Bio: Cesar Tirado is an academic researcher from University of Texas at El Paso. The author has contributed to research in topics: Axle & Slab. The author has an hindex of 7, co-authored 46 publications receiving 224 citations.
TL;DR: In this paper, the increase in the axle loads and frequency of operations of over-weight (OW) torsion vehicles was studied in the energy sector across the United States.
Abstract: Recent traffic trends and permit issuance show significant mobility demands in the energy sectors across the nation. The increase in the axle loads and frequency of operations of over-weight (OW) t...
TL;DR: In this article, a dynamic finite element model that considers the nonlinear behavior of geomaterials was developed to simulate the LWD on a pavement structure, and transfer functions were developed to adjust the surface deformations and moduli from the responses obtained from the measured deflections.
Abstract: Lightweight deflectometers (LWDs) are being used more often for modulus-based quality control of earthwork. One of the practical concerns about implementation of LWDs is that the equation used to estimate the LWD modulus is based on elastic half-space theory and does not account for the nonlinear behavior of soil and soil–impact plate interaction. The finite element method can be used to study the effects of nonlinear behaviors of geomaterials and the soil–plate interaction on the measured deflections. This study provides a means for accounting for the impact of these parameters on the measured responses and the depths of influence. A dynamic finite element model that considers the nonlinear behavior of geomaterials was developed to simulate the LWD on a pavement structure. A comprehensive range of single-layer and two-layer systems with a wide range of properties and thicknesses was considered. Transfer functions were developed to adjust the surface deformations and moduli from the responses obtained fro...
01 Mar 2021
TL;DR: In this paper, a process to quantify the impact of truck suspension systems and road surface condition on the damage exerted to the pavement is presented, where the International Roughness Index (IRI) was used to simulate the road roughness.
Abstract: Several mechanistic-empirical software packages have been developed in the last two decades to address the impact of axle load and axle configuration on pavement responses and their performance. These software packages generally do not consider the vehicle pavement interaction. The interaction of truck suspension system with the roughness of the road surface may exert additional forces to the pavement. A process to quantify the impact of truck suspension systems and road surface condition on the damage exerted to the pavement is presented in this paper. The International Roughness Index (IRI) was used to simulate the road roughness. The truck-pavement interaction was then modeled to estimate the dynamic load applied to the pavement. The process followed to develop the algorithms is discussed, followed by a parametric study to demonstrate the interaction among the suspension properties, road roughness and vehicle speed. The impact of dynamic load on the pavement distresses and the progress of deterioration is also discussed.
01 Jan 2013
TL;DR: In this paper, a process based on a mechanistic-empirical (ME) analysis was developed to estimate permit fees on the basis of truck-axle loading and configuration as well as the predicted pavement deterioration that they cause.
Abstract: A process based on a mechanistic–empirical (ME) analysis was developed to estimate permit fees on the basis of truck-axle loading and configuration as well as the predicted pavement deterioration that they cause. The process was implemented in a software package, Integrated Pavement Damage Analyzer (IntPave). IntPave is a finite element–based program that calculates pavement responses, uses ME distress models to predict performance under any type of traffic load, is capable of comparing the level of distress caused by a heavy truck relative to a standard truck, and accordingly provides a permit fee. On the basis of a parametric study, it was found that, aside from the truck gross vehicle weight and axle configuration, pavement structure and the damage threshold to rehabilitation also heavily affect the permit fee.
TL;DR: In this article, a 3D finite element (FE) model was developed to simulate proof-mapping of the roller on compacted geomaterials, and the model was used to carry out an extensive parametric study that considered the nonlinear behavior of the geommaterials and the roller operating conditions.
Abstract: One concern usually expressed about the implementation of Intelligent Compaction (IC), especially in tandem with modulus-based spot tests, is the uncertainty in the depth of influence of different devices. The depth of influence is mainly contingent upon the weight, dimensions, and operating settings of the employed roller as well as the characteristics of the underlying materials. This study attempts to evaluate the depth of influence of IC rollers using simulated and field data. A three-dimensional (3D) finite element (FE) model was developed to simulate proof-mapping of the roller on compacted geomaterials. That FE model was used to carry out an extensive parametric study that considered the nonlinear behavior of the geomaterials and the roller operating conditions including static and vibratory movements at stationary and moving conditions for single- (subgrade only) and two-layered (subgrade and base) geosystems. Different test sites were instrumented using in-ground sensors to conduct vibratory IC tests with several instrumented IC rollers for further assessment and verification of the depth of influence. The estimated field, as well as numerical results, indicate a dependency between the depth of influence and the type of geomaterial. The depth of influence increases when geomaterial becomes more granular and decreases with an increase in the cohesion of the geomaterial.
