Performance Monitoring Data Research Articles

Quality of Transmission Estimation and Short-Term Performance Forecast of Lightpaths

With ever-increasing traffic, the need of more dynamic, flexible, and autonomous optical networks is more important than ever. The availability of performance monitoring data makes it possible to leverage machine learning (ML) for fast quality of transmission (QoT) estimation and performance prediction of lightpaths in complex optical networks. In this article, we will explore classifiers based on support vector machine (SVM) and artificial neural network (ANN) for QoT estimation of unestablished lightpaths. Using a synthetic knowledge base (KB), the classification accuracy of the ANN and SVM models decreased from 99%, with a complete feature set, to 85.03% and 88.52%, respectively, with a reduced feature set. We also propose a Long Short-Term Memory (LSTM), an Encoder-Decoder LSTM and a Gated Recurrent Unit (GRU) models, trained with 13-months field performance data, for lightpath signal-to-noise (SNR) prediction over forecast horizons up to four days. Positive R2 values combined with low (<0.285 dB) root mean square error (RMSE) indicated that the GRU model achieved slightly better predictions than the naive method for forecast horizons ranging from 1 to 96 hours, whereas the LSTM performed better over 24 to 96-hour forecast horizons. The Encoder-Decoder LSTM model achieved the lowest R2 and the highest RMSE values (0.296 dB). Additional input data will be needed to improve the prediction accuracy of the LSTM and GRU models trained with single lightpath data.

Abstract WP189: Application of a Continuous Quality Improvement Framework: Longitudinal Results From Year 1 of the Stroke Survivor to Survivor Program

In 2018, the Mission Lifeline North Dakota (ND) initiative adopted and began to implement Stroke Survivors Empowering Each Other, Inc.’s (SSEEO’s) Stroke Survivor to Survivor (SS2S) program. The SS2S program is a post-stroke support program that aims to facilitate survivors’ efforts to recover after stroke. Volunteers, who are themselves survivors, call participants monthly at least twice following discharge from the hospital and provide them with stroke-related resources and support. The adaptation and implementation of the Chicago-borne SS2S program in ND was embedded within a continuous quality improvement framework to ensure ongoing performance monitoring data could inform programmatic improvements as needed. Key questions: • How has the Chicago-borne SS2S program been adapted to fit the unique characteristics and environment of ND, while maintaining fidelity? • What are performance monitoring findings from the first three quarters of implementation? Between Oct 2018- Feb 2019, four of the six ND tertiary hospitals implemented the SS2S program. These hospitals provide monthly quantitative and qualitative data on program implementation. From Oct 2018 to May 2019, SS2S volunteers made 153 calls to survivors, with 57% (n = 87 of 153) of calls answered by a prospect, including stroke survivors (n=74) and their caregivers (n=13). Thirteen percent (n = 9 of 69) of stroke survivors calls required additional follow-up by hospital staff. SS2S volunteers mailed additional resources to stroke survivors for 48% percent of the calls where the prospect was reached (n= 33 of 69). Seventy percent (n = 48 of 69) of survivors reached were able to identify at least one sign of stroke. Preliminary results suggest that both volunteers and stroke survivors may benefit from the program. Importantly, performance monitoring data also identified opportunities for programmatic improvements. For example, these data informed edits to the volunteers’ phone scripts to clarify signs-of-stroke patient-education. Ongoing feedback from the hospitals have also informed improvements to the performance monitoring processes.

Efficiency assessment of higher education institutions: Contradictions and paradoxes. Part II

