La deformación permanente en las mezclas asfálticas y el consecuente deterioro de los pavimentos asfálticos en el Perú

En los últimos 10 años el Perú ha impulsado una política favorable para la Construcción de Obras Viales a lo largo y ancho del territorio, habiéndose ejecutado más de 15,000 kilómetros de carreteras con pavimentos asfálticos, según reportes del Ministerio de Transportes y Comunicaciones, organismo encargado de la red vial nacional. Ante esta realidad existe la imperiosa necesidad de mejorar la tecnología de los pavimentos asfálticos en el Perú para que alcancen la vida útil para la cual fueron diseñados. La deformación permanente es una de las fallas más preocupan tes en el deterioro de pavimentos, siendo necesario conocer con sus causas fundamentales a fin de tomar las previsiones del caso en las etapas de elaboración del proyecto, construcción, y mantenimiento futuro. Por esto es primordial que se realicen diversos ensayos y análisis en el Perú utilizando equipos de laboratorio y de campo especializados quenas permitan evaluar la estructura de pavimento para evitar la deformación permanente. Esto conlleva a la necesidad de desarrollar nuevas especificaciones técnicas para mezclas asfálticas que dependiendo de los resultados del análisis, puedan incluir el uso de modificadores como polímeros, polvo de caucho, y la aplicación de la tecnología SUPERPAVE para una mejor caracterización de los materiales constituyentes de la mezcla asfáltica con el propósito de incrementar la durabilidad de los pavimentos asfálticos .

A Hybrid Technique for Calibrating Network Performance Models of Continuously Reinforced Concrete Pavements

Pavement performance models exist in various forms to cater to the pavement management agencies’ needs and resources. Well-calibrated models are needed to accurately predict future pavement conditions and to forecast and prioritize confidently the future rehabilitation and maintenance expenditures. Statistical tools are commonly used to develop the performance models. These statistical models may be impractical or misleading if they do not consider experts’ opinions. This paper presents a hybrid technique where statistical tools and expert knowledge are combined for the calibration of pavement performance models. This technique was validated using historical pavement condition data for continuously reinforced concrete pavements (CRCP) from the Texas Department of Transportation’s pavement management information system (TxDOT-PMIS). The recalibrated CRCP performance models obtained with the hybrid technique represent an improvement when compared to the current models since they merge expert opinion and statistical analysis, which better reflect field observations regarding distress initiation, distress evolution rate, and maximum allowable amount of distress growth

Measurement-Type Calibration of Expert Estimates Improves Their Accuracy and Their Usability: Pavement Engineering Case Study

In many applications areas, including pavement engineering, experts are used to estimate the values of the corresponding quantities. Expert estimates are often imprecise. As a result, it is difficult to find experts whose estimates will be sufficiently accurate, and for the selected experts, the accuracy is often barely within the desired accuracy. A similar situations sometimes happens with measuring instruments, but usually, if a measuring instrument stops being accurate, we do not dismiss it right away, we first try to re-calibrate it -- and this re-calibration often makes it more accurate. We propose to do the same for experts -- calibrate their estimates. On the example of pavement engineering, we show that this calibration enables us to select more qualified experts, and make estimates of the current experts more accurate

Calibration Helps Reduce Disagreement Between Different Pavement Condition Indices

To make expert estimates of pavement condition more accurate, the American Society for Testing and Materials (ASTM) split one of the original pavement distress categories, for which experts previously provided a single numerical estimate, into two subcategories to be estimated separately. While this split has indeed made expert estimates more accurate, there is a problem: to get a good understanding of the road quality, we would like to see how this quality changed over time, and it is not easy to compare past estimates (based on the old methodology) with the new estimates, which are based on the new after-split methodology. In this paper, we show that a linear calibration reduced disagreement between these two types of estimates -- and thus, leads to a more adequate picture of how the road quality changes with time

Towards a Theoretical Explanation of How Pavement Condition Index Deteriorates over Time

To predict how the Pavement Condition Index will change over time, practitioners use a complex empirical formula derived in the 1980s. In this paper, we provide a possible theoretical explanation for this formula, an explanation based on general ideas of invariance. In general, the existence of a theoretical explanation makes a formula more reliable; thus, we hope that our explanation will make predictions of road quality more reliable

