266 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
Multivariate Regression of Road Segments’ Accident Data in Italian Rural Networks
Full text linkIncreasing 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
Stiffness Moduli Modelling and Prediction in Four-Point Bending of Asphalt Mixtures: A Machine Learning-Based Framework
Full text linkStiffness modulus represents one of the most important parameters for the mechanical characterization of asphalt mixtures (AMs). At the same time, it is a crucial input parameter in the process of designing flexible pavements. In the present study, two selected mixtures were thoroughly investigated in an experimental trial carried out by means of a four-point bending test (4PBT) apparatus. The mixtures were prepared using spilite aggregate, a conventional 50/70 penetration grade bitumen, and limestone filler. Their stiffness moduli (SM) were determined while samples were exposed to 11 loading frequencies (from 0.1 to 50 Hz) and 4 testing temperatures (from 0 to 30 °C). The SM values ranged from 1222 to 24,133 MPa. Observations were recorded and used to develop a machine learning (ML) model. The main scope was the prediction of the stiffness moduli based on the volumetric properties and testing conditions of the corresponding mixtures, which would provide the advantage of reducing the laboratory efforts required to determine them. Two of the main soft computing techniques were investigated to accomplish this task, namely decision trees with the Categorical Boosting algorithm and artificial neural networks. The outcomes suggest that both ML methodologies achieved very good results, with Categorical Boosting showing better performance (MAPE = 3.41% and R2 = 0.9968) and resulting in more accurate and reliable predictions in terms of the six goodness-of-fit metrics that were implemented
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
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 °C of 0.62 MPa (well above the Italian technical acceptance limits), a stiffness modulus at 25 °C equal to 6171 MPa, and a number of cycles to failure at 20 °C equal to 6989 for a stress level of 300 kPa
El Tlacuache Núm. 496 (2011). 496 Año 11 (2011) diciembre. El Tlacuache
No full textEspejismos raciales por Ricardo Noguera Solano. -Las ideas racistas y la búsqueda de la identidad nacional mexicana por Alfredo Bueno Hernández, Fabiola Juárez Barrera, Carlos Pérez Malváez
ANALISIS RISIKO DAN BAHAYA KESELAMATAN DAN KESEHATAN KERJA (K3) DENGAN METODE HAZARD IDENTIFICATION AND RISK ASSESSMENT (HIRA) DAN ROOT CAUSE ANALYSIS (RCA)
No full textJika membutuhkan abstrak atau isi jurnal silahkan menghubungi author melalui e-mail [email protected] atau [email protected]
Dipublikasikan tanggal: 16 Mei 202
Las fronteras culturales y textuales: “Cuento en camino” de Ana Lydia Vega
Full text linkAmong the writers of the Caribbean countries, the notion of the border is related to the discourse of insularity. This concept goes beyond geographical limits, because it designates a historical-cultural experience and a peculiar identifying perspective that have generated a heterogeneous writing. Based on these premises, the essay proposes the analysis of “Cuento en camino” written by the Puerto Rican writer Ana Lydia Vega, text included in Falsas crónicas del Sur (1991). The story emerges as the synthesis of the narrative poetics developed throughout the collection, in which the stories are installed in the imprecise border between the chronicle and the fiction, between the legend and the oral tradition of the Puerto Rican peoples. It is also presented as a metaliterary story, since the author reveals the narrative and narratological instruments used to construct the story
El centenario de Virgilio Piñera en Venecia
Full text linkIn a year during which some of the most leading figures in Latin American literature were celebrated on their centenary of their birth, (Julio Cortázar, Adolfo Bioy Casares, Gertrudis Gomez de Avellaneda, Octavio Paz), I would like to remember a belated centenary, due to a real “rebirth” in the cultural and literary Cuban scene. I’m going to propose an excellent cuban writer, Virgilio Piñera (1912-1979), who become the subject of growing critical and popular attention in recent years. His singular stories – or better still, his whole existential adventure – answer to a deep-rooted obsession with authenticity, that insidious zone where each character and situation denude his author and the ghosts tormenting him. His writing, being a continuous paradox, is the instrument trough which he wants to fight the trivial and the meaningless. His troublesome literary voice was reconsidered after his death, and his first official centenary, in his native country, was commemorated only three years ago, in 2012
El Tlacuache Núm. 501 (2012). 501 Año 13 (2012) enero. El Tlacuache
No full textWegener, la tectónica de placas y la evolución humana por Eduardo Corona Martínez. -La teoría de la deriva continental de Alfred Lothar Wegener: 100 años por Carlos Pérez Malváez, Alfredo Bueno Hernández, Guadalupe Bribiesca Escutia, Fabiola Juárez Barrer
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