Iowa Experience on Local Calibration of AASHTOWare Pavement ME Design (PMED) for Jointed Plain Concrete Pavements

The AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) pavement performance models and the associated AASHTOWare pavement ME design (PMED) software are nationally calibrated using design inputs and distress data largely obtained from National Long-Term Pavement Performance (LTPP) to predict Jointed Plain Concrete Pavement (JPCP) performance measures. To improve the accuracy of nationally-calibrated JPCP performance models for various local conditions, further calibration and validation studies in accordance with the local conditions are highly recommended, and multiple updates have been made to the PMED since its initial release in 2011, with the latest version (i.e., Ver. 2.5.X) becoming available in 2019. Validation of JPCP performance models after such software updates is necessary as part of PMED implementation, and such local calibration and validation activities have been identified as the most difficult or challenging parts of PMED implementation. As one of the states at the forefront of implementing the MEPDG and PMED, Iowa has conducted local calibration of JPCP performance models extending from MEPDG to updated versions of PMED. The required MEPDG and PMED inputs and the historical performance data for the selected JPCP sections were extracted from a variety of sources and the accuracy of the nationally-calibrated MEPDG and PMED performance prediction models for Iowa conditions was evaluated. To improve the accuracy of model predictions, local calibration factors of MEPDG and PMED performance prediction models were identified and gained local calibration experiences of MEPDG and PMED in Iowa are presented and discussed here to provide insight of local calibration for other State Highway Agencies (SHAs).

Victorious and Hierarchical: Past Performance as a Determinant of Team Hierarchical Differentiation

Hierarchies emerge as collectives attempt to organize themselves toward successful performance. Consequently, research has focused on how team hierarchies affect performance. We extend existing models of the hierarchy-performance relationship by adopting an alternative: Performance is not only an output of hierarchy but also a critical input, as teams’ hierarchical differentiation may vary based on whether they are succeeding. Integrating research on exploitation and exploration with work on group attributions, we argue that teams engage in exploitation by committing to what they attribute as the cause of their performance success. Specifically, collectives tend to attribute their success to individuals who wielded greater influence within the team; these individuals are consequently granted relatively higher levels of influence, leading to a higher degree of hierarchy. We additionally suggest that the tendency to attribute, and therefore grant more influence, to members believed to be the cause of success is stronger for teams previously higher (versus lower) in hierarchy, as a higher degree of hierarchical differentiation provides clarity as to which members had a greater impact on the team outcome. We test our hypotheses experimentally with teams engaging in an online judgement task and observationally with teams from the National Basketball Association. Our work makes two primary contributions: (a) altering existing hierarchy-performance models by highlighting performance as both an input and output to hierarchy and (b) extending research on the dynamics of hierarchy beyond individual rank changes toward examining what factors increase or decrease hierarchical differentiation of the team as a whole.

Economic Impact Analysis of the Application of Different Pavement Performance Models on First-Class Roads with Selected Repair Technology

Mathematical expression of the deterioration of individual pavement parameters is, from the point of optimal repair and maintenance strategy decision-making process, an important part of the application of any pavement management system (PMS). The reliability of individual PMS depends on the quality of the inputs and the reliability of its internal sub-systems; thus, deterioration equations derived from high-quality input data play pivotal roles in a system for the prediction of the pavement life cycle. This paper describes the application of pavement performance models within pavement life cycle analysis (LCA) with the use of the integrated system of economic evaluation (ISEH), which is a calculation tool used for first-class roads with a standardized pavement composition of asphalt binders, where changes in operational capability parameters are modeled using individual model simulations. The simulations presented in this paper demonstrate changes in main economic indicators (net present value and internal rate of return) on two different pavement performance models. Both simulations share the same input parameters (traffic intensity, construction intervention, maintenance costs, discount rate) but differ in deterioration evaluation, all of which were applied to each model (a total of five models).

Roughness Modeling for Composite Pavements using Machine Learning

A large number of paved highway surfaces comprises composite pavements as a result of concrete pavement rehabilitation that uses an asphalt overlay on top of the concrete surface. Annually, billions of dollars are spent on the maintenance and rehabilitation of road networks. Roughness is one of the several indicators of road conditions used to make objective decisions related to road network management. The irregularities in the pavement surface affecting the ride quality of road users can be described by a standard roughness index defined as the International Roughness Index (IRI). Roughness prediction models can identify rehabilitation needs, analyze rehabilitation effects, and estimate future pavement conditions to implement different Maintenance and Rehabilitation (M&R) activities to extend the pavement life cycle and provide a smooth surface for road users. This study intended to develop pavement performance models to predict roughness for asphalt overlay on concrete pavement sections using the Long-Term Performance Pavement (LTPP) program database. Artificial Neural Networks (ANNs) approach was used to develop roughness prediction models. A total of 52 pavement sections with 592 data points were analyzed. Five models were developed, and the best performing model, Model 5 was found with an average square error (ASE) of 0.0023, mean absolute relative error (MARE) of 12.936, and coefficient of determination (R2) of 0.88. Model 5 utilized one output variable (IRIMean) and 14 input variables (i.e., Initial IRIMean, Age, Wet-Freeze, Wet Non-Freeze, Dry-Freeze, Dry Non-Freeze, Asphalt Thickness, Concrete Thickness, CN Code, ESAL, Annual Air Temperature, Freeze Index, Freeze-Thaw, and Precipitation). The ANN model structure utilized for Model 5 was 14-9-1 (14 inputs, 9 hidden nodes, and 1 output). Environmental impacts and traffic repetitions can cause severe damage to the pavement if timely maintenance and rehabilitation are not performed. By considering the effects of the M&R history of the pavement, it is possible to obtain realistic prediction models for future planning. Therefore, the developed ANN roughness performance models in this paper can be used as a prediction tool for IRI values and guide decision-makers to develop a better M&R plan. Local and state agencies can use available historical traffic and climatological data in the developed models to estimate the change in IRI values. Utilizing these prediction models eliminates time-consuming data collection and post-processing, and consequently, a cost reduction. This low-cost tool will improve the condition assessment and effective M&R scheduling.

