Papers by Mojtaba "Max" Ziyadi
Road Materials and Pavement Design, 2022
Pavement condition assessment plays a key role in infrastructure programming and planning process... more Pavement condition assessment plays a key role in infrastructure programming and planning processes. Similar to other state agencies, the Illinois Department of Transportation (IDOT) has been using a system to evaluate the condition of pavements since 1974. Since 1994-1995, IDOT has been using a system to project future pavement performance as well. The condition rating survey (CRS) value is the index between 1 (failed) and 9 (new), representing the overall condition of pavement. The purpose of this study was to update and revise the existing CRS calculation and prediction models using new data. To accomplish the goals of the study, the CRS data was received for the years 2000-2014. The data was initially processed and cleaned in preparation for modeling. CRS prediction models were prepared for Interstate and Non-Interstate pavement types. The two-slope model was used for all asphalt-surfaced pavements, whereas a new model was proposed for concrete-surfaced pavements. The proposed model for concrete-surfaced pavements is a nonlinear survival type designed to capture the distinct deterioration patterns of concrete pavements with little to no reduction in CRS-followed by a rapid and linear deterioration and a flatter region at the end, once the pavement is saturated with damage. The CRS calculation models were updated to incorporate new distresses. Based on the literature review and the analysis of distress composition, it was found that IDOT's distress ratings are generally in agreement with the ASTM standard-with the exception of alligator cracking. A database containing recorded distresses, used by experts, was referenced to add missing distresses, such as alligator cracking, for each Interstate model.
The implementation of a pavement preservation program was initiated in Fiscal Year (FY) 2005 at t... more The implementation of a pavement preservation program was initiated in Fiscal Year (FY) 2005 at the Illinois Department of Transportation (IDOT) by appropriating funding for four specific pavement preservation treatments. The types of treatments included micro-surfacing, slurry seals, cape seal, and bituminous surface treatments (also known as chip seals). The scope and funding level for the state's nine highway districts has expanded over the years. As a result, several years of performance data was collected from the projects constructed since the inception of the program. In this study, the performance of preservation treatments used by districts as part of the pavement preservation program were evaluated. After treatments were applied, pavement condition prediction models were developed for nine preservation treatments. Two methodologies were followed in developing the models. The first is solely based on the collected data when historical pavement condition data were sufficient. Due to the lack of data for many of the treatments, an alternative method was used to develop models. A multi-criteria decisionmaking method known as the analytic network process (ANP) was used to integrate expert opinion collected through questionnaires into the model development. The proposed model form is consistent with the existing condition rating survey CRS prediction models with a single slope, with the addition of project-specific factors to adjust the deterioration rate. The model variables included the existing pavement condition prior to the treatment, traffic, and truck percentage, along with the base deterioration rate. According to the modeling results, chip seals, slurry seals, and Half-SMART treatments were among the shortest-lived treatments, with an average service life of 3-4 years. For micro-surfacing treatments, single-pass and double-pass, could extend the service life to approximately 6 and 7 years, respectively. The average service life extension for cape seal treatment was more than 7 years, whereas cold in-place recycling treatment, with surface overlay and surface treatment, can extend pavement service life by approximately 8-10 years. It was also found that the performance of the ultra-thin bonded wearing course UTBWC treatment can be comparable to that of the micro-surfacing treatments, by extending the pavement service life by 6 years on average with a wider range of variability.
