Mixture Optimization of Recycled Aggregate Concrete Using Hybrid Machine Learning Model

Nunez, Itzel; Marani, Afshin; Nehdi, Moncef L.

doi:10.3390/ma13194331

Cited by 65 publications

(20 citation statements)

References 94 publications

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“…In 2020, Nunez I. et al [ 41 ] presented a study in which they built a machine learning model to predict the recycled aggregate concrete compressive strength and optimize the concrete mix design process. A reliable optimization method for concrete mix design is especially significant for recycled aggregate concrete, due to its variability and lack of proper compressive strength estimation formulas.…”

Section: Concrete MIX Design and Machine Learningmentioning

confidence: 99%

Model-Based Adaptive Machine Learning Approach in Concrete Mix Design

2021

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Concrete mix design is one of the most critical issues in concrete technology. This process aims to create a concrete mix which helps deliver concrete with desired features and quality. Contemporary requirements for concrete concern not only its structural properties, but also increasingly its production process and environmental friendliness, forcing concrete producers to use both chemically and technologically complex concrete mixtures. The concrete mix design methods currently used in engineering practice are joint analytical and laboratory procedures derived from the Three Equation Method and do not perform well enough for the needs of modern concrete technology. This often causes difficulties in predicting the final properties of the designed mix and leads to precautionary oversizing of concrete properties for fear of not providing the required parameters. A new approach that would make it possible to predict the newly designed concrete mix properties is highly desirable. The answer to this challenge can be methods based on machine learning, which have been intensively developed in recent years, especially in predicting concrete compressive strength. Machine learning-based methods have been more or less successful in predicting concrete compressive strength, but they do not reflect well the variability that characterises the currently used concrete mixes. A new adaptive solution that allows estimating concrete compressive strength on the basis of the concrete mix main ingredient composition by including two observations for a given batch of concrete is proposed herein. In presented study, a machine learning model was built with a deep neural network architecture, trained on an extensive database of concrete recipes, and translated into a mathematical formula. Testing on four concrete mix recipes was performed, which were calculated according to contemporary design methods (Bolomey and Fuller method), and a comparative analysis was conducted. It was found out that the new algorithm performs significantly better than that without adaptive features trained on the same dataset. The presented algorithm can be used as a concrete strength checking tool for the concrete mix design process.

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Section: Concrete MIX Design and Machine Learningmentioning

confidence: 99%

Model-Based Adaptive Machine Learning Approach in Concrete Mix Design

2021

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“…In the last few years, many researchers have developed different prediction models to estimate the compressive strength of eco-friendly concrete [ 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ]. To build a prediction model for the CS that contains RAC, two types of hybridized machine learning methods (an interval type-2 fuzzy inference system (IT2FIS) and type-1 fuzzy inference system (T1FIS)) were used [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

“…The results reveal that the suggested ICA-XGBoost model outperformed the other models. In this study [ 17 ], RAC compressive strength and its optimal mixture design were predicted by machine learning models. The results demonstrated that the generated models, including deep learning, Gaussian processes, and gradient boosting regression, obtained reliable predictive performance, with the gradient-boosting regression trees outperforming the others.…”

Section: Introductionmentioning

confidence: 99%

Development of Prediction Model to Predict the Compressive Strength of Eco-Friendly Concrete Using Multivariate Polynomial Regression Combined with Stepwise Method

et al. 2022

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Concrete is the most widely used building material, but it is also a recognized pollutant, causing significant issues for sustainability in terms of resource depletion, energy use, and greenhouse gas emissions. As a result, efforts should be concentrated on reducing concrete’s environmental consequences in order to increase its long-term viability. In order to design environmentally friendly concrete mixtures, this research intended to create a prediction model for the compressive strength of those mixtures. The concrete mixtures that were used in this study to build our proposed prediction model are concrete mixtures that contain both recycled aggregate concrete (RAC) and ground granulated blast-furnace slag (GGBFS). A white-box machine learning model known as multivariate polynomial regression (MPR) was developed to predict the compressive strength of eco-friendly concrete. The model was compared with the other two machine learning models, where one is also a white-box machine learning model, namely linear regression (LR), and the other is the black-box machine learning model, which is a support vector machine (SVM). The newly suggested model shows robust estimation capabilities and outperforms the other two models in terms of R2 (coefficient of determination) and RMSE (root mean absolute error) measurements.

