1,543 research outputs found
Predicting volumetric properties and mechanical characteristics of asphalt concretes for thin wearing layers using a categorical boosting model
No full textIn recent years, new modeling strategies based on data-driven approaches are gaining increasing popularity in the field of pavement engineering. This study is aimed at developing a novel predictive model based on a supervised categorical boosting (CatBoost) algorithm that allows volumetric properties and mechanical characteristics of asphalt concretes (ACs) for thin wearing layers to be simultaneously predicted. The research involved 92 AC specimens produced both in laboratory and in plant with two different types of bitumen: a conventional and a modified one. In particular, air voids content, voids in the mineral aggregate, and stiffness modulus at 20C were successfully correlated to bitumen content, particle size parameters and a categorical variable distinguishing the mixture production site and the binder type. The best model hyperparameters were accurately determined, and several performance metrics were evaluated to confirm the remarkable predictive capabilities achieved by the developed machine learning model
Stiffness modulus and marshall parameters of hot mix asphalts: Laboratory data modeling by artificial neural networks characterized by cross-validation
No full textThe present paper discusses the analysis and modeling of laboratory data regarding the
mechanical characterization of hot mix asphalt (HMA) mixtures for road pavements, by means of
artificial neural networks (ANNs). The HMAs investigated were produced using aggregate and
bitumen of different types. Stiffness modulus (ITSM) and Marshall stability (MS) and quotient (MQ)
were assumed as mechanical parameters to analyze and predict. The ANN modeling approach was
characterized by multiple layers, the k-fold cross validation (CV) method, and the positive linear
transfer function. The effectiveness of such an approach was verified in terms of the coeffcients of
correlation (R) and mean square errors; in particular, R values were within the range 0.965–0.919 in
the training phase and 0.881–0.834 in the CV testing phase, depending on the predicted parameters
Asphalt concrete mechanical behavior prediction by artificial neural networks
No full textThe current paper deals with the numerical prediction of the mechanical response of Asphalt Concretes (AC) for flexible pavements, using Artificial Neural Networks (ANN). The AC mixes considered in the study were consisted of diabase aggregates and two different types of bitumen; a conventional bituminous binder and a polymer modified one. The ACs were produced in the laboratory and in a production plant. The ACs mechanical behaviour was investigated in terms of Marshall Stability, Flow, Quotient and Stiffness Modulus. The ANN used had one hidden layer and 10 artificial neurons. The results have been extremely satisfactory, with correlation coefficients in the testing phase within the range 0.98798 – 0.91024, demonstrating the feasibility of ANN prediction models’ application. Furthermore, a closed form equation has been with input parameters the production process, the bitumen type and content, the filler/bitumen ratio and the volumetric properties of the mixes
Numerical characterization of high modulus asphalt concrete containing RAP: A comparison among optimized shallow neural models
No full textKnowing the relationship between the stiffness modulus and the empirical mechanical characteristics of asphalt concrete, road engineers may predict the expected results of costly laboratory tests and save both time and financial resources in the mix design phase. In fact, such a model would make it possible to assess a priori whether the stiffness of a specific mixture, characterised in the laboratory only by the common Marshall test, is suitable for the level of service required by the road pavement under analysis. In this study, 54 Marshall test specimens of high modulus asphalt concrete were prepared and tested in the laboratory to determine an empirical relationship between the stiffness modulus and Marshall stability by means of shallow artificial neural networks. Part out of these mixtures was characterised by different types of bitumen (20/30 or 50/70 penetration grade) and percentages of used reclaimed asphalt (RAP at 20% or 30%); a polymer modified bitumen was used in the preparation of the remaining Marshall test specimens, which do not contain RAP. For the complex and laborious identification of the neural model hyperparameters, which define its architecture and algorithmic functioning, the Bayesian optimization approach has been adopted. Although the results of this methodology depend on the predefined hyperparameters variability ranges, it allows an unbiased definition of the optimal neural model characteristics to be performed by minimizing (or maximizing) a loss function. In this study, the mean square error on 5 validation folds was used as a loss function, in order to avoid a poor performance evaluation due to the small number of samples. In addition, 3 different neural training algorithms were applied to compare results and convergence times. The procedure presented in this study is a valuable guide for the development of predictive models of asphalt concretes' behaviour, even for different types of bitumen and aggregates considered here
A machine learning approach for the prediction of very thin wearing layers asphalt concretes volumetric properties and performance
No full textIn recent years, many researchers in the field of pavement engineering have worked with the aim of developing a model capable of predicting the mechanical behavior of a mixture starting from its composition's parameters. This has been done following two different approaches. The first involved the use of advanced constitutive laws based on the materials mechanics; the second, instead of being physically based, was data-driven. The present work belongs to this second context and aims to present, implement and apply a strategy to develop the optimal model for solving an assigned predictive problem. Specifically, a Machine Learning approach, a Feedforward Backpropagation Shallow Neural Network, was investigated. The objective was to correlate stiffness modulus, air voids and voids in the mineral aggregate to the mixture main composition's parameters identified in: bitumen content, particle size and a categorical variable distinguishing the bitumen type and production site. Since