Chapter 5 Research Paper Conclusion And Recommendation Example

Computer-Based Guidelines for Concrete Pavements Volume I-Project Summary

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CHAPTER 5. SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS

Significant findings and recommendations for enhancing the guidelines in the future are outlined in this chapter.

5.1 Summary

This report documents enhancements incorporated in the HIPERPAV II system. These enhancements include the addition of two major modules: a module to predict the performance of JPCP as affected by early-age factors, and a module to predict the early-age behavior (first 72 hours) and early life (up to 1 year) of CRCP. Two additional FHWA studies were also incorporated: one that predicts dowel bearing stresses as a function of environmental loading during the early age, and a module for optimization of concrete paving mixes as a function of 3-day strength, 28-day strength, and cost.

Additional software functionality was incorporated by reviewing and prioritizing the feedback provided by HIPERPAV I system users. The following is a short list of the many features incorporated:

  • A new graphical user's interface accommodating the different analysis types and options while keeping the simplicity and user friendliness of the previous version.
  • A geographical weather database system that contains historical averages of weather data from weather stations located throughout the United States. Climatic information includes air temperatures, windspeed, relative humidity, cloudiness, and annual rainfall conditions.
  • Analysis of multiple strategies: The new HIPERPAV II system is capable of analyzing multiple strategies for one specific project. This allows for evaluating "what if" scenarios within the same project file and facilitates comparison between strategies.
  • A routine to perform consistency of inputs (input range validation).
  • A reference database that includes the primary references used during the HIPERPAV II development.
  • Improved cement and admixtures characterization with recently developed models.
  • A strength conversion tool with default and user-defined conversion factors.
  • An option for user-defined equivalent age maturity in addition to the Nurse-Saul maturity option previously incorporated.
  • Inputs for user-defined nonlinear slab support characterization.
  • Optimum sawcutting, skip sawcutting, and no sawcutting options.
  • Concrete CTE and ultimate shrinkage inputs.
  • Enhanced input capability with tabular and graphic options.

To ensure a successful implementation of the HIPERPAV II system, a TEP was formed, which consisted of stakeholders in the paving industry. Throughout the project's development, the project team followed recommendations from the TEP, and numerous feedback items were incorporated to facilitate software implementation.

To incorporate the new modules, an exhaustive literature search was performed, and the pertinent models were identified and selected after evaluating the advantages and disadvantages of each of them. Special emphasis was placed in selecting models developed with a mechanistic or mechanistic-empirical approach that took early-age factors into account. Model selection was followed by a plan for model integration. This integration was achieved by following a systems approach methodology that built on the concrete temperature and early-age behavior prediction core modules within HIPERPAV I. Model integration included developing a new graphical user interface and extensive model coding. This phase was followed by extensive software debugging and testing.

Although the CRCP behavior models and long-term JPCP models selected for use in this project had already undergone extensive calibration and validation efforts, further modification to some of the models to reflect specific early-age conditions and to integrate well with the overall system warranted further validation during this effort. Validation focused on determining the reliability of model prediction. Two levels of validation were undertaken. The first level of validation was performed with databases. The SMP pavement database maintained by LTPP and the Texas rigid pavement database were used to validate the JPCP LTE response and early-age behavior prediction, respectively. The second level of validation consisted of evaluating the accuracy of prediction with information collected from a field investigation performed on select pavement sites. Two JPCP sections were investigated to evaluate distress prediction, and two CRCP sections were investigated to evaluate early-age CRCP behavior. Additional validation also was performed for general early-age behavior enhanced models, such as a FDM temperature prediction model and an improved drying shrinkage model.

5.2 Conclusions

The objectives for this study were accomplished successfully. The module for JPCP long-term performance prediction as a function of early-age factors and the module for prediction of CRCP early-age behavior were successfully incorporated by employing available models in the literature from recognized sources. Because developing new models was outside the scope of this project, available models were adapted for integration into the HIPERPAV II system.