Relationship Between Measurement Results and Expert Estimates of Cumulative Quantities, on the Example of Pavement Roughness

In many practical situation, we are interesting in values of cumulative quantities -- e.g., quantities that describe the overall quality of a long road segment. Some of these quantities we can measure, but measuring such quantities requiring measuring many local values and is, thus, expensive and time-consuming. As a result, in many cases, instead of the measurement, we reply on expert estimating such cumulative quantities on a scale, e.g., from 0 to 5. Researchers have come up with an empirical formula that provides a relation between the measurement result and a 0-to-5 expert estimate. In this paper, we provide a theoretical explanation for this empirically efficient formula

How to Estimate Pavement Roughness: Beyond International Roughness Index

The standard way to describing the road\u27s roughness it to use a single numerical characteristics called International Roughness Index (IRI). This characteristic describes the effect of the road roughness on a vehicle of standard size. To estimate IRI, practitioners tried to use easily available vehicles (whose size may be somewhat different) and then estimate IRI based on these different-size measurements. The problem is that the resulting estimates of IRI are very inaccurate -- which means that a single numerical characteristic like IRI is not sufficient to properly describe road roughness. In this paper, we show that the road roughness can be described by a fractal (power law) model. As a result, we propose to supplement IRI with another numerical characteristic: the power-law exponent that describes how the effect of roughness changes when we change the size of the vehicle

A Dynamic Programming Optimization Approach for Budget Allocation to Early Right-of-Way Acquisitions

Maximizing potential savings when purchasing right-of-way within a limited budget is a challenge currently faced by state departments of transportation (DOTs) across the nation. Early right-ofway acquisitions promote smoother negotiations and are aimed to save money, time, and human resources. This paper describes an optimization approach based on dynamic programming developed for the Texas Department of Transportation (TxDOT) to identify projects with candidate parcels for early right-of-way acquisition in order to achieve the highest potential savings. Each candidate parcel must be subjected to a preliminary environmental analysis to ensure that each comply with the National Environmental Policy Act (NEPA) standards

A Systematic Statistical Approach to Populate Missing Performance Data in Pavement Management Systems

Transportation agencies use pavement management systems (PMS) for their maintenance and rehabilitation planning, programming, and budgeting. PMS is used to make decisions regarding when maintenance and rehabilitation should be applied. To support these decisions, it is important to have reliable data on pavement conditions and accurate performance models for predicting pavement condition. The data on pavement condition typically come from regular field surveys resulting in distress, condition, and ride scores. PMS data sets are often incomplete (for some locations and some years) as a result of operational limitations reducing the predictive power of the performance models. Model-free and model-based replacement techniques for estimating missing data points have been designed and successfully used in other application areas like statistics, economics, marketing, medicine, psychometrics, and political science. It is therefore reasonable to apply these methods to the PMS databases. Statistical techniques are assembled and used in a robust approach to systematically analyze the effect of applying these techniques to rebuild missing performance data. As a case study, continuous reinforced concrete pavement (CRCP) sections were selected to test the proposed statistical systematic approach from a pavement management information system (PMIS) maintained by the Texas Department of Transportation (TxDOT). A major effect was observed in the results of predicting the distress scores when applying the developed approach

Fuzzy Ideas Explain a Complex Heuristic Algorithm for Gauging Pavement Conditions

To gauge pavement conditions, researchers have come up with a complex heuristic algorithm that combines several expert estimates of pavement characteristics into a single index -- which is well correlated with the pavement\u27s durability and other physical characteristics. While empirically, this algorithm works well, it lacks physical or mathematical justification beyond being a good fit for the available data. This lack of justification decreases our confidence in the algorithm\u27s results -- since it is known that often, empirically successful heuristic algorithms need change when the conditions change. To increase the practitioners\u27 confidence in the resulting pavement condition estimates, it is therefore desirable to come up with a theoretical justification for this algorithm. In this paper, we show that by using fuzzy techniques, it is possible to come up with the desired justification