The International Journal of Life Cycle Assessment, 2018
Purpose Objective uncertainty quantification (UQ) of a product life-cycle assessment (LCA) is a c... more Purpose Objective uncertainty quantification (UQ) of a product life-cycle assessment (LCA) is a critical step for decisionmaking. Environmental impacts can be measured directly or by using models. Underlying mathematical functions describe a model that approximate the environmental impacts during various LCA stages. In this study, three possible uncertainty sources of a mathematical model, i.e., input variability, model parameter (differentiate from input in this study), and model-form uncertainties, were investigated. A simple and easy to implement method is proposed to quantify each source. Methods Various data analytics methods were used to conduct a thorough model uncertainty analysis; (1) Interval analysis was used for input uncertainty quantification. A direct sampling using Monte Carlo (MC) simulation was used for interval analysis, and results were compared to that of indirect nonlinear optimization as an alternative approach. A machine learning surrogate model was developed to perform direct MC sampling as well as indirect nonlinear optimization. (2) A Bayesian inference was adopted to quantify parameter uncertainty. (3) A recently introduced model correction method based on orthogonal polynomial basis functions was used to evaluate the model-form uncertainty. The methods are applied to a pavement LCA to propagate uncertainties throughout an energy and global warming potential (GWP) estimation model; a case of a pavement section in Chicago metropolitan area was used. Results and discussion Results indicate that each uncertainty source contributes to the overall energy and GWP output of the LCA. Input uncertainty was shown to have significant impact on overall GWP output; for the example case study, GWP interval was around 50%. Parameter uncertainty results showed that an assumption of ± 10% uniform variation in the model parameter priors resulted in 28% variation in the GWP output. Model-form uncertainty had the lowest impact (less than 10% variation in the GWP). This is because the original energy model is relatively accurate in estimating the energy. However, sensitivity of the model-form uncertainty showed that even up to 180% variation in the results can be achieved due to lower original model accuracies. Conclusions Investigating each uncertainty source of the model indicated the importance of the accurate characterization, propagation, and quantification of uncertainty. The outcome of this study proposed independent and relatively easy to implement methods that provide robust grounds for objective model uncertainty analysis for LCA applications. Assumptions on inputs, parameter distributions, and model form need to be justified. Input uncertainty plays a key role in overall pavement LCA output. The proposed model correction method as well as interval analysis were relatively easy to implement. Research is still needed to develop a more generic and simplified MCMC simulation procedure that is fast to implement.
Transportation Research Record: Journal of the Transportation Research Board, 2018
This paper summarizes a multi-year effort comparing the new-generation wide-base tires (NG-WBT) a... more This paper summarizes a multi-year effort comparing the new-generation wide-base tires (NG-WBT) and dual-tire assembly from a holistic point of view. The tires were compared considering not only pavement damage but also environmental impact. Numerical modeling, prediction methods, experimental measurements, and life-cycle assessment were combined to provide recommendations about the use of NG-WBT. A finite element (FE) approach considering variables that are usually omitted in the conventional analysis of flexible pavement was used for modeling pavement structures combining layer thickness, material properties, tire load, tire-inflation pressure, and pavement type (interstate and low volume). A prediction tool, ICT-Wide, was developed based on an artificial neural network to obtain critical pavement responses in cases excluded from the FE analysis matrix. Based on the bottom-up fatigue cracking, permanent deformation, and international roughness index, the life-cycle energy consumpt...
Journal of Cleaner Production, 2018
The International Journal of Life Cycle Assessment, 2018
Purpose New-generation wide-base tire (NG-WBT) is known for improving fuel economy and at the sam... more Purpose New-generation wide-base tire (NG-WBT) is known for improving fuel economy and at the same time for potentially causing a greater damage to pavement. No study has been conducted to evaluate the net environmental saving of the combined system of pavements and NG-WBT. This study adopted a holistic approach (life cycle assessment [LCA] and life cycle costing [LCC]) to quantitatively evaluate the environmental and economic impact of using NG-WBT. Methods The net effect of different levels of market penetration of NG-WBT on energy consumption, global warming potential (GWP), and cost based on the fatigue cracking and rutting performance of two different asphalt concrete (AC) pavement structures was evaluated. The performance of pavements was determined based on pavement design lives; pavement surface characteristics, and pavement critical strain responses obtained from the artificial neural network (ANN) based on finite element (FE) simulations were used to calculate design lives of pavements. Based on the calculated design lives, life cycle inventory (LCI) and cost databases, and rolling resistance (RR) models previously developed by the University of Illinois at Urbana-Champaign (UIUC) were used to calculate the environmental and economic impact of the combined system. Results and discussion The fuel economy improvement using NG-WBT is 1.5% per axle. Scenario-based case studies were conducted. Considering 0% NG-WBT market penetration (or 100% standard dual tire assembly [DTA]) as a baseline, scenario 1 assumed the same fatigue and rutting potential between NG-WBT and DTA; therefore, the only difference came from fuel economy improvement of using NG-WBT. In scenario 2, pavement fatigue cracking potential determined the pavement design life; both thick and thin AC overlay sections experienced positive net environmental savings, but mixed net economic savings. In scenario 3, pavement rutting potential determined the pavement design life; the thick AC overlay section experienced positive net environmental savings, but mixed net economic savings. The thin section experienced negative net environmental and economic savings. Conclusions The outcomes of scenario-based case studies indicated that NG-WBT can result in significant savings in life cycle energy consumption and cost, and GWP; however, these benefits were sensitive to the method used to determine the pavement performance; especially, a small change in pavement strain can result in significant change in pavement life. In addition, the effect of fuel price/economy improvement, discount rate, and International Roughness Index (IRI) threshold values was studied in the sensitivity analyses.
