1,720,963 research outputs found
Volumetric Properties and Stiffness Modulus of Asphalt Concrete Mixtures Made with Selected Quarry Fillers: Experimental Investigation and Machine Learning Prediction
Full text linkIn recent years, the attention of many researchers in the field of pavement engineering has focused on the search for alternative fillers that could replace Portland cement and traditional limestone in the production of asphalt mixtures. In addition, from a Czech perspective, there was the need to determine the quality of asphalt mixtures prepared with selected fillers provided by different local quarries and suppliers. This paper discusses an experimental investigation and a machine learning modeling carried out by a decision tree CatBoost approach, based on experimentally determined volumetric and mechanical properties of fine-grained asphalt concretes prepared with selected quarry fillers used as an alternative to traditional limestone and Portland cement. Air voids content and stiffness modulus at 15 °C were predicted on the basis of seven input variables, including bulk density, a categorical variable distinguishing the aggregates’ quarry of origin, and five main filler-oxide contents determined by means of X-ray fluorescence spectrometry. All mixtures were prepared by fixing the filler content at 10% by mass, with a bitumen content of 6% (PG 160/220), and with roughly the same grading curve. Model predictive performance was evaluated in terms of six different evaluation metrics with Pearson correlation and coefficient of determination always higher than 0.96 and 0.92, respectively. Based on the results obtained, this study could represent a forward feasibility study on the mathematical prediction of the asphalt mixtures’ mechanical behavior on the basis of its filler mineralogical composition
Foamed Bitumen Mixtures for Road Construction Made with 100% Waste Materials: A Laboratory Study
Full text linkNowadays, budget restrictions for road construction, management, and maintenance require innovative solutions to guarantee the user acceptable service levels respecting environmental requirements. Such goals can be achieved by the re-use of various waste materials at the end of their service life in the pavement structure, therefore avoiding their disposal in landfill. At the same time, significant savings are achieved on natural aggregate by replacing it with such waste materials, improving the economic and environmental sustainability of road constructions. The purpose of this study is to discuss a laboratory investigation about foamed bitumen-stabilized mixtures for road foundation layers, in which the aggregate structure was entirely made up of industrial by-products and civil wastes, namely metallurgical slags such as electric arc furnace (EAF) and ladle furnace (LF) slags, coal fly (CF) ash, bottom ash from municipal solid waste incineration (MSWI), glass waste (GW) and reclaimed asphalt pavement (RAP). Combining these recycled aggregates in different proportions, six foamed bitumen mixtures were produced and investigated in terms of indirect tensile strength, stiffness modulus, and fatigue resistance. The leaching test carried out on the waste materials considered did not show any toxicological issue and the best foamed bitumen mixture's composition was characterized by 20% of EAF slags, 10% of LF slags, 20% of MSWI ash, 10% of CF ash, 20% of GW, and 20% of RAP. Its mechanical characterization presented a dry indirect tensile strength at 25 degrees C of 0.62 MPa (well above the Italian technical acceptance limits), a stiffness modulus at 25 degrees C equal to 6171 MPa, and a number of cycles to failure at 20 degrees C equal to 6989 for a stress level of 300 kPa
Multivariate Regression of Road Segments' Accident Data in Italian Rural Networks
No full textIncreasing traffic flows on road infrastructures and the associated comfort and safety problems have led to an increased risk of accidents for road users. To take the proper corrective actions, it is fundamental to analyze the accident phenomenon in all its aspects. The purpose of the current paper was the development of an accident prediction model for rural road segments of Friuli-Venezia Giulia (FVG) Region. The model predicts the accident frequency as a function of Annual Average Daily Traffic (AADT), segment length, and both geometrical and environmental features related to the targeted road segment. The procedure is based on the Empirical Bayes (EB) method. The statistical model used to express the road segments' safety was the multivariate regression structure of the Safety Performance Functions. Results of a CURE plots analysis verified that the model is highly reliable in predicting the accident dataset for AADT up to 12500 vehicles per day
A Machine Learning Approach for the Simultaneous Prediction of Dynamic Modulus and Phase Angle of Asphalt Concrete Mixtures
