Construction of Performance Model of Tile CAQR and Performance Result of the Implementation

2017 ◽  
Author(s):  
Masatoshi Takayanagi ◽  
Tomohiro Suzuki
Keyword(s):  
Performance Model ◽  
Performance Result ◽  
And Performance

Local Calibration of Pavement Mechanistic-Empirical Faulting Reliability using Pavement Management Data

2021 ◽  
pp. 036119812110013
Author(s):  
Lucio Salles de Salles ◽  
Lev Khazanovich
Keyword(s):  
Model Calibration ◽  
Prediction Models ◽  
Plain Concrete ◽  
Performance Model ◽  
Pavement Management ◽  
Pavement Design ◽  
Reliability Model ◽  
Transverse Joint ◽  
Design Characteristics ◽  
And Performance

The Pavement ME transverse joint faulting model incorporates mechanistic theories that predict development of joint faulting in jointed plain concrete pavements (JPCP). The model is calibrated using the Long-Term Pavement Performance database. However, the Mechanistic-Empirical Pavement Design Guide (MEPDG) encourages transportation agencies, such as state departments of transportation, to perform local calibrations of the faulting model included in Pavement ME. Model calibration is a complicated and effort-intensive process that requires high-quality pavement design and performance data. Pavement management data—which is collected regularly and in large amounts—may present higher variability than is desired for faulting performance model calibration. The MEPDG performance prediction models predict pavement distresses with 50% reliability. JPCP are usually designed for high levels of faulting reliability to reduce likelihood of excessive faulting. For design, improving the faulting reliability model is as important as improving the faulting prediction model. This paper proposes a calibration of the Pavement ME reliability model using pavement management system (PMS) data. It illustrates the proposed approach using PMS data from Pennsylvania Department of Transportation. Results show an increase in accuracy for faulting predictions using the new reliability model with various design characteristics. Moreover, the new reliability model allows design of JPCP considering higher levels of traffic because of the less conservative predictions.

Business Analytics and Firm Agility and Performance--Model

2019 ◽  
Author(s):  
Amir Ashrafi ◽  
Ahad Zare Ravasan ◽  
Peter Trkman ◽  
Samira Afshari
Keyword(s):  
Performance Model ◽  
Business Analytics ◽  
And Performance

scholarly journals Defining an Industry: What is the Size and Scope of the Australian Building and Construction Industry

1970 ◽  
Vol 10 (1-2) ◽  
pp. 119-131 ◽  
Author(s):  
Gerard De Valence
Keyword(s):  
Construction Industry ◽  
Performance Model ◽  
Cluster Approach ◽  
Industry Cluster ◽  
Traditional Structure ◽  
Economic Contribution ◽  
And Performance ◽  
Using Data ◽  
Traditional Industry ◽  
Different Characteristics

This is a reprint from Vol 1, no 1, which has not previously been available in electronic format.The analysis and understanding of the conduct and performance of an industry begins with a study of its structure. However, before analysing an industry's structure it is necessary to define the industry and identify its size, scope and scale to establish its true economic contribution. This paper discusses the size and scope of the Australian building and construction industry, firstly froma traditional industry economics approach by firm size and business characteristics using data fron three construction industry surveys done over 15 years by the ABS. Secondly, data from an industry 'cluster' perspective is shown. The objective of the paper is to compare the differences found in industry size and scope in the structure-conduct-performance approach and the alternative industry cluster approach. Each model reveals different characteristics of the industry. The conclusion finds that the building and construction industry is a case where the traditional structure-conduct-performance model cannot be easily applied. 

