Investigating the effects of ensemble and weight optimization approaches on neural networks’ performance to estimate the dynamic modulus of asphalt concrete

Huang, Jiandong; Zhang, Jia; Li, Xin; Qiao, Yaning; Zhang, Runhua; Kumar, G. Shiva

doi:10.1080/14680629.2022.2112061

Cited by 39 publications

(23 citation statements)

References 53 publications

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“…The results show that the developed model can successfully estimate the E* with better performance than the Witczak models. Huang et al [23] hybridized the ensemble and weight optimization approaches with an artificial neural network (ANN) algorithm to develop an asphalt concrete E* prediction model. The ρ200, V beff , binder G* (dynamic shear modulus) and binder δ (phase angle) were the variables of the developed model.…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic Modulus Prediction Validation for the AASHTOWare Pavement ME Design Implementation in Egypt

Saudy,

Breakah,

El-Badawy

2023

Sustainability

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Dynamic Modulus, E* is a crucial property of the hot mix asphalt (HMA). For the AASHTOWare Pavement ME design, E* is an essential material input. E* can be measured in the laboratory or predicted using different models based on some fundamental properties of the HMA. The NCHRP 1-37A and NCHRP 1-40D prediction models are the two main models adopted by the AASHTOWare to predict the E* based on the HMA mixture volumetrics, gradation, and binder properties. The main objective of this research was to validate these two prediction models using local HMA mixes for the purpose of the regional application of the AASHTOWare Pavement ME design in Egypt. For this purpose, the E* values of ten locally plant-produced HMA mixes were measured in the laboratory. The two E* prediction models were then used to estimate the E* values for the same materials. Consequently, the performance of both models was studied by comparing the measured values to the estimated values. The results showed that the NCHRP 1-40D prediction model can satisfactorily predict the E* of the Egyptian HMA mixes with minimal bias and high accuracy. The model yielded an adjusted coefficient of determination (R2) of 0.86 based on 480 E* measurements. On the other hand, the NCHRP 1-37A prediction accuracy was not satisfactory, with very poor accuracy (Adjusted R2 = 0.18) and high bias. Afterwards, the effect of the predicted E* from the NCHRP 1-40D model on the AASHTOWare Pavement ME predicted pavement performance in terms of rutting, cracking, and roughness was further studied. Accordingly, twenty-four simulation runs for typical Egyptian design cases were conducted using, first, the laboratory measured E* values and, then, the NCHRP 1-40D predicted E* values. The results showed that the NCHRP 1-40D predictions had no significant effect on the pavement performance predicted by the AASHTOWare Pavement ME with R2 of the different pavement distresses ranged from 0.980, for the AC rutting, to 0.9996 for the International Roughness Index (IRI). Hence, the NCHRP 1-40D model can be used satisfactorily to predict E* for the Egyptian HMA mixes without compromising the structural pavement design.

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Section: Literature Reviewmentioning

confidence: 99%

Dynamic Modulus Prediction Validation for the AASHTOWare Pavement ME Design Implementation in Egypt

Saudy,

Breakah,

El-Badawy

2023

Sustainability

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“…With the development of artificial intelligence, machine learning models have attracted more and more attention from civil engineers because of their high prediction accuracy and high prediction efficiency [31,32], and have been successfully applied to the prediction of concrete strength [33][34][35][36][37]. Hoang et al [38] proposed using Gaussian Process Regression (GPR) to simulate the mechanical properties of high-performance concrete (HPC) and compared the prediction effect of GPR with the prediction models of least squares support vector machines and artificial neural networks.…”

Section: Introductionmentioning

confidence: 99%

Potential of Repurposing Recycled Concrete for Road Paving: Flexural Strength (FS) Modeling by a Novel Systematic and Evolved RF-FA Model

Gu¹,

Shen

Pang

et al. 2023

Sustainability

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Concrete can be recycled after certain processing technologies for use in pavement engineering but the flexural strength (FS) is difficult to predict accurately in the design process. This study proposes a novel systematic and evolved approach to estimate the FS of recycled concrete. The proposed methods are conducted based on the random forest (RF) model as well as the firefly algorithm (FA), where the latter is employed to tune the hyperparameters of the RF model. For this purpose, data sets were collected from previously published literature for the training and verification of the model, and the accuracy of the model was verified by the fitting effect of the predicted and actual values. The results showed that the proposed hybrid machine learning model has a good fitting effect on the predicted and actual values; the calculation and evaluation process demonstrated fast convergence and significantly lower values of RMSE for the proposed model to determine the FS of the recycling concrete. In addition, the study analyzed the sensitivity of the FS of recycled concrete to input variables, and the results showed that effective water-cement ratio (WC), water absorption of recycling concrete (WAR), and water absorption of natural aggregate (WAN) show more obvious influences on FS, so these factors should be paid more attention in future pavement design using the recycling of concrete.

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“…ML models can accurately model the objectives of cementitious materials without knowing the explicit relationships between the objectives and the input variables [ 8 ], which is superior to conventionally used linear or nonlinear regression models that rely highly on the coefficients of the models [ 9 , 10 ]. Currently, the widely used ML models for modeling objectives include the Artificial Neural Network (ANN), Support Vector Machine (SVM), and tree-based models, such as Decision Tree (DT), Random Forest (RF), and Gradient Boosted Regression Tree (GBRT) [ 11 , 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Mixture Optimization of Cementitious Materials Using Machine Learning and Metaheuristic Algorithms: State of the Art and Future Prospects

Song¹,

Wang

Li³

et al. 2022

Materials

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The hybrid optimization of modern cementitious materials requires concrete to meet many competing objectives (e.g., mechanical properties, cost, workability, environmental requirements, and durability). This paper reviews the current literature on optimizing mixing ratios using machine learning and metaheuristic optimization algorithms based on past studies on varying methods. In this review, we first discuss the conventional methods for mixing optimization of cementitious materials. Then, the problem expression of hybrid optimization is discussed, including decision variables, constraints, machine learning algorithms for modeling objectives, and metaheuristic optimization algorithms for searching the best mixture ratio. Finally, we explore the development prospects of this field, including, expanding the database by combining field data, considering more influencing variables, and considering more competitive targets in the production of functional cemented materials. In addition, to overcome the limitation of the swarm intelligence-based multi-objective optimization (MOO) algorithm in hybrid optimization, this paper proposes a new MOO algorithm based on individual intelligence (multi-objective beetle antenna search algorithm). The development of computationally efficient robust MOO models will continue to make progress in the field of hybrid optimization. This review is adapted for engineers and researchers who want to optimize the mixture proportions of cementitious materials using machine learning and metaheuristic algorithms.

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Investigating the effects of ensemble and weight optimization approaches on neural networks’ performance to estimate the dynamic modulus of asphalt concrete

Cited by 39 publications

References 53 publications

Dynamic Modulus Prediction Validation for the AASHTOWare Pavement ME Design Implementation in Egypt

Dynamic Modulus Prediction Validation for the AASHTOWare Pavement ME Design Implementation in Egypt

Potential of Repurposing Recycled Concrete for Road Paving: Flexural Strength (FS) Modeling by a Novel Systematic and Evolved RF-FA Model

Mixture Optimization of Cementitious Materials Using Machine Learning and Metaheuristic Algorithms: State of the Art and Future Prospects

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