The International Journal of Life Cycle Assessment, 2018
Purpose Fuel economy and emissions of heavy-duty trucks greatly vary based on vehicular/environme... more Purpose Fuel economy and emissions of heavy-duty trucks greatly vary based on vehicular/environmental conditions. Largescale infrastructure construction projects require a large amount of material/equipment transportation. Single-parameter generic hauling models may not be the best option for an accurate estimation of hauling contribution in life cycle assessment (LCA) involving construction projects; therefore, more precise data and parameterized models are required to represent this contribution. This paper discusses key environmental/operational variables and their impact on transportation of materials and equipment; a variable-impact transportation (VIT) model accounting for these variables was developed to predict environmental impacts of hauling. Methods The VIT model in the form of multi-nonlinear regression equations was developed based on simulations using the U.S. Environmental Protection Agency (EPA)'s Motor Vehicle Emission Simulator (MOVES) to compute all the impact categories in EPA's TRACI 2.1 and energy consumption of transportation. Considering actual driving cycles of hauling trucks recorded during a pavement rehabilitation project, the corresponding environmental impacts were calculated, and sensitivity analyses were performed. In addition, an LCA case study based on historical pavement reconstruction projects in Illinois was conducted to analyze the contribution of transportation and variability of its impacts during the pavements' life cycle. Results and discussion The importance of vehicle driving cycles was realized from simulation results. The case study results showed that considering driving cycles using the VIT model could increase the contribution of hauling in total life cycle Global Warming Potential (GWP) and total life cycle GWP itself by 2-4 and 3-5%, respectively. In addition to GWP, ranges of other hauling-related impact categories including Smog, Ozone Depletion, Acidification, and Primary Energy Demand from fuel were presented based on the case study. Ozone Depletion ranged from 9 to 45%, and Smog ranged from 11 to 48% of the total relevant life cycle impacts. The GWP contribution of hauling in pavement LCA ranged between 5 and 32%. The results indicate that the contribution of hauling transportation can be significant in pavement LCA. Conclusions For large-scale roadway infrastructure construction projects that need a massive amount of material transportation, high fidelity models and data should be used especially for comparative LCAs that can be used as part of decision making between alternatives. The VIT model provides a simple analytical platform to include the critical vehicular/operational variables without any dependence on an external software; the model can also be incorporated in those studies where some of the transportation activity data are available.
Neural Computing and Applications, 2016
A computationally efficient surrogate model was developed based on artificial neural networks (AN... more A computationally efficient surrogate model was developed based on artificial neural networks (ANN) to investigate the effect of the new generation of wide-base tires on pavement responses. Non-uniform tire contact stress measurements were obtained using a stress-in-motion instrument. The measured 3-D contact stresses were applied on two extreme 3-D flexible pavement finite element models representing low-volume (thin) and highvolume (thick) roads. Eleven critical pavement responses were modeled at two different material properties input levels-detailed and simplified-depending on data availability. The results rendered by the ANN surrogate models were highly accurate with average prediction error less than 5 % and R-square values higher than 0.95. In addition, two sensitivity analyses were performed to investigate the variables effect on pavement responses. It was found that the type of tire (wide-base vs. dual tire assembly) is more influential than the inflation pressure on pavement responses. However, the tire inflation pressure seemed to have a significant effect on near-surface responses. The developed models were incorporated into a tool to assist designers and engineers in investigating the effect of the pavement responses of wide-base versus dual tire assembly under typical loading conditions and pavement structures. Keywords Neural networks Á Surrogate model Á Wide-base tire Á Finite element modeling Á Thin and fulldepth pavement Á Pavement response prediction & Mojtaba Ziyadi
The Roles of Accelerated Pavement Testing in Pavement Sustainability, 2016
The proposed paper summarizes the accelerated pavement testing database obtained from various loc... more The proposed paper summarizes the accelerated pavement testing database obtained from various locations, including a database of recent studies. Test sections built as part of a study investigating the effect of new generation wide-base tire (NG-WBT 445/50R22.5) and dual-tire assembly (DTA 275/80R22.5) on flexible pavement response were used. These test sections were established at three different test locations throughout the U.S.—Florida, Davis-California, and Ohio State. Various instrumentations were installed at several locations within the pavement system to capture the most critical pavement responses. Instrumentations installed include strain gauges, pressure cells, thermocouples, multi-depth defloctometers, surface strain gauges, and strain gauge rosettes. Critical pavement responses, including strain at bottom of AC and stress on top of subgrade, were analyzed. Moreover, the effect of temperature, differential tire pressure, and near surface responses were investigated for NG-WBT versus DTA.
