New machine learning-based prediction models for fracture energy of asphalt mixtures

Majidifard, Hamed; Jahangiri, Behnam; Buttlar, William G.; Alavi, Amir H.

doi:10.1016/j.measurement.2018.11.081

Cited by 97 publications

(37 citation statements)

References 59 publications

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“…Consequently, a further analysis of the literature was carried out. Relevant and recent examples of the ML (and hybrid methods that consist of the combination of ML algorithms and the methods reported above) used to carry out the SHM of road pavements refer to (a) 3D reconstruction of concealed cracks using convolutional neural network (CNN) [31], (b) prediction of condition and performance, definition of management and maintenance strategies, surface distress forecasting (cracking, rutting, raveling, and roughness), structural evaluation (layer thicknesses, moduli, shear wave velocity, and deflection), distress identification using image analysis and classification, materials modeling [32], (c) detection of structural damage using wavelet-based and Artificial Neural Networks ANN [33,34], (d) detection of crack propagation using finite element method and ANN [35], (e) detection and classification of pavement crack using CNN and principal component analysis [36], (f) vision-based classification of cracks using a feed-forward ANN [37], and (g) predict the fracture energy of asphalt mixture specimens using an innovative ML algorithms [38].…”

Section: State Of the Art About Technological Solutions Used To Detecmentioning

confidence: 99%

“…• Random forest classifier (RFC): an ensemble learning method for classification that operates by constructing a set of decision trees and returning the class that is the mode of the classes (classification) of the individual trees [38]; • Support vector classifier (SVC): a supervised learning classifier with associated learning algorithms that can perform a nonlinear classification, implicitly mapping the model inputs into high-dimensional feature spaces.…”

Section: The Machine Learning Classifiers Usedmentioning

confidence: 99%

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Detection and Monitoring of Bottom-Up Cracks in Road Pavement Using a Machine-Learning Approach

et al. 2020

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The current methods that aim at monitoring the structural health status (SHS) of road pavements allow detecting surface defects and failures. This notwithstanding, there is a lack of methods and systems that are able to identify concealed cracks (particularly, bottom-up cracks) and monitor their growth over time. For this reason, the objective of this study is to set up a supervised machine learning (ML)-based method for the identification and classification of the SHS of a differently cracked road pavement based on its vibro-acoustic signature. The method aims at collecting these signatures (using acoustic-sensors, located at the roadside) and classifying the pavement’s SHS through ML models. Different ML classifiers (i.e., multilayer perceptron, MLP, convolutional neural network, CNN, random forest classifier, RFC, and support vector classifier, SVC) were used and compared. Results show the possibility of associating with great accuracy (i.e., MLP = 91.8%, CNN = 95.6%, RFC = 91.0%, and SVC = 99.1%) a specific vibro-acoustic signature to a differently cracked road pavement. These results are encouraging and represent the bases for the application of the proposed method in real contexts, such as monitoring roads and bridges using wireless sensor networks, which is the target of future studies.

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Section: State Of the Art About Technological Solutions Used To Detecmentioning

confidence: 99%

Section: The Machine Learning Classifiers Usedmentioning

confidence: 99%

Detection and Monitoring of Bottom-Up Cracks in Road Pavement Using a Machine-Learning Approach

et al. 2020

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“…Hence, ML exhibits a great potential in assisting materials design and synthesis in the future with the ambitious goal of accelerated and application-tailored materials design and discovery [28]. Recently, ML is successfully employed to design organic light-emitting diodes [29], metal-organic frameworks [30], drugs [31], classify steel microstructures [32], construction materials [33] and in combination with computational materials to predict graphene bandgap [34]. A paradigm shift in the eld of material science is inevitable in the upcoming years as ML and deep learning (DL) becoming increasingly powerful.…”

Section: Introductionmentioning

confidence: 99%

Exploration of Carbon Nanotube Forest Synthesis-Structure Relationships Using Physics-Based Simulation and Machine Learning

Hajilounezhad¹,

Oraibi²,

Surya³

et al. 2020

Preprint

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The parameter space of CNT forest synthesis is vastand multidimensional, making experimental and/or numericalexploration of the synthesis prohibitive. We propose a morepractical approach to explore the synthesis-process relationshipsof CNT forests using machine learning (ML) algorithms toinfer the underlying complex physical processes. Currently, nosuch ML model linking CNT forest morphology to synthesisparameters has been demonstrated. In the current work, weuse a physics-based numerical model to generate CNT forestmorphology images with known synthesis parameters to trainsuch a ML algorithm. The CNT forest synthesis variablesof CNT diameter and CNT number densities are varied togenerate a total of 12 distinct CNT forest classes. Images of theresultant CNT forests at different time steps during the growthand self-assembly process are then used as the training dataset.Based on the CNT forest structural morphology, multiplesingle and combined histogram-based texture descriptors areused as features to build a random forest (RF) classifier topredict class labels based on correlation of CNT forest physicalattributes with the growth parameters. The machine learningmodel achieved an accuracy of up to 83.5% on predicting thesynthesis conditions of CNT number density and diameter.These results are the first step towards rapidly characterizingCNT forest attributes using machine learning. Identifying therelevant process-structure interactions for the CNT forests usingphysics-based simulations and machine learning could rapidlyadvance the design, development, and adoption of CNT forestapplications with varied morphologies and properties.

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“…Thus, the predictive models have been evolved to forecast the above-mentioned performance indicators of soil-structure systems. Conventional regression analysis has been of interest to many researchers for development of predictive functions in the geotechnical context [22,[24][25][26][27][28][29][30][31]. Soft computing techniques have also been used for prediction of SSI effects in general, and rocking responses in particular.…”

Section: Introductionmentioning

confidence: 99%

Estimation of rocking capacity of soil-structure systems using a hybrid inverse solver

et al. 2019

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Among the geotechnical-earthquake community, the rocking concept is being acknowledged as an energy dissipation mechanism that benefits soil-structure systems during strong vibrations. Nevertheless, several involving factors such as geomaterials behavior and superstructure size can make the rocking analysis of soil-foundation-structure systems complicated. The mobilized moment and the dissipated energy can be represented as the two primary performance indicators of the soil-structure systems under strong motions. This study employs an assembled database comprised of a wide range of geomaterials with different stiffness values associated with high-rise structures with different dimensions acquired from the implementation of a dynamic nonlinear elastic-perfect plastic finite element model. This study aims to develop predictive models for the responses mentioned above using the implemented database. To predict the both mobilized moment and dissipated energy, a hybrid gene-expression programming-artificial neural network technique (GEP-ANN) was used. The results show that the GEP model can yield promising predictions with reasonable accuracy. However, the GEP model can be fine-tuned by introducing the hybrid model. The hybrid model decreases the recorded prediction errors by a factor of three for both the mobilized moment and damping ratio as compared to the GEP model. The results show that the predictive models yield a sensible performance power that minimizes the efforts needed for implementation of time-consuming finite element method. This study also deploys a local sensitivity analysis technique to assess how the input parameters are attributed to the target values.

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New machine learning-based prediction models for fracture energy of asphalt mixtures

Cited by 97 publications

References 59 publications

Detection and Monitoring of Bottom-Up Cracks in Road Pavement Using a Machine-Learning Approach

Detection and Monitoring of Bottom-Up Cracks in Road Pavement Using a Machine-Learning Approach

Exploration of Carbon Nanotube Forest Synthesis-Structure Relationships Using Physics-Based Simulation and Machine Learning

Estimation of rocking capacity of soil-structure systems using a hybrid inverse solver

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