Probabilistic Mechanistic-Based Pavement Damage Costs for Multitrip Overweight Vehicles

Batioja-Alvarez, Dario; Kazemi, Seyed-Farzan; Hajj, Elie Y.; Siddharthan, Raj; Hand, Adam J. T.

doi:10.1061/jpeodx.0000033

Cited by 18 publications

(7 citation statements)

References 10 publications

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“…In the GEP framework, the genes in a chromosome may consist of two types of information stored in either the tail or head of genes, i.e., information for generating the overall GEP model and the information from terminals for producing subsequent of the model. Specific details about GEP can be found elsewhere [10,31,32,34,35]. Figure 1 presents a sample program illustration of evolving GEP, where d1, d2, and d3 are the model inputs.…”

Section: Gepmentioning

confidence: 99%

Prediction of Compression Index of Fine-Grained Soils Using a Gene Expression Programming Model

et al. 2019

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In construction projects, estimation of the settlement of fine-grained soils is of critical importance, and yet is a challenging task. The coefficient of consolidation for the compression index (Cc) is a key parameter in modeling the settlement of fine-grained soil layers. However, the estimation of this parameter is costly, time-consuming, and requires skilled technicians. To overcome these drawbacks, we aimed to predict Cc through other soil parameters, i.e., the liquid limit (LL), plastic limit (PL), and initial void ratio (e0). Using these parameters is more convenient and requires substantially less time and cost compared to the conventional tests to estimate Cc. This study presents a novel prediction model for the Cc of fine-grained soils using gene expression programming (GEP). A database consisting of 108 different data points was used to develop the model. A closed-form equation solution was derived to estimate Cc based on LL, PL, and e0. The performance of the developed GEP-based model was evaluated through the coefficient of determination (R 2 ), the root mean squared error (RMSE), and the mean average error (MAE). The proposed model performed better in terms of R 2 , RMSE, and MAE compared to the other models.

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Section: Gepmentioning

confidence: 99%

Prediction of Compression Index of Fine-Grained Soils Using a Gene Expression Programming Model

et al. 2019

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“…information to generate the overall GEP model, and the information of terminals for producing subsequent GEP models. Specific details about GEP can be found elsewhere [10,31,32,34,35]. with coefficients of c0, c1, and c2 while utilizing the nonlinear terms [31,32].…”

Section: Gene Expression Programming (Gep)mentioning

confidence: 99%

Evolutionary Prediction Model for Fine-Grained Soils Compression Index Using Gene-Expression Programming

Mohammadzadeh¹,

Kazemi²,

Mosavi³

2019

Preprint

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Appropriate estimation of soil settlement is of significant importance since it directly influences the performance of building and infrastructures that are built on soil. In particular, the settlement of fine-grained soils is critical because of low permeability and continuous settlement with time.Coefficient of consolidation (Cc) is a key parameter to estimate settlement of fine-grained soil layers. However, estimation of this parameter is time consuming, needs skilled technicians, and specific equipment. In this study, Cc was estimated using several soil parameters such as liquid limit (LL), plastic limit (PL), and initial void ratio (e0). Estimating such parameters in laboratory is straight forward and needs substantially less time and cost compared to conventional tests to estimate Cc such as oedometer test. This study presents a novel prediction model for Cc of finegrained soils using gene-expression programming (GEP). GEP is a biologically inspired technique capable of offering closed-form solution for the optimal solution. A database consisted of 108 different data points was used to develop the model. A closed-form equation solution was derived to estimate Cc based on LL, PL, and e0. The performance of developed GEP-based model was evaluated through coefficient of determination (R 2 ), root mean squared error (RMSE), and mean average error (MAE). High R 2 and low error values indicated the descent performance of the model. Furthermore, the model was evaluated using the additional performance measures and met all the suggested criteria. Furthermore, the model had a better performance in terms of R 2 , RMSE, and MAE compared to most of existing models. It is expected that the developed model will decrease the time and cost associate with determining Cc of fine-grained soils.

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“…This understanding will be helpful to modify axle configuration and load distribution so that potential pavement damage can be minimized. Batioja-Alvarez et al focused on pavement damage costs associated with multi-trip scenarios of a few selected OW vehicles in Nevada with single yearly permits ( 11 ). The NDOT database on past OW vehicle passes was used to determined pavement repair and maintenance associated costs.…”

Section: Introductionmentioning

confidence: 99%

Statistical Distributions of Pavement Damage Associated with Overweight Vehicles: Methodology and Case Study

Batioja-Alvarez

Kazemi

Hajj

et al. 2018

Transportation Research Record

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The trucking industry is the primary mode of transporting for goods and commodities in the United States. Currently, there is an increasing trend in the use of overweight (OW) vehicles on the highway network. State highway agencies (SHAs) are challenged to address this increase, particularly relative to associated pavement damage. In this study, a probabilistic method was developed to evaluate rutting and fatigue cracking damage caused by OW vehicles under different road and environmental conditions. The influential input parameters in this analysis included gross vehicle weight (GVW), axle configuration, axle weight, pavement temperature, and vehicle-miles traveled (VMT). Necessary information was obtained by analyzing more than 170,000 entries of a Nevada Department of Transportation (NDOT) OW permit database. The developed model was based on mechanistic-empirical (ME) approach and considered asphalt concrete (AC) viscoelastic characterization. The results of this study were distributions of AC critical responses, load equivalency factors (LEFs), and relative damage factors (RDFs). The analysis showed that load equivalent factor (LEF) distributions could be incorporated in pavement design methods to account for OW vehicles. Furthermore, the damage induced by specific OW vehicles could be assessed using the relative damage factor (RDF) concept and may be efficiently used by SHAs during the permit application process. A case study was presented illustrating the impact of an OW axle configuration on pavement damage. Finally, a method was suggested for developing RDF tables with damage ranges corresponding to different axle configurations and the GVW that could be a tool for SHAs to evaluate and understand pavement damage induced by OW vehicles.

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Probabilistic Mechanistic-Based Pavement Damage Costs for Multitrip Overweight Vehicles

Cited by 18 publications

References 10 publications

Prediction of Compression Index of Fine-Grained Soils Using a Gene Expression Programming Model

Prediction of Compression Index of Fine-Grained Soils Using a Gene Expression Programming Model

Evolutionary Prediction Model for Fine-Grained Soils Compression Index Using Gene-Expression Programming

Statistical Distributions of Pavement Damage Associated with Overweight Vehicles: Methodology and Case Study

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