Mechanics of Time-Dependent Materials, 2016
The structure-induced rolling resistance of pavements, and its impact on vehicle fuel consumption... more The structure-induced rolling resistance of pavements, and its impact on vehicle fuel consumption, is investigated in this study. The structural response of pavement causes additional rolling resistance and fuel consumption of vehicles through deformation of pavement and various dissipation mechanisms associated with inelastic material properties and damping. Accurate and computationally efficient models are required to capture these mechanisms and obtain realistic estimates of changes in vehicle fuel consumption. Two mechanistic-based approaches are currently used to calculate vehicle fuel consumption as related to structural rolling resistance: dissipation-induced and deflection-induced methods. The deflection-induced approach is adopted in this study, and realistic representation of pavement–vehicle interactions (PVIs) is incorporated. In addition to considering viscoelastic behavior of asphalt concrete layers, the realistic representation of PVIs in this study includes non-uniform three-dimensional tire contact stresses and dynamic analysis in pavement simulations. The effects of analysis type, tire contact stresses, pavement viscoelastic properties, pavement damping coefficients, vehicle speed, and pavement temperature are then investigated.
Pavement Life-Cycle Assessment, 2017
Researchers have been studying wide-base tires for over two decades, but no evidence has been pro... more Researchers have been studying wide-base tires for over two decades, but no evidence has been provided regarding the net benefit of this tire technology. In this study, a comprehensive approach is used to compare new-generation wide-base tires (NG-WBT) with the dual-tire assembly (DTA). Numerical modeling, prediction methods, experimental measurements, and environmental impact assessment were combined to provide recommendations about the use of NG-WBT. A finite element approach, considering variables usually omitted in the conventional analysis of flexible pavement was utilized for modeling. Five hundred seventy-six cases combining layer thickness, material properties, tire load, tire inflation pressure, and pavement type (thick and thin) were analyzed to obtained critical pavement responses. A prediction tool, known as ICT-Wide, was developed based on artificial neural networks to obtain critical pavement responses in cases outside the finite element analysis matrix. The environmen...
A computationally efficient surrogate model was developed based on artificial neural networks (AN... more A computationally efficient surrogate model was developed based on artificial neural networks (ANN) to investigate the effect of the new generation of wide-base tires on pavement responses. Non-uniform tire contact stress measurements were obtained using a stress-in-motion (SIM) instrument. The measured 3-D contact stresses were applied on two extreme 3-D flexible pavement finite-element (FE) models representing low-volume (thin) and high-volume (thick) roads. Eleven critical pavement responses were modeled at two different material properties input levels—detailed and simplified—depending on data availability. The results rendered by the ANN surrogate models were highly accurate with average prediction error less than 5% and R-square values higher than 0.95. In addition, two sensitivity analyses were performed to investigate the variables effect on pavement responses. It was found that the type of tire (wide-base vs. dual tire assembly) is more influential than the inflation pressure on pavement responses. However, the tire inflation pressure seemed to have a significant effect on near-surface responses. The developed models were incorporated into a tool to assist designers and engineers in investigating the effect of the pavement responses of wide-base vs. dual assembly tires under typical loading conditions and pavement structures.