Performance Variations Among Strategic Group Members: An Examination of Individual Firm Capability

1989 ◽  
Vol 15 (4) ◽  
pp. 649-661 ◽  
Author(s):  
Michael W. Lawless ◽  
Donald D. Bergh ◽  
William D. Wilsted
Keyword(s):  
Explanatory Power ◽  
Group Analysis ◽  
Performance Model ◽  
Strategic Groups ◽  
Strategic Group ◽  
Common Strategy ◽  
Firm Capability ◽  
Group Members ◽  
And Performance ◽  
Performance Variations

Because of inconsistent empirical evidence, the membership-performance model pervasive in strategic group analysis is re-examined. We propose that individualfirm capabilities, which reflect capacity to implement or change strategy, moderate the effect of members' shared strategy characteristics on performance. Controlling for market structure, we defined two strategic groups based on common strategy characteristics among 55 manufacturing firms. We found significant differences in performance and capabilities within each group. There was also evidence of a significant correlation between capabilities and performance within each group. We conclude that effects offirms' capabilities should be accountedfor to increase the explanatory power of strategic groups in competitive performance.

CARBON BLACK FOR RACING AND MOTORCYCLE TIRE TREADS. PERFORMANCE MODEL DEVELOPMENT

2011 ◽  
Vol 84 (4) ◽  
pp. 493-506
Author(s):  
Irene S. Yurovska ◽  
Michael D. Morris ◽  
Theo Al
Keyword(s):  
Carbon Black ◽  
Critical Role ◽  
Model Development ◽  
Aggregate Size ◽  
Rolling Resistance ◽  
Performance Model ◽  
Average Life ◽  
Carbon Blacks ◽  
Truck Tires ◽  
And Performance

Abstract Racing tires and motorcycle tires present individual segments of the tire market. For instance, while the average life of car and truck tires is 50 000 miles, the average life of race tires is 100 miles. Because tires play a critical role in a race, technical demands to assure safety and performance are growing. Similarly, tires have a large influence on safety, handling/grip, and performance of the rapidly growing world fleet of motorcycles, due to the fact of only two wheels being in contact with the ground. Thus, the common feature of both market segments is that the typical tire compromise of wear, rolling resistance, and traction is strongly weighted toward traction. Most of the recent efforts of rubber scientists have been directed toward lowering rolling resistance of the tread compounds, which left a certain void in the science of compounding for racing and motorcycle treads. Particularly, the industrial assortment of polymers and fillers used for motorcycle treads is commonly different from that used for car or truck treads, but it is not known how the filler properties affect the hysteresis–stiffness compromise. The objective of this study is to evaluate the effects of the carbon black characteristics on the important properties of a typical racing and motorcycle tire tread compound. More than 50 individual carbon blacks were mixed in a SBR formulation. The acquired data were statistically analyzed, and a linear multiple regression model was developed to relate rubber properties (responses), such as static modulus, complex dynamic modulus, hysteresis, and viscosity to the key carbon black characteristics (variables) of surface area, structure, aggregate size distribution, and surface activity. Prediction profiles created from the model demonstrate rubber performance limits for the range of carbon blacks tested, and indicate the niches to provide required combinations of the rubber properties.

Towards a Multi-Formalism Multi-Solution Framework for Model-Driven Performance Engineering

2013 ◽  
pp. 34-55
Author(s):  
Catalina M. Lladó ◽  
Pere Bonet ◽  
Connie U. Smith
Keyword(s):  
Queueing Network ◽  
Network Models ◽  
Performance Model ◽  
Analysis Model ◽  
Model Driven ◽  
Performance Engineering ◽  
Performance Specifications ◽  
Interchange Format ◽  
And Performance ◽  
Model Interchange

Model-Driven Performance Engineering (MDPE) uses performance model interchange formats among multiple formalisms and tools to automate performance analysis. Model-to-Model (M2M) transformations convert system specifications into performance specifications and performance specifications to multiple performance model formalisms. Since a single tool is not good for everything, tools for different formalisms provide multiple solutions for evaluation and comparison. This chapter demonstrates transformations from the Performance Model Interchange Format (PMIF) into multiple formalisms: Queueing Network models solved with Java Modeling Tools (JMT), QNAP, and SPE·ED, and Petri Nets solved with PIPE2.

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