen demand using data-driven techniques

Contact Info

Product

Resources

About