Introduction. The development of a clear and understandable strategy for the development of higher education, focused on training, which will be in demand by the modernised economy in the strategic perspective, is one of the conditions for the implementation of the Decree the President of the Russian Federation of 07 May2018. In this regard, the importance of an adequate efficiency assessment of the training system in general and in higher education is growing.Aim. In the context of the initiated discussion on methodological improvement of efficiency assessment of universities, the aims of the present article are the following: to discuss the existing criteria and indicators (proposed by the macro-regulator) of the expertise; to justify the inconsistency of such criteria and indicators to obtain real information on the innovative potential and the quality of human potential of graduates.Methodology and research methods. The research work was carried out on the basis of a systematic approach and inductive research method. To confirm the hypothesis, the method of comparative analysis and the following general scientific methods were applied: analytical review of scientific literature and normative documents, generalisation, comparison and analogy.Results and scientific novelty. Based on the performance monitoring data, the author presents the results of the evaluation of some universities, recognised as centers of innovation, technological and social development of the regions according to the indicators proposed by the Ministry of Education and Science. The conducted analysis indicates the complexity of the formulation of conclusions regarding the innovative potential of universities due to the incorrectness of the approach proposed by the macro-regulator. The shortcomings of the new project proposed by the Ministry of Science and Higher Education to assess the effectiveness of universities and their leaders are revealed. It is necessary to revise the methodology applied. When assessing university efficiency, principle focus should be emphasised on: a) identifying the fact and the dynamics of the increment of knowledge and skills of trainees (primarily, the degree of formation of abilities to learn and independently gain new knowledge); b) assessment of the economic component of the human potential of graduates, involving the introduction of indicators, by which it is possible to judge their demand in the labour market. Three measurement control points are proposed: the level of remuneration at the initial stage of career, in five and ten years after graduation. As a basic indicator of the university innovation activity, it is proposed to consider the share of the profits derived from the use of intellectual activity results in the total income of the organisation. In technical universities, this indicator can be used with additional indicators: number of patents per 100 scientific and pedagogical workers; the number of patents per 100 published articles indexed in the world scientometric databases. In order to maximise the potential of in-service teachers, the principles of financing universities should be changed, as well as the target rates should be introduced to attract young scientific and pedagogical personnel.Practical significance. The research results can be used to improve the methodology for efficiency assessment of universities, as well as to avoid the institutional traps in higher education and science.

Analyse resilience risks in microservice architecture systems with causality search and inference algorithms

The microservice architecture has already become the mainstream architecture pattern of web service applications in recent years. However, compared with traditional software architectures, the microservice architecture has a more sophisticated deployment structure, which makes it have to face more potential risks with greater diversity of fault symptoms. Microservice practitioners started to use the word 'resilience' to describe the capability of coping with different unexpected conditions. How to judge whether a system environment disruption is a risk of microservice resilience, and how to analyse resilience risks before the system is released, are the research questions in microservice development. As the practice of chaos engineering has solved the problem of resilience risk identification, this paper focuses on how to analyse identified resilience risks in microservice architecture systems, and a resilience risk analysis method is proposed. Based on performance monitoring data collected during chaos experiments, the analysis method uses the causality search algorithm to build causality graphs of performance indicators, and generates causality chains to system operators by the causality inference algorithm. The effectiveness of the proposed approach is proved by conducting a case study on a microservice architecture system.

Analyse resilience risks in microservice architecture systems with causality search and inference algorithms

The microservice architecture has already become the mainstream architecture pattern of web service applications in recent years. However, compared with traditional software architectures, the microservice architecture has a more sophisticated deployment structure, which makes it have to face more potential risks with greater diversity of fault symptoms. Microservice practitioners started to use the word 'resilience' to describe the capability of coping with different unexpected conditions. How to judge whether a system environment disruption is a risk of microservice resilience, and how to analyse resilience risks before the system is released, are the research questions in microservice development. As the practice of chaos engineering has solved the problem of resilience risk identification, this paper focuses on how to analyse identified resilience risks in microservice architecture systems, and a resilience risk analysis method is proposed. Based on performance monitoring data collected during chaos experiments, the analysis method uses the causality search algorithm to build causality graphs of performance indicators, and generates causality chains to system operators by the causality inference algorithm. The effectiveness of the proposed approach is proved by conducting a case study on a microservice architecture system.

Efficiency Assessment of Higher Education Institutions: Contradictions and Paradoxes. Part I