TL;DR: In this article, two main detection strategies are considered: (a) the wave propagation method for far-field damage detection; and (b) the electro-mechanical (E/M) impedance method for near field damage detection.
Abstract: Piezoelectric wafer active sensors can be applied to aging aircraft structures to monitor the onset and progress of structural damage such as fatigue cracks and corrosion. Two main detection strategies are considered: (a) the wave propagation method for far-field damage detection; and (b) the electro-mechanical (E/M) impedance method for near-field damage detection. These methods are developed and verified on simple-geometry specimens, and then tested on realistic aging-aircraft panels with seeded cracks and corrosion. The specimens instrumentation with piezoelectric-wafer active sensors and ancillary apparatus is presented. The experimental methods, signal processing, and damage detection algorithms, tuned to the specific method used for structural interrogation, are discussed. In the wave propagation approach, the pulse-echo and acousto-ultrasonic methods were considered. Reflections from seeded cracks were successfully recorded. In addition, acoustic emission and low-velocity impact were also detected. In the E/M impedance method approach, the high-frequency spectrum is processed using overall-statistics damage metrics. The (1-R 2 ) 3 damage metric, where R is the correlation coefficient, was found to yield the best results. The simultaneous use of the E/M impedance method in the near field and of the wave propagation method in the far field opens the way for a comprehensive multifunctional damage detection system for aging aircraft structural health monitoring.
TL;DR: The interaction of the low-order antisymmetric (a0) and symmetric (s0) Lamb waves with vertical cracks in aluminum plates is studied and these coefficients together with the through-thickness displacement fields are compared to those predicted using a finite element code widely used in the past for modeling Lamb mode diffraction problems.
Abstract: The interaction of the low-order antisymmetric (a0) and symmetric (s0) Lamb waves with vertical cracks in aluminum plates is studied. Two types of slots are considered: (a) internal crack symmetrical with respect to the middle plane of the plate and (b) opening crack. The modal decomposition method is used to predict the reflection and transmission coefficients and also the through-thickness displacement fields on both sides of slots of various heights. The model assumes strip plates and cracks, thus considering two-dimensional plane strain conditions. However, mode conversion (a0 into s0 and vice versa) that occurs for single opening cracks is considered. The energy balance is always calculated from the reflection and transmission coefficients, in order to check the validity of the results. These coefficients together with the through-thickness displacement fields are also compared to those predicted using a finite element code widely used in the past for modeling Lamb mode diffraction problems. Experiments are also made for measuring the reflection and transmission coefficients for incident a0 or s0 lamb modes on opening cracks, and compared to the numerical predictions.
TL;DR: The outcome of this study could be conveniently employed to evaluate the pavement conditions during its service life and help to make valid decisions for rehabilitation or reconstruction of the roads at the right time.
Abstract: Pavement condition assessment provides information to make more cost-effective and consistent decisions regarding management of pavement network. Generally, pavement distress inspections are performed using sophisticated data collection vehicles and/or foot-on-ground surveys. In either approach, the process of distress detection is human-dependent, expensive, inefficient, and/or unsafe. Automated pavement distress detection via road images is still a challenging issue among pavement researchers and computer-vision community. In recent years, advancement in deep learning has enabled researchers to develop robust tools for analyzing pavement images at unprecedented accuracies. Nevertheless, deep learning models necessitate a big ground truth dataset, which is often not readily accessible for pavement field. In this study, we reviewed our previous study, which a labeled pavement dataset was presented as the first step towards a more robust, easy-to-deploy pavement condition assessment system. In total, 7237 google street-view images were extracted, manually annotated for classification (nine categories of distress classes). Afterward, YOLO (you look only once) deep learning framework was implemented to train the model using the labeled dataset. In the current study, a U-net based model is developed to quantify the severity of the distresses, and finally, a hybrid model is developed by integrating the YOLO and U-net model to classify the distresses and quantify their severity simultaneously. Various pavement condition indices are developed by implementing various machine learning algorithms using the YOLO deep learning framework for distress classification and U-net for segmentation and distress densification. The output of the distress classification and segmentation models are used to develop a comprehensive pavement condition tool which rates each pavement image according to the type and severity of distress extracted. As a result, we are able to avoid over-dependence on human judgement throughout the pavement condition evaluation process. The outcome of this study could be conveniently employed to evaluate the pavement conditions during its service life and help to make valid decisions for rehabilitation or reconstruction of the roads at the right time.