Chinese securities investment funds: the role of luck in performance

Purpose The Chinese fund market has witnessed significant developments in recent years. However, although there has been a range of studies assessing fund performance in developed industries, the rapidly developing fund industry in China has received very little attention. This study aims to examine the performance of open-end securities investment funds investing in Chinese domestic equity during the period May 2003 to September 2020. Specifically, applying a non-parametric bootstrap methodology from the literature on fund performance, the authors investigate the role of skill versus luck in this rapidly evolving investment funds industry. Design/methodology/approach This study evaluates the performance of Chinese equity securities investment funds from 2003–2020 using a bootstrap methodology to distinguish skill from luck in performance. The authors consider unconditional and conditional performance models. Findings The bootstrap methodology incorporates non-normality in the idiosyncratic risk of fund returns, which is a major drawback in “conventional” performance statistics. The evidence does not support the existence of “genuine” skilled fund managers. In addition, it indicates that poor performance is mainly attributable to bad stock picking skills. Practical implications The authors find that the top-ranked funds with positive abnormal performance are attributed to “good luck” not “good skill” while the negative abnormal performance of bottom funds is mainly due to “bad skill.” Therefore, sensible advice for most Chinese equity investors would be against trying to “pick winners funds” among Chinese securities investment funds but it would be recommended to avoid holding “losers.” At the present time, investors should consider other types of funds, such as index/tracker funds with lower transactions. In addition, less risk-averse investors may consider Chinese hedge funds [Zhao (2012)] or exchange-traded fund [Han (2012)]. Originality/value The paper makes several contributions to the literature. First, the authors examine a wide range (over 50) of risk-adjusted performance models, which account for both unconditional and conditional risk factors. The authors also control for the profitability and investment risks in Fama and French (2015). Second, the authors select the “best-fit” model across all risk-adjusted models examined and a single “best-fit” model from each of the three classes. Therefore, the bootstrap analysis, which is mainly based on the selected best-fit models, is more precise and robust. Third, the authors reduce the possibility that findings may be sample-period specific or may be a survivor (upward) biased. Fourth, the authors consider further analysis based on sub-periods and compare fund performance in different market conditions to provide more implications to investors and practitioners. Fifth, the authors carry out extensive robustness checks and show that the findings are robust in relation to different minimum fund histories and serial correlation and heteroscedasticity adjustments. Sixth, the authors use higher frequency weekly data to improve statistical estimation.

A Novel Method to Speedup Calibrating Horizontal Well Performance Model with Multi-Stage Fracturing Treatments and Its Applications in Delaware Basin

The multi-stage fracture treatments create complex fracture networks with various proppant type, size, and concentration distributed within and along fractures through reservoir rock, where larger size and higher concentrations usually result in higher long-term conductivity. To model the fracture conductivity reduction with depletion, we traditionally use a single monotonic relationship between fracture conductivity and pressure, which is proper for a single proppant concentration but obviously hard to describe the situation in the horizontal wells with complex concentration distributions. This paper is to present a new method to speed-up the calibration process of well performance models with multi-million cells and its two applications in the Wolfcamp reservoir in the Delaware Basin. To study well performance and completion effectiveness of 3000 horizontal wells over University Lands acreage in the Permian Basin, we have built a series of well performance models with complex fracture networks (SPE 189855 and 194367). We have used those models to methodically investigate the drivers of well completion parameters and well spacing on well performance and field development value (URTeC 554). In the process of building multiple robust well performance models, we found out it is hard and time-consuming to calibrate a well performance model with multi-million cells based upon a single correlation between fracture conductivity and pressure. We first modeled the complex fracture networks and fracture conductivity distributions based upon the historical completion pumping data; we then developed multiple correlations to characterize fracture conductivity reduction and closure behaviors with pressure depletion based upon initial fracture conductivities (as the result of proppant type, size, and concentration) and reservoir geomechanical properties. We found out that this method significantly reduced our model calibration time. We then applied our method to multiple case studies in the Permian Basin to test and improve the method. We have thus developed a method to mimic the fracture conductivity reduction and closure behavior in the horizontal wells with complex fracture networks. The paper will layout the theoretical foundation and detail our method to develop the multiple correlations to model fracture conductivity reduction and fracture closure behaviors in the horizontal well performance models in the unconventional reservoirs. We will then show two case studies to illustrate how we have applied our method to speed up the model calibration process. Based upon the multiple applications into our model calibration process, we have concluded that the method is very effective to calibrate the well performance model with complex fracture networks. The method can be used for engineers to simplify and speedup calibrating horizontal well performance models. Therefore, engineers can more effectively build more robust well performance models to optimize field development plans in the unconventional reservoirs.