higher education, focused on training, which will be in demand by the modernised economy in the strategic perspective, is one of the conditions for the implementation of the Decree the President of the Russian Federation of 07 May 2018. In this regard, the importance of an adequate efficiency assessment of the training system in general and in higher education is growing.Aim. In the context of the initiated discussion on methodological improvement of efficiency assessment of universities, the aims of the present article are the following: to discuss the existing criteria and indicators (proposed by the macroregulator) of the expertise; to justify the inconsistency of such criteria and indicators to obtain real information on the innovative potential and the quality of human potential of graduates.Methodology and research methods. The research work was carried out on the basis of a systematic approach and inductive research method. To confirm the hypothesis, the method of comparative analysis and the following general scientific methods were applied: analytical review of scientific literature and normative documents, generalisation, comparison and analogy.Results and scientific novelty. Based on the performance monitoring data, the author presents the results of the evaluation of some universities, recognised as centers of innovation, technological and social development of the regions according to the indicators proposed by the Ministry of Education and Science. The conducted analysis indicates the complexity of the formulation of conclusions regarding the innovative potential of universities due to the incorrectness of the approach proposed by the macro-regulator. The shortcomings of the new project proposed by the Ministry of Science and Higher Education to assess the effectiveness of universities and their leaders are revealed. It is necessary to revise the methodology applied. When assessing university efficiency, principle focus should be emphasised on: a) identifying the fact and the dynamics of the increment of knowledge and skills of trainees (primarily, the degree of formation of abilities to learn and independently gain new knowledge); b) assessment of the economic component of the human potential of graduates, involving the introduction of indicators, by which it is possible to judge their demand in the labour market. Three measurement control points are proposed: the level of remuneration at the initial stage of career, in five and ten years after graduation. As a basic indicator of the university innovation activity, it is proposed to consider the share of the profits derived from the use of intellectual activity results in the total income of the organisation. In technical universities, this indicator can be used with additional indicators: number of patents per 100 scientific and pedagogical workers; the number of patents per 100 published articles indexed in the world scientometric databases. In order to maximise the potential of in-service teachers, the principles of financing universities should be changed, as well as the target rates should be introduced to attract young scientific and pedagogical personnel.Practical significance. The research results can be used to improve the methodology for efficiency assessment of universities, as well as to avoid the institutional traps in higher education and science.

Groundwater heat pump feasibility in shallow urban aquifers: Experience from Cardiff, UK

Ground source heat pumps have the potential to decarbonise heating and cooling in many urban areas. The impact of using shallow groundwater from unconsolidated sedimentary aquifers for heating in urban areas is often modelled, but rarely validated from field measurements. This study presents findings from the ‘Cardiff Urban Geo-Observatory’ project. This study focuses on an experimental open loop ground source heat pump scheme retrofitted to a school building. Field monitoring for three years between 2015 and 2018 provided data on the environmental impact of the scheme on aquifer conditions. Average aquifer thermal degradation in the first three years was kept below 2 °C, with a maximum change of 4 °C measured during the heating season. The numerically modelled predictions of thermal degradation around the production and injection wells are compared with long-term field monitoring data, providing new insights into both aquifer, and user, behaviour. The Seasonal Performance Factor (SPFH4) of the pilot installation was 4.5 (W13/W50) in the monitoring period. An initial thermal resource estimation of the wider aquifer volume suggests that lowering the temperature of the aquifer by 8 °C could generate equivalent to 26% of the city's 2020 heating demand, but achievable heat extraction would in reality, be less. The study concludes that large parts of the aquifer can sustain shallow open loop ground source heat pump systems, as long as the local ground conditions support the required groundwater abstraction and re-injection rates. Future schemes can be de-risked and better managed by introduction of a registration of all GSHP schemes, with open sharing of investigation, design and performance monitoring data, and by managing thermal interference between systems using spatial planning tools.

The importance of fire in the success of a 15 hectare subtropical heathland translocation

Fire is a key factor in the maintenance of many ecosystems; however, little is known on how it affects the success of translocated fire-dependent plant species and communities. Further, translocation success is currently limited, creating uncertainty around the effectiveness of translocations in mitigating the negative impacts of urbanisation on biodiversity. Translocation evaluations may improve future outcomes by providing vital information: therefore, the aim of the present study was to investigate the long-term success of a large-scale subtropical heathland translocation as well as the impacts of a fire management regime eight years after it was completed. Original performance criteria and monitoring data combined with new site surveys were utilised to assess the ongoing success of the translocation. Specifically, the structure and composition of the heath vegetation and the population size and demographic structure of five specific threatened plant species were assessed. Results showed that translocation still met the performance criteria requirements and so can continue to be classed as a success. Fire played a key role in enabling the vegetation to meet the performance criteria by promoting regeneration of target species and maintaining community composition. This was likely supported by the immediate availability of seed through the preservation of the seedbank. Long-term success in translocated fire-dependent plant species and communities will depend largely on the application of appropriate fire regimes.