The structure-induced rolling resistance of pavements, and its impact on vehicle fuel consumption... more The structure-induced rolling resistance of pavements, and its impact on vehicle fuel consumption, is investigated in this study. The structural response of pavement causes additional rolling resistance and fuel consumption of vehicles through deformation of pavement and various dissipation mechanisms associated with inelastic material properties and damping. Accurate and computationally efficient models are required to capture these mechanisms and obtain realistic estimates of changes in vehicle fuel consumption. Two mechanistic-based approaches are currently used to calculate vehicle fuel consumption as related to structural rolling resistance: dissipation-induced and deflection-induced methods. The deflection-induced approach is adopted in this study, and realistic representation of pavement–vehicle interactions (PVIs) is incorporated. In addition to considering viscoelastic behavior of asphalt concrete layers, the realistic representation of PVIs in this study includes non-uniform three-dimensional tire contact stresses and dynamic analysis in pavement simulations. The effects of analysis type, tire contact stresses, pavement viscoelas-tic properties, pavement damping coefficients, vehicle speed, and pavement temperature are then investigated. Keywords Structure-induced rolling resistance · Viscoelastic asphalt concrete · Pavement–vehicle interactions · Use-phase · Sustainability
In this work we propose a heteroscedastic generalization to RVM, a fast Bayesian framework for re... more In this work we propose a heteroscedastic generalization to RVM, a fast Bayesian framework for regression, based on some recent similar works. We use variational approximation and expectation propagation to tackle the problem. The work is still under progress and we are examining the results and comparing with the previous works.
Transportation Research Record: Journal of the Transportation Research Board, 2013
performance prediction in optimization engines to obtain optimal pavement maintenance and rehabil... more performance prediction in optimization engines to obtain optimal pavement maintenance and rehabilitation strategies over a planning horizon (2-4).
Journal of Infrastructure Systems, 2013
Accurate prediction of pavement performance is essential to a pavement infrastructure management ... more Accurate prediction of pavement performance is essential to a pavement infrastructure management system. The prediction process usually consists of classifying sections into families and then developing prediction curves or models for each family. Artificial intelligence, especially machine learning algorithms, provides a medium to investigate techniques that address these management concerns. This paper presents a two-stage model to classify and accurately predict the performance of a pavement infrastructure system. First, sections with similar characteristics are classified into groups using a support vector classifier (SVC). Next, a recurrent neural network (RNN) uses the classification results from the first stage in addition to other performance-related factors to predict performance. A case study using the Minnesota Department of Transportation (MnRoad) test facility database shows that the proposed model is a good classification decision support system, has better prediction results than the single-stage RNN model, and captures all underlying effects of the different variables. The significance and a sensitivity analysis of the model parameters are also presented.
In spite of significant advances in highways safety, a lot of crashes in high severities still oc... more In spite of significant advances in highways safety, a lot of crashes in high severities still occur in highways. Investigation of
influential factors on crashes enables engineers to carry out calculations in order to reduce crash severity. Therefore, this paper
deals with the models to illustrate the simultaneous influence of human factors, road, vehicle, weather conditions and traffic
features including traffic volume and flow speed on the crash severity in urban highways.
This study uses a series of artificial neural networks to model and estimate crash severity and to identify significant crash-related
factors in urban highways. Applying artificial neural networks in engineering science has been proved in recent years. It is
capable to predict and present desired results in spite of limited data sets, which is the remarkable feature of the artificial neural
networks models.
Obtained results illustrate that the variables such as highway width, head-on collision, type of vehicle at fault, ignoring lateral
clearance, following distance, inability to control the vehicle, violating the p
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Papers by Mojtaba "Max" Ziyadi
influential factors on crashes enables engineers to carry out calculations in order to reduce crash severity. Therefore, this paper
deals with the models to illustrate the simultaneous influence of human factors, road, vehicle, weather conditions and traffic
features including traffic volume and flow speed on the crash severity in urban highways.
This study uses a series of artificial neural networks to model and estimate crash severity and to identify significant crash-related
factors in urban highways. Applying artificial neural networks in engineering science has been proved in recent years. It is
capable to predict and present desired results in spite of limited data sets, which is the remarkable feature of the artificial neural
networks models.
Obtained results illustrate that the variables such as highway width, head-on collision, type of vehicle at fault, ignoring lateral
clearance, following distance, inability to control the vehicle, violating the p
influential factors on crashes enables engineers to carry out calculations in order to reduce crash severity. Therefore, this paper
deals with the models to illustrate the simultaneous influence of human factors, road, vehicle, weather conditions and traffic
features including traffic volume and flow speed on the crash severity in urban highways.
This study uses a series of artificial neural networks to model and estimate crash severity and to identify significant crash-related
factors in urban highways. Applying artificial neural networks in engineering science has been proved in recent years. It is
capable to predict and present desired results in spite of limited data sets, which is the remarkable feature of the artificial neural
networks models.
Obtained results illustrate that the variables such as highway width, head-on collision, type of vehicle at fault, ignoring lateral
clearance, following distance, inability to control the vehicle, violating the p