No full textRoad pavements represent the backbone of every road network. Asphalt concrete (AC) mixtures are the main technological solution for road pavement construction. Their composition must be optimized to ensure adequate structural and functional performance. One of the most reliable parameters for the characterization of AC mixtures’ viscoelastic behavior is called complex modulus. Such a stiffness property is crucial in the evaluation of pavements’ mechanical performance. The complex modulus is usually described in terms of dynamic modulus and phase angle and, to be determined, long and expensive experimental campaigns must be carried out. An interesting alternative is represented by machine learning models that could provide fast and reliable predictions if properly trained on meaningful datasets. In this paper, the results of an extensive 4-point bending test laboratory investigation are thoroughly discussed and an up-to-date artificial neural network (ANN) methodology is outlined to simultaneously predict the dynamic modulus and the phase angle of nine different AC mixtures. To summarize the performance achieved by the developed model, six different metrics were evaluated. The empirical Witczak 1-37A equation, a well-established regression model, was used as a reference to compare the performance obtained by the neural modeling in terms of dynamic modulus. Machine learning predictions showed remarkable accuracy, outperforming regression-based ones with respect to all the evaluation metrics used. Both in terms of dynamic modulus and phase angle, Pearson correlation coefficients and coefficients of determination achieved by the ANN model were higher than 0.98, resulting in a powerful and reliable predictive tool
Improved predictions of asphalt concretes’ dynamic modulus and phase angle using decision-tree based categorical boosting model
Full text linkThe most suitable parameter to summarize the viscoelastic response of asphalt concrete (AC) mixtures is the complex modulus, defined by means of its two main components: the dynamic modulus E∗ and the phase angle φ. They are frequently determined by means of expensive and time-consuming laboratory procedures that require suitable equipment and high-skilled technicians. As an alternative, machine learning models can be trained to make very accurate predictions and thus, substitute at least some of these lab tests. This study proposes an innovative Categorical Boosting (CatBoost) approach for the simultaneous prediction of both E∗ and φ. Nine different AC mixtures were prepared, and an extensive 4-point bending test (4PBT) experimental campaign was carried out under ten loading frequencies and six testing temperatures. In order to thoroughly compare the developed model with two well-established empirical equations (Witczak-Fonseca and Witczak 1–37A), the same input features were selected. Pre-processing and resampling techniques were implemented to both reduce computational effort and improve model efficiency, whereas an in-depth sensitivity analysis was also performed. The entire methodology was implemented in Python 3.8.5. Six different goodness-of-fit metrics were used to robustly evaluate the performance of the developed CatBoost model and to compare it with the results of two regression-based models and a reference state-of-the-art artificial neural network (SoA-ANN). Findings showed that both machine learning (ML) models outperformed the regression-based ones, displaying significantly better performance for all metrics used. CatBoost and SoA-ANN showed roughly comparable results, characterized by a mean coefficient of determination (R2) slightly higher than 0.98. Since goodness-of-fit metrics resulted in no marked differences between machine learning models, CatBoost approach might be preferred because of its easy implementation in Python and its high interpretability. Within the context of pavement engineering, such an advanced machine learning model could provide a useful and powerful tool for asphalt mixtures’ design applications
Mechanical performance prediction of asphalt mixtures: a baseline study of linear and non-linear regression compared with neural network modeling
No full textAccurate predictions of asphalt mixtures’ mechanical performance are crucial to improve the conventional mix-design procedures and to optimize both pavements’ performance and service life. This research explores this issue by means of a comparative analysis between different modeling approaches: conventional regressions, both linear and non-linear, and artificial neural networks. The former are widely used but may lack the flexibility to capture complex relationships between testing conditions and the corresponding mechanical behavior. The latter represent promising alternatives due to their capability to successfully model non-linear interactions between variables. This research compares the predictive accuracy of these different modeling approaches using experimental data resulting from 4-point bending tests carried out under several temperatures and loading frequencies. The outcomes suggest that neural networks outperform conventional regression models in capturing complex relationships, highlighting the strengths and limitations of each modeling approach and providing insights for selecting optimal models in road pavement engineering applications
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
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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
- …