Prediction Method of Asphalt Pavement Performance and Corrosion Based on Grey System Theory

The Grey system theory is a new mathematical method to predict data changes in the poor data integrity. As a branch of Grey system theory, the GM (1, 1) model is widely used because only small sample data and simple calculations are needed in prediction of engineering project. It is a critical problem to effectively predict the performance and corrosion of asphalt pavement of highway construction due to the inadequacy of highway performance monitoring data. The smoothness, rut, and pavement skid resistance are three important indexes to evaluate the performance and corrosion of asphalt pavement. This paper has established the prediction model and derived prediction equation of asphalt pavement performance according to the GM (1, 1) model method and then listed the calculation equation of residual and the gray absolute correlation degree. Based on the experience of constructed Dalian-Guangzhou expressway in China, the vectors “a” and “b” in the prediction equation of smoothness, rut, and pavement skid resistance have been calculated by using the original monitoring data. The field monitoring data are compared with the predictive data for residual and the gray absolute correlation. The results reveal that the predicted data of the smoothness, rut, and skid resistance are mostly consistent with the monitoring data, the biggest residual of the above three indexes is smaller than 8.09%, and the gray absolute correlation degrees all exceed 0.9, which means the accuracy of the predicted equation is excellent. The calculation method based on GM (1, 1) model can effectively predict the changing performance index of asphalt pavement.

Performance Evaluation of JPCP with Changes of Pavement Mix Design Using Pavement Management Data

This study aimed to analyze long‐term performance of JPCP (jointed plain concrete pavement) according to changes in standard mix design using evaluation of concrete properties based on Korea HPMS (highway pavement management system) and Korea LTPP (long‐term pavement performance) data accumulated for over 15 years. The concrete pavements built in the 2010s by the specification of a durability‐based mix design adopted in 2010 were found to have better performance with much fewer surface distresses than the concrete pavements built before 2010 by the specification of a classical strength‐based mix design. Also, in order to realize long‐life concrete pavement, experimental construction was carried out for high‐durability concrete mix design. The performance monitoring data for the construction site implied that the high‐durability mix design can make it possible to lead a long‐life concrete pavement.

Long-term performance predictions in ground improvements with vacuum assisted Prefabricated Vertical Drains

Investigation into time dependent long-term performance of Prefabricated Vertical Drains (PVDs) combined with vacuum consolidation in thick deposits of clay has been extremely limited. Predicting both settlements and excess pore pressures in such cases has become increasingly challenging when time duration is long-term, e.g. several years. In discussing such matter, finding a suitable model to predict the long-term performance is inevitable. Elasto-plastic analysis models such as Cam-Clay cannot predict long-term time-dependent deformational behaviour in soft soils. In this technical note, a Biot type fully-coupled creep-based elastic viscoplastic (EVP) finite element (FE) numerical model has been extended for application in vacuum consolidation. The vacuum consolidation section of the embankment constructed in Ballina, New South Wales, Australia (hereafter referred as Ballina embankment), is analysed using the model through a unit cell analysis and the numerical predictions are compared with field performance monitoring data up to 1200 days (>3 years). The proposed analysis method for PVD combined with vacuum consolidation involving an EVP model is found to be capable of predicting both short-term and long-term deformational behaviours. Predictions are improved when an exponential function is used for the secondary compression index in the EVP model. Comparison has also been carried out at another location in the embankment where the foundation clay thickness was different to check the precision of the methodology and for better understanding of ground settlement behaviour. Details of the analysis methodology and its validation against field performance data are presented in this note.

Total system error analysis of UAV-CRP technology for monitoring transportation infrastructure assets

Infrastructure forms the backbone of a nation's economy, and there is a growing demand for a technology that offers quick, safe, and reliable infrastructure performance monitoring data, which would lead to proactive maintenance or repairs of the infrastructure to ensure its quality performance as per the original design. The increase in the applications of unmanned aerial vehicles (UAVs) or drones for such monitoring purposes have been propelled by advancements in aerial vehicle platforms and image-analyses software. Evaluating UAVs for high-quality infrastructure-distress data monitoring has been a major focus for this research. Photogrammetry is the science of measuring distances from two or multiple images of an object and close range photogrammetry (CRP) can be accomplished by using an optical camera mounted on a UAV platform and taking the images of the infrastructure object. This technology, hereafter termed as UAV-CRP technology, utilizes UAVs coupled with high quality camera lens and a global navigation satellite system (GNSS) device to collect high quality data with fewer ground control points (GCPs). The various pieces of equipment need to have their aerial technology compatibility tested in different environmental conditions and evaluate the imagery that is used for image analyses and interpretations. A comprehensive set of calibration checks were executed using the total system comprising of UAV and accessories to evaluate the imagery quality level of transportation and geotechnical infrastructure data. All the checks performed ensured that the data obtained from the UAV-CRP technology meets the quality requirements for infrastructure data. A case study of pavement infrastructure data was provided to validate the high accuracy UAV-CRP data obtained following the guidelines developed in this paper.