TL;DR: In this article, two optimized mixing orders were designed based on the microscopic observation of synthetic composite interfaces to simulate the worst situation during the traditional mixing process and observed in micro scale by scanning electron microscopy.
Abstract: Cold recycling technology is getting more and more attention due to economic and environmental benefits by reduced energy consumption and resource conservation. However, its application has been limited to the base and subbase layer because of complicated components and poor crack resistance for the last decades. The design method has shortcomings especially the traditional mixing order, which may be one of the reasons for poor crack resistance. To support the viewpoint above, synthetic composite interfaces were designed to simulate the worst situation during the traditional mixing process and observed in micro scale by scanning electron microscopy. In the microstructure of the traditional mixing order, it was apparent that cement paste had a number of microdefects, a signal of lower interfacial adhesive strength. Moreover, based on the microscopic observation, the adding sequence of cement ought to be changed and two optimized mixing orders were designed of which the difference was verified by the SEM observation of synthetic composite interfaces and the mechanical experiments for different curing time. It can be summarized that mechanical performance was consistent with the microscopic observation. The traditional mixing order was the worst one both in the strength and moisture sensitivity. Finally, the optimal mixing order is put forward to decrease the possibility of the adverse interface, that is, the graded aggregates are mixed with additional water first to reach the workability, while cement, asphalt emulsion and mineral powder are mixed to form cement-asphalt emulsion mortar, finally mixing them all up.
TL;DR: Wang et al. as mentioned in this paper used genetic algorithm optimization artificial neural network (GA-ANN) model to analyze the behavior of bio-oil/rock asphalt composite modified asphalt, which can further promote the recycling of both biooil and Buton rock asphalt, save energy and lead to a greener construction material.
Abstract: Applying bio-asphalt in road engineering can effectively resolve its shortage of petroleum asphalt, and enhance its environmental friendliness and sustainability. However, the poor high-temperature stability with added bio-oil is a key obstacle restricting its wide utilization. To broaden the application of bio-asphalt in road engineering, in this research was used rock asphalt to further improve bio-asphalt through the Box-Behnken Design (BBD). During the optimization of the design, bio-oil content, rock asphalt content, and shear time were defined as independent variables. Penetration, softening point, creep rate, stiffness modulus, and irrecoverable creep compliance of the modified asphalt were considered response values. Response surface method (RSM) model and Genetic algorithm optimization artificial neural network (GA-ANN) model were utilized to analyze the behavior of bio-oil/rock asphalt composite modified asphalt. Furthermore, the feasibility of both models was verified through experimental results. The results indicated that the incorporation of bio-oil and rock asphalt improved the low-temperature and high-temperature resistance of neat asphalt, respectively. Besides, the low-temperature crack resistance of composite modified asphalt was remarkably enhanced, and the high-temperature performance was similar to the one related to neat asphalt. GA-ANN model had higher feasibility for composite modified asphalt performance optimization. The optimal bio-oil content, rock asphalt content, and shear time determined by using RSM and GA-ANN model were equal to 6.3%, 11.2%, 52.8 min, and 6.3%, 12.9%, 76.6 min, respectively. The bio-oil/rock asphalt composite modified asphalt with targeted performance requirements can be achieved by combining the proposed weight function with the RSM model and GA-ANN model. Optimization with GA-ANN can further promote the recycling of both bio-oil and Buton rock asphalt, save energy, and lead to a greener construction material. This study can serve as a solid base for more efficient utilization of bio-asphalt in road engineering.