A formulation for asphalt concrete air void during service life by adopting a hybrid evolutionary polynomial regression and multi-gene genetic programming.

Bitumen, aggregate, and air void (VA) are the three primary ingredients of asphalt concrete. VA changes over time as a function of four factors: traffic loads and repetitions, environmental regimes, compaction, and asphalt mix composition. Due to the high as-constructed VA content of the material, it is expected that VA will reduce over time, causing rutting during initial traffic periods. Eventually, the material will undergo shear flow when it reaches its densest state with optimum aggregate interlock or refusal VA content. Therefore, to ensure the quality of construction, VA in asphalt mixture need to be modeled throughout the service life. This study aims to implement a hybrid evolutionary polynomial regression (EPR) combined with a teaching-learning based optimization (TLBO) algorithm and multi-gene genetic programming (MGGP) to predict the VA percentage of asphalt mixture during the service life. For this purpose, 324 data records of VA were collected from the literature. The variables selected as inputs were original as-constructed VA, VA orig (%); mean annual air temperature, MAAT (°F); original viscosity at 77 °F, η o r i g , 77 (Mega-Poises); and time (months). EPR-TLBO was found to be superior to MGGP and existing empirical models due to the interquartile ranges of absolute error boxes equal to 0.67%. EPR-TLBO had an R2 value of more than 0.90 in both the training and testing phases, and only less than 20% of the records were predicted utilizing this model with more than 20% deviation from the observed values. As determined by the sensitivity analysis, η o r i g , 77 is the most significant of the four input variables, while time is the least one. A parametric study showed that regardless of MAAT , η o r i g , 77 , of 0.3 Mega-Poises, and VA orig above 6% can be ideal for improving the pavement service life. It was also witnessed that with an increase of MAAT from 37 to 75 °F, the serviceability of asphalt concrete takes 15 months less on average.

Effect of cations and anions on flocculation of dispersive clayey soils.

Dispersivity of clay soils is one of the most important issues that should be considered in civil engineering projects. Dispersive soils are clay soils that are easily washed in water with low concentrations of salt; these clay soils usually contain high levels of sodium ions in their adsorption cation sites. Kaolin, sepiolite (fibrous clay), and bentonite soils are among the most important and useful industrial materials. Therefore, in this study, these three clay soils were selected to investigate dispersivity potential by adding 4% of dispersive materials (Sodium hexametaphosphate) and performing shear strength, crumb, double hydrometer, pinhole tests, and chemical experiments. Results indicated a change in the Sodium Adsorption Ratio (SAR) in the following order: kaolin > sepiolite > bentonite. Stabilization practices using chemical methods were done after performing soil divergence with sodium hexametaphosphate. CaCl2, CaSO4, AlCl3, and Al2 (SO4)3 were used for chemical stabilization to assess the effect of ion valence on soil improvement parameters. Results obtained for chemical properties showed that, stabilization potential was in the following order: kaolin > sepiolite > bentonite; meaning that clay soils with lower cation exchange capacity have more remediation potential and are more susceptible to dispersion. The role of calcium and aluminum cations was prominent in improving mechanical and dispersivity properties, respectively. In general, further dispersion potential of clays in the same Na+ concentration was found to be related to a decrease in the cation exchange capacity, specific surface area, and plastic index. Soil dispersion was directly associated with diffuse double layer and electrostatic forces while; soil strength parameters were mainly dependent on cementation and connection of soil particles to each other. Consequently, it was observed that, clay soils with suitable engineering properties (higher strength and compaction or lower Atterberg limits) are more sensitive to dispersion compared to other types of clay with higher CEC and plasticity values.

Prediction of frequency for simulation of asphalt mix fatigue tests using MARS and ANN.

Fatigue life of asphalt mixes in laboratory tests is commonly determined by applying a sinusoidal or haversine waveform with specific frequency. The pavement structure and loading conditions affect the shape and the frequency of tensile response pulses at the bottom of asphalt layer. This paper introduces two methods for predicting the loading frequency in laboratory asphalt fatigue tests for better simulation of field conditions. Five thousand (5000) four-layered pavement sections were analyzed and stress and strain response pulses in both longitudinal and transverse directions was determined. After fitting the haversine function to the response pulses by the concept of equal-energy pulse, the effective length of the response pulses were determined. Two methods including Multivariate Adaptive Regression Splines (MARS) and Artificial Neural Network (ANN) methods were then employed to predict the effective length (i.e., frequency) of tensile stress and strain pulses in longitudinal and transverse directions based on haversine waveform. It is indicated that, under controlled stress and strain modes, both methods (MARS and ANN) are capable of predicting the frequency of loading in HMA fatigue tests with very good accuracy. The accuracy of ANN method is, however, more than MARS method. It is furthermore shown that the results of the present study can be generalized to sinusoidal waveform by a simple equation.