the maximum aggregate size is 10 mm, the sieves considered were of 10, 6.3, 2, 0.5 and 0.063-mm diameters. The present study focused on 92 variants of asphalt concretes for very thin road pavement wearing layers produced both in plant and in laboratory. Despite the wide variation ranges of each parameter considered, the optimal model returns fully satisfactory performance. The overall Pearson correlation coefficient is equal to 0.9490, also by virtue of the innovative algorithms implemented as k-fold Cross-Validation (CV) and Bayesian Optimization (BO). These algorithms have allowed on the one hand the improvement of the model's predictive performance making them more reliable and, on the other hand, the optimization of hyperparameters and architecture. The methodology developed can become an important reference in this field since it is independent from the specific predictive application. In this sense, it can help other researchers in the fine-tuning of neural models in the field of pavement engineering
Road Pavement Asphalt Concretes for Thin Wearing Layers: A Machine Learning Approach towards Stiffness Modulus and Volumetric Properties Prediction
No full textIn this study a novel procedure is presented for an efficient development of predictive models of road pavement asphalt concretes mechanical characteristics and volumetric properties, using shallow artificial neural networks. The problems of properly assessing the actual generalization feature of a model and avoiding the effects induced by a fixed training-test data split are addressed. Since machine learning models require a careful definition of the network hyperparameters, a Bayesian approach is presented to set the optimal model configuration. The case study covered a set of 92 asphalt concrete specimens for thin wearing layers
Road Pavement Asphalt Concretes for Thin Wearing Layers: A Machine Learning Approach towards Stiffness Modulus and Volumetric Properties Prediction
No full textIn this study a novel procedure is presented for an efficient development of predictive models of road pavement asphalt concretes mechanical characteristics and volumetric properties, using shallow artificial neural networks. The problems of properly assessing the actual generalization feature of a model and avoiding the effects induced by a fixed training-test data split are addressed. Since machine learning models require a careful definition of the network hyperparameters, a Bayesian approach is presented to set the optimal model configuration. The case study covered a set of 92 asphalt concrete specimens for thin wearing layers
Asphalt mixtures’ stiffness modulus prediction using a machine-learning approach based on temperature and frequency conditions
No full textOne of the most suitable parameters to summarize the mechanical behaviour of asphalt mixtures is the stiffness modulus. Such performance parameter roughly describes the durability and serviceability provided by road pavements. However, it is strongly influenced by testing conditions, namely the loading frequency and the testing temperature. This study is aimed at investigating this relationship using a machine learning approach based on artificial neural networks. First, the physical and volumetric properties of the asphalt mixture under investigation were determined. Then, a 4-Point Bending Test experimental campaign was carried out and the stiffness modulus was evaluated under several testing conditions. Laboratory results were used to train a neural model that had temperature and frequency as inputs and the stiffness as output. The performance achieved was remarkable. Although the model is limited to only the mixture under investigation, this research is promising in view of an expanded dataset with multiple mixtures considered
Stiffness Data of High-Modulus Asphalt Concretes for Road Pavements: Predictive Modeling by Machine-Learning
No full textThis paper presents a study about a Machine Learning approach for modeling the stiffness of different high-modulus asphalt concretes (HMAC) prepared in the laboratory with harder paving grades or polymer-modified bitumen which were designed with or without reclaimed asphalt (RA) content. Notably, the mixtures considered in this study are not part of purposeful experimentation in support of modeling, but practical solutions developed in actual mix design processes. Since Machine Learning models require a careful definition of the network hyperparameters, a Bayesian optimization process was used to identify the neural topology, as well as the transfer function, optimal for the type of modeling needed. By employing different performance metrics, it was possible to compare the optimal models obtained by diversifying the type of inputs. Using variables related to the mix composition, namely bitumen content, air voids, maximum and average bulk density, along with a categorical variable that distinguishes the bitumen type and RAP percentages, successful predictions of the Stiffness have been obtained, with a determination coefficient (R2 ) value equal to 0.9909. Nevertheless, the use of additional input, namely the Marshall stability or quotient, allows the Stiffness prediction to be further improved, with R2 values equal to 0.9938 or 0.9922, respectively. However, the cost and time involved in the Marshall test may not justify such a slight prediction improvement
Bituminous mixtures experimental data modeling using a hyperparameters‐optimized machine learning approach
No full textThis study introduces a machine learning approach based on Artificial Neural Networks (ANNs) for the prediction of Marshall test results, stiffness modulus and air voids data of different bituminous mixtures for road pavements. A novel approach for an objective and semi‐automatic identification of the optimal ANN’s structure, defined by the so‐called hyperparameters, has been introduced and discussed. Mechanical and volumetric data were obtained by conducting laboratory tests on 320 Marshall specimens, and the results were used to train the neural network. The k‐fold Cross Validation method has been used for partitioning the available data set, to obtain an unbiased evaluation of the model predictive error. The ANN’s hyperparameters have been optimized using the Bayesian optimization, that overcame efficiently the more costly trial‐and‐error procedure and automated the hyperparameters tuning. The proposed ANN model is characterized by a Pearson coefficient value of 0.868
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