scholarly journals Prediction of Compressive Strength of Rice Husk Ash Concrete through Different Machine Learning Processes

Crystals ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 352
Author(s):  
Ammar Iqtidar ◽  
Niaz Bahadur Khan ◽  
Sardar Kashif-ur-Rehman ◽  
Muhmmad Faisal Javed ◽  
Fahid Aslam ◽  
...  
Keyword(s):  
Machine Learning ◽  
Compressive Strength ◽  
Rice Husk ◽  
Carbon Dioxide Emissions ◽  
Rice Husk Ash ◽  
Fuzzy Inference ◽  
Correlation Factor ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Data Points

Cement is among the major contributors to the global carbon dioxide emissions. Thus, sustainable alternatives to the conventional cement are essential for producing greener concrete structures. Rice husk ash has shown promising characteristics to be a sustainable option for further research and investigation. Since the experimental work required for assessing its properties is both time consuming and complex, machine learning can be used to successfully predict the properties of concrete containing rice husk ash. A total of 192 data points are used in this study to assess the compressive strength of rice husk ash blended concrete. Input parameters include age, amount of cement, rice husk ash, super plasticizer, water, and aggregates. Four soft computing and machine learning methods, i.e., artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS), multiple nonlinear regression (NLR), and linear regression are employed in this research. Sensitivity analysis, parametric analysis, and correlation factor (R2) are used to evaluate the obtained results. The ANN and ANFIS outperformed other methods.

scholarly journals Comparison of Machine Learning Approaches with Traditional Methods for Predicting the Compressive Strength of Rice Husk Ash Concrete

Crystals ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 779
Author(s):  
Muhammad Nasir Amin ◽  
Ammar Iqtidar ◽  
Kaffayatullah Khan ◽  
Muhammad Faisal Javed ◽  
Faisal I. Shalabi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Compressive Strength ◽  
Rice Husk ◽  
Rice Husk Ash ◽  
Harmful Effect ◽  
Partial Replacement ◽  
Learning Methods ◽  
Traditional Methods ◽  
Inference System ◽  
Machine Learning Methods

Efforts are being devoted to reducing the harmful effect of the construction industry around the globe, including the use of rice husk ash as a partial replacement of cement. However, no method is available to date to predict the compressive strength (CS) of rice husk ash blended concrete (RHAC). In this study, advanced machine learning techniques (artificial neural network, artificial neuro-fuzzy inference system) were used to predict the CS of RHAC. Based on the published literature, six inputs, i.e., age of specimen, percentage of rice husk ash, percentage of superplasticizer, aggregates, water, and amount of cement, were selected. Results obtained from machine learning methods were compared with traditional methods such as linear and non-linear regressions. It was observed that the performance of machine learning methods was superior to traditional methods for determining the CS of RHAC. This study will prove beneficial in minimizing the cost and time of executing laboratory experiments for designing the optimum content portions of RHAC.

A two-stage methodology based on ensemble Adaptive Neuro-Fuzzy Inference System to predict carbon dioxide emissions

2019 ◽  
Vol 231 ◽  
pp. 446-461 ◽  
Author(s):  
Abbas Mardani ◽  
Yee Van Fan ◽  
Mehrbakhsh Nilashi ◽  
Robert E. Hooker ◽  
Seckin Ozkul ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Fuzzy Inference System ◽  
Carbon Dioxide Emissions ◽  
Fuzzy Inference ◽  
Two Stage ◽  
Inference System ◽  
Neuro Fuzzy

scholarly journals Prediction of vitrinite reflectance values using machine learning techniques: a new approach

2020 ◽  
Author(s):  
Zahra Sadeghtabaghi ◽  
Mohsen Talebkeikhah ◽  
Ahmad Reza Rabbani
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Low Cost ◽  
Vitrinite Reflectance ◽  
Empirical Equations ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Rock Eval

AbstractVitrinite reflectance (VR) is considered the most used maturity indicator of source rocks. Although vitrinite reflectance is an acceptable parameter for maturity and is widely used, it is sometimes difficult to measure. Furthermore, Rock-Eval pyrolysis is a current technique for geochemical investigations and evaluating source rock by their quality and quantity of organic matter, which provide low cost, quick, and valid information. Predicting vitrinite reflectance by using a quick and straightforward method like Rock-Eval pyrolysis results in determining accurate and reliable values of VR with consuming low cost and time. Previous studies used empirical equations for vitrinite reflectance prediction by the Tmax data, which was accompanied by poor results. Therefore, finding a way for precise vitrinite reflectance prediction by Rock-Eval data seems useful. For this aim, vitrinite reflectance values are predicted by 15 distinct machine learning models of the decision tree, random forest, support vector machine, group method of data handling, radial basis function, multilayer perceptron, adaptive neuro-fuzzy inference system, and multilayer perceptron and adaptive neuro-fuzzy inference system, which are coupled with evolutionary optimization methods such as grasshopper optimization algorithm, bat algorithm, particle swarm optimization, and genetic algorithm, with four inputs of Rock-Eval pyrolysis parameters of Tmax, S1/TOC, HI, and depth for the first time. Statistical evaluations indicate that the decision tree is the most precise model for VR prediction, which can estimate vitrinite reflectance precisely. The comparison between the decision tree and previous proposed empirical equations indicates that the machine learning method performs much more accurately.

Prediction of Concrete Strength Using Fuzzy Neural Networks

2011 ◽  
Vol 243-249 ◽  
pp. 6121-6126 ◽  
Author(s):  
Jing Xu ◽  
Xiu Li Wang
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Concrete Strength ◽  
Fuzzy Neural Networks ◽  
Output Data ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Fuzzy Neural

The purpose of this paper is to develop the Ⅰ-PreConS (Intelligent PREdiction system of CONcrete Strength) that predicts the compressive strength of concrete to improve the accuracy of concrete undamaged inspection. For this purpose, the system is developed with adaptive neuro－fuzzy inference system (ANFIS) that can learn cube test results as training patterns. ANFIS does not need a specific equation form differ from traditional prediction models. Instead of that, it needs enough input-output data. Also, it can continuously re-train the new data, so that it can conveniently adapt to new data. In the study, adaptive neuro－fuzzy inference system (ANFIS) based on Takagi-Sugeno rules is built up to prediction concrete strength. According to the expert experience, the relationship between the rebound value and concrete strength tends to power function. So the common logarithms of rebound value and strength value are used as the inputs and outputs of the ANFIS. System parameter sets are iteratively adjusted according to input and output data samples by a hybrid-learning algorithm. In the system, in order to improve of the ANFIS, condition parameter sets can be determined by the back propagation gradient descent method and conclusion parameter sets can be determined by the least squares method. As a result, the concrete strength can be inferred by the fuzzy inference. The method takes full advantage of the characteristics of the abilities of Fuzzy Neural Networks (FNN) including automatic learning, generation and fuzzy logic inference. The experiment shows that the average relative error of the predicted results is 10.316% and relative standard error is 12.895% over all the 508 samples, which are satisfied with the requirements of practical engineering. The ANFIS-based model is very efficient for prediction the compressive strength of in-service concrete.

Sign in / Sign up

Export Citation Format

Share Document