Overall, the results from the validation efforts for both long-term performance of JPCP and early-age CRCP behavior models were positive. A summary of the findings obtained during the validation phase of this project is summarized below:

  • It was found during the verification process that although the JPCP LTE model predictions follow the general trends of LTE as computed from FWD tests, further investigation of a number of factors are necessary to predict LTE with improved accuracy.
  • Reasonable predictions were obtained in terms of long-term performance for the JPCP field sites evaluated; these follow logical trends. Although limited, this validation was done with quality data on pavement design, materials, climatic, and construction inputs.
  • For the validation of the CRCP models, variable results were obtained for bond development length and steel stress prediction. This difference in prediction was attributed to the limitations in the bond-slip relationships assumed in the CRCP-8 model.
  • A large overprediction of CRCP crack widths was also observed with both pavement databases and field sites investigated. The overprediction was attributed to the fact that the CRCP-8 model does not take into account the time when the crack forms, but rather is dependent on the predicted crack spacing, PCC thermal properties, and total shrinkage. It is believed that the residual drying shrinkage after the crack forms has a large effect on crack width. It is also believed that the limitations in bond-slip characterization in the CRCP-8 model contributed to the overprediction in crack width.
  • Despite the expected overpredictions in crack width, a reasonably good prediction of CRCP average crack spacing was observed with both the pavement databases and the field sites investigated.

The long-term JPCP module of the HIPERPAV II system was developed to optimize early-age strategies based on how they perform in the long term. With this objective in mind, two early-age strategies can be analyzed in the long term under the same long-term environmental and traffic conditions. Accurate predictions of long-term performance require accurate and detailed information on pavement structural factors, materials characterization, environmental conditions, and traffic data. Because the long-term module in HIPERPAV II is intended to help the user optimize early-age strategies rather than serve as a tool for pavement design, a number of considerations were made to simplify the data entry and improve user-friendliness. Long-term models assumptions and limitations are described in volume III, appendix B of this report series. Despite the model limitations, significant efforts were made to include mechanistic or mechanistic-empirical models. The advantage of taking a more mechanistic approach is that new developments and model improvements can be incorporated gradually in the future.

Regarding the CRCP models, it is believed that despite the observed overprediction in bond development length, steel stress, and crack width, the CRCP model provides a good foundation for comparing alternatives. With relatively moderate effort, the crack width model could be improved to account for drying shrinkage effects and time of crack formation. Furthermore, the CRCP-8 model could be replaced with relative ease with the newer CRCP-9/10 model which validation is currently in progress.(67) The CRCP-9/10 model may provide improved predictions.

Regarding the additional FHWA studies evaluated, although all FHWA studies reviewed potentially could have been implemented successfully in HIPERPAV II, only two of these studies had to be selected. Several factors were considered for study selection, including the status of completion, level of difficulty required for incorporation, easiness of implementation, and usability by the pavement community. Based on the advantages and disadvantages identified on each study, the dowel bar study and the mix optimization study were incorporated in HIPERPAV II.

5.3 Recommendations

5.3.1 Model Improvements

Based on the findings from the model validation, a number of key recommendations are provided below; these would greatly enhance the prediction capabilities of the HIPERPAV II system.

  • To improve the prediction of LTE, further investigation of the slab support conditions, aggregate interlock, dowel looseness, and aggregate wearout, among other factors, is recommended, both in the early age and throughout the pavement's long-term performance.
  • A limited validation with good quality early-age information available for two field sites was performed; however, the long-term module requires further validation with numerous other sites. Database validation efforts within this project were limited due to the lack of extensive early-age information on current databases required for validation. Required information includes mix design information, climatic data, construction times and dates, early-age material characterization, initial construction smoothness, history of structural pavement response, and distress information.
  • Because of the model limitations and assumptions made, predictions will not be comparable with the NCHRP 1-37A product result. Furthermore, HIPERPAV II must never be used for pavement structural design, since it was not validated for this purpose. Instead, the results of long-term performance comparisons should be used for further optimization of early-age strategies examining the effect of early-age environment, materials, and construction factors. The inputs for pavement structural design should already have been performed using a design procedure such as the American Association of State Highway and Transportation Officials (AASHTO) method.(69)
  • Continued validation of the long-term JPCP models is recommended as more sites become available with enough information on materials characterization and construction information.

The PRS module based on the FHWA PaveSpec study was not incorporated in HIPERPAV. However, three unique ways of integrating HIPERPAV with standard specifications were identified:

  1. Merging standard specifications into HIPERPAV: This method consists of incorporating the text of standard specifications into HIPERPAV through a knowledge base. It would provide recommendations and warnings during the HIPERPAV runs that relate to items considered in the standard specifications, tying them to the inputs in the software.
  2. Merging HIPERPAV into standard specifications: In this method, standard specifications could be written to require the use of temperature management software such as HIPERPAV, further assuring that uncontrolled cracking is avoided.
  3. Integrating HIPERPAV and PaveSpec: This method involves combining HIPERPAV and PaveSpec together as described in the above paragraphs, and would be ideal for highway agencies currently considering PRS.