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“…In this way, the input dataset is split in two groups during the learning process (training and validation datasets), allowing the validation dataset to evaluate the DL model after its learning process on the retained input dataset in order to give more robustness to the DL model in its later use in similar input dataset and same target task (i.e., to avoid overfitting). However, in unsupervised learning, the DL model must be able to find natural groupings or structures within the input dataset (in a process known as clustering) or to reduce the input dataset dimensionality (procedure known as dimensionality reduction) without example input–output pairs [ 35 , 36 ]. Then, the task assigned is more specific when it is defined as supervised learning (e.g., estimating house prices from the values of surface areas, year of construction and location; or classifying images according to the animal species contained from a dataset acquired in a protected natural space), and more general when it is defined as unsupervised learning (e.g., grouping the DNA sequence of different patients in different categories in order to detect some genetic abnormality; or reducing the information contained in a point cloud for the elimination of noise and thus obtain a better 3D representation of the environment under study).…”

Section: Introductionmentioning

confidence: 99%

Introduction of Deep Learning in Thermographic Monitoring of Cultural Heritage and Improvement by Automatic Thermogram Pre-Processing Algorithms

Garrido

Erazo-Aux

Lagüela

et al. 2021

Sensors

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The monitoring of heritage objects is necessary due to their continuous deterioration over time. Therefore, the joint use of the most up-to-date inspection techniques with the most innovative data processing algorithms plays an important role to apply the required prevention and conservation tasks in each case study. InfraRed Thermography (IRT) is one of the most used Non-Destructive Testing (NDT) techniques in the cultural heritage field due to its advantages in the analysis of delicate objects (i.e., undisturbed, non-contact and fast inspection of large surfaces) and its continuous evolution in both the acquisition and the processing of the data acquired. Despite the good qualitative and quantitative results obtained so far, the lack of automation in the IRT data interpretation predominates, with few automatic analyses that are limited to specific conditions and the technology of the thermographic camera. Deep Learning (DL) is a data processor with a versatile solution for highly automated analysis. Then, this paper introduces the latest state-of-the-art DL model for instance segmentation, Mask Region-Convolution Neural Network (Mask R-CNN), for the automatic detection and segmentation of the position and area of different surface and subsurface defects, respectively, in two different artistic objects belonging to the same family: Marquetry. For that, active IRT experiments are applied to each marquetry. The thermal image sequences acquired are used as input dataset in the Mask R-CNN learning process. Previously, two automatic thermal image pre-processing algorithms based on thermal fundamentals are applied to the acquired data in order to improve the contrast between defective and sound areas. Good detection and segmentation results are obtained regarding state-of-the-art IRT data processing algorithms, which experience difficulty in identifying the deepest defects in the tests. In addition, the performance of the Mask R-CNN is improved by the prior application of the proposed pre-processing algorithms.

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Mixture Optimization of Recycled Aggregate Concrete Using Hybrid Machine Learning Model

Cited by 65 publications

References 94 publications

Model-Based Adaptive Machine Learning Approach in Concrete Mix Design

Model-Based Adaptive Machine Learning Approach in Concrete Mix Design

Development of Prediction Model to Predict the Compressive Strength of Eco-Friendly Concrete Using Multivariate Polynomial Regression Combined with Stepwise Method

Introduction of Deep Learning in Thermographic Monitoring of Cultural Heritage and Improvement by Automatic Thermogram Pre-Processing Algorithms

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