Visualizing value for money in public health interventions.

The Socio-Technical Allocation of Resources (STAR) has been developed for value for money analysis of health services through stakeholder workshops. This article reports on its application for prioritization of interventions within public health programmes. The STAR tool was used by identifying costs and service activity for interventions within commissioned public health programmes, with benefits estimated from the literature on economic evaluations in terms of costs per Quality-Adjusted Life Years (QALYs); consensus on how these QALY values applied to local services was obtained with local commissioners. Local cost-effectiveness estimates could be made for some interventions. Methodological issues arose from gaps in the evidence base for other interventions, inability to closely match some performance monitoring data with interventions, and disparate time horizons of published QALY data. Practical adjustment for these issues included using population prevalences and utility states where intervention specific evidence was lacking, and subdivision of large contracts into specific intervention costs using staffing ratios. The STAR approach proved useful in informing commissioning decisions and understanding the relative value of local public health interventions. Further work is needed to improve robustness of the process and develop a visualization tool for use by public health departments.

Development of deformation criteria for predictive monitoring system for levees

Performance monitoring and real-time data analysis are essential tools to evaluate the integrity of infrastructure. Among other techniques, non-contact deformation measurement techniques such as satellite or drone-based methods are usually low-cost options, not requiring expensive sensors to be installed on target structures. The techniques are particularly economic for large structures such as levees, major slopes or dams. This study introduces lower bound and upper bound deformation concepts for levees. These two bounds are the deformation at which local failure is about to initiate and at which general failure is about to initiate, respectively. This study compared numerically computed lower and upper bounds deformation to field data from a full-scale loading test for a section of the levee at London Canal Avenue, New Orleans, and it showed that the two deformation criteria could be used to monitor and evaluate the integrity of levees.

Telecommunications Performance Monitoring and Unlimited Data

The Australian telecommunications industry has been slow to call for or to adopt new practices and the National Broadband Network has exacerbated the problem of technology adoption lag. There are two key issues facing telecommunication consumers today. The cost of optical networking has significantly reduced over the past five years so there is no justification for the network congestion that occurs on Australian telecommunication networks. To remedy this situation the introduction of performance monitoring is fully supported. It is time for the telecommunications industry to adopt new broadband business models that are based on the provision of unlimited data and a maximum of 90 to 95 per cent utilisation on optical network links.

Telecommunications Performance Monitoring and Unlimited Data

The Australian telecommunications industry has been slow to call for or to adopt new practices and the National Broadband Network has exacerbated the problem of technology adoption lag. There are two key issues facing telecommunication consumers today. The cost of optical networking has significantly reduced over the past five years so there is no justification for the network congestion that occurs on Australian telecommunication networks. To remedy this situation the introduction of performance monitoring is fully supported. It is time for the telecommunications industry to adopt new broadband business models that are based on the provision of unlimited data and a maximum of 90 to 95 per cent utilisation on optical network links.

Substance use outcomes of patients served by a large US implementation of Screening, Brief Intervention and Referral to Treatment (SBIRT).

To estimate changes in the substance use behaviors of patients who received services as part of the US Substance Abuse and Mental Health Services Administration's (SAMHSA) Screening, Brief Intervention and Referral to Treatment (SBIRT) grant program. We use a pre-post design and performance monitoring data collected by SBIRT organizations. For a sample of 17 575 patients, we compare pre-SBIRT substance use with substance use 6months after receipt of SBIRT services. SBIRT's correlation with changes in substance use was estimated using generalized linear mixed models to account for the clustering of patients within health-care facility and US state. From pre- to post-SBIRT we found large and statistically significant decreases for almost every measure of substance use. Model-adjusted means indicate that the prevalence of alcohol use was lower 6 months later by 35.6%, heavy drinking by 43.4% and illicit drug use by 75.8%. Greater intervention intensity was associated with larger decreases in substance use. The study design does not support causal conclusions and estimated decreases in reported substance use are due, at least in part, to a well-known set of confounders and natural substance use patterns that may be unrelated to any particular SBIRT intervention. Compared with previously published findings on the Screening, Brief Intervention and Referral to Treatment grant program, our estimates of substance use reduction were smaller, but still consistently large in absolute magnitude and within ranges of estimates from past trials of Screening, Brief Intervention and Referral to Treatment.

Hyperactive performance monitoring as a transdiagnostic marker: Results from health anxiety in comparison to obsessive–compulsive disorder

Hyperactive error-related brain activity has been found in individuals with obsessive-compulsive disorder (OCD), generalized and social anxiety as well as depression and has been proposed as a transdiagnostic marker. The specific phenotype to which it is related is still debated and anxious apprehension, threat sensitivity and checking have been proposed as promising candidates. To validate the idea that hyperactive performance monitoring is shared by anxiety and anxiety-related disorders and to refine our knowledge about its specificity, data from further anxiety and anxiety-related disorders are required. We examined performance monitoring in 24 participants with health anxiety and 24 healthy participants. Further, we compared results to performance monitoring data from 24 patients with OCD taken from a previously published study (Riesel et al., 2014). The three groups were matched with regard to age, gender and level of education. The error-related and correct-related negativity (ERN, CRN) derived during a flanker task served as neural indicators of performance monitoring. Participants with health anxiety showed enhanced ERN amplitudes compared to healthy controls and did not differ from OCD patients in ERN. The Health anxiety and healthy control group did not differ in CRN amplitudes, while OCD patients showed enhanced CRN amplitudes. The results suggest that health anxiety is characterized by hyperactive error-monitoring that seems to represent a common information processing dysfunction in health anxiety and OCD. This validates the idea that hyperactive error-monitoring cuts across diagnoses and seems to be a transdiagnostic trait shared by individuals that are highly sensitive to the commission of errors.

Experimental and Analytical Modeling of Shield Segment under Cyclic Loading

AbstractBecause of the lack of adequate monitoring performance data for shield tunnel segments from either laboratory testing or in situ measurement, it is difficult to improve the current segment design codes. As a result, overdesign or excessive use of reinforcement in tunnel linings is common. To overcome these segment design inadequacies, this study aimed to investigate the bending behavior of RC members based on measured strain data and the modeling results from a proposed analytical model, which takes into account section nonlinearity resulting from concrete tensile cracking and the actual effective moment of inertia. The cyclic four-point loading test was performed on a RC beam with an embedded optical fiber sensor and on a smart tunnel lining segment, in which two vibrating wire strain gauges with thermistors were welded into a rebar cage on either side of the neutral axis. The measured strain data for the rectangular beam subject to concrete curing and subsequent four-point loading justify the ap...

Characterization of structural conditions for pavement rehabilitations

For concrete pavements it is critical to identify weak spots and locations with very poor load transfer efficiency and then to apply localized repairs before placing a structural overlay. Collecting a continuous deflection profile is the most efficient way to identify those locations. The Texas Department of Transportation’s Total Pavement Acceptance Device (TPAD) has been used successfully on two field projects with main goal of identifying these problematic areas. TPAD is an integrated nondestructive testing tool and it collects continuous deflections at approximately 50mm intervals with three rolling geophone sensors while traveling over the pavement at 2mph. Empirical threshold values have been established to identify and prioritize the problematic areas. Refinements to the threshold values can be made with additional field performance monitoring data. In addition to continuous deflections, the TPAD unit also collects continuous Ground Penetrating Radar (GPR) data. In a project on IH 20, the GPR data presented clear evidence that there are wet areas beneath the existing concrete slabs due to clogged edge drains. Trapped moisture is one of the main causes for the recurring failures observed on IH20.On the jointed concrete pavement on Loop 12 in Dallas, five different short sections with high deflections were successfully identified. The responsible district has implemented recommendation for pre-overlay treatment on those five short sections through full depth repairs. The TPAD data provides quantifiable and defensible data for selecting the required repair areas before the overlay to minimize premature failures. With its continuing utilization and additional field performance monitoring, it is anticipated that the refinements to the threshold values for TPAD analyses can be made.