The best option from the above three would depend on the current specifications being used by any individual SHA. It is believed that the first method, merging standard specifications into HIPERPAV, would be the most readily implementable, since it involves less risk to the highway agency in terms of liability. On the other hand, following the current trend of highway agencies shifting to PRS, an integration of HIPERPAV and PaveSpec would provide an ideal tool for implementing such a specification.

5.3.2 The Future of HIPERPAV

When HIPERPAV was first developed in 1996, a new approach was born: a total systems approach to concrete paving. In this simple to use yet technically complex piece of software, the power to simulate problems before they happen is now a reality. Since its development, the HIPERPAV concept has expanded into a usable and reliable tool for concrete pavement design and construction. Demand for HIPERPAV has spread throughout the industry. Contractors, suppliers, agencies, and academics all realize the power in this approach. In the future, it is only logical to further advance the total systems approach concepts inherent in HIPERPAV by incorporating additional modules. The following sections briefly identify some of the possible future trends that have been recognized by the users of the HIPERPAV system.

5.3.2.1 Bridge Deck Application

Users have asked at nearly every HIPERPAV presentation: "Can I use this software for my concrete bridge decks?" The answer at this time is: "Not without proper modification of the models for this application." However, there is high demand for this application. A bridge deck (or bridge deck overlay) application of HIPERPAV would allow a user to predict the potential for uncontrolled cracking just as it does currently for pavements. In truth, because the majority of the models inherent in the HIPERPAV system are based on structural engineering models for concrete, industry acceptability of this could be achieved with minimal validation.

5.3.2.2 Real-Time HIPERPAV Application

Another concept that is often discussed involves the development of a real-time version of HIPERPAV. As with the current version of HIPERPAV, the real-time version would provide a means to predict the behavior of a concrete pavement during the first few critical hours after construction. The difference would be in the methods in which the inputs to the program are determined. In the current version, the user enters the various inputs and a number of assumptions are made as a result. A real-time version would use a weather station and pavement instrumentation to, in essence, calibrate the models in the HIPERPAV system in real time. As a result, the reliability of the HIPERPAV solution is increased substantially, and more informed decisions could be made.

5.3.2.3 Fiber-Reinforced Concrete Application

Fibers, both synthetic and steel, are being used more often in today's concrete pavements. Although common in other concrete flatwork construction, such as industrial floors, the use of fibers in concrete paving has been slow to evolve. However, in many instances, fibers can contribute to the durability and overall performance of concrete pavements. HIPERPAV currently does not use models that would predict the difference in concrete behavior as a result of fiber use. However, using more sophisticated materials characterization models, such as fracture mechanics, would allow for the HIPERPAV system to objectively assess the impact of fibers in the mix.

5.3.2.4 Internet (Web)-Based Application

Although slow to respond at first, the paving industry is now realizing the potential of the Internet in improving efficiency in day-to-day operations. One possible future direction for HIPERPAV would be to deploy the software in an Internet-based mode. By developing a Web-based HIPERPAV application, the customer base of the HIPERPAV systems would expand. In addition, the resulting client server-based system would allow HIPERPAV system use to be evaluated. Trends could be tracked, and modifications to the system made more effective.

5.3.2.5 Concrete Durability Predictive Application

One final application that could be developed, based on the current HIPERPAV system, is an application to better predict the potential durability of concrete used in paving operations. There are a number of ongoing research efforts that aim to better predict the durability of paving concrete as a function of the mix and the surrounding conditions. A future version of HIPERPAV could be developed that can use these models to predict the potential for durability-related issues in a practical manner. With the current trend toward longer life pavements, the durability of the materials used in concrete is becoming more prevalent. In the future, HIPERPAV can be used to make more informed decisions objectively and practically with respect to this important criterion.

 

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1. Bias. Business establishments, agencies, or organizations usually present or manipulate figures to their favor. For instance, an advertisement may quote statistics to show that a given product is superior to any other leading brand. We should be wary of the use of statistics in this case because of the obvious

profit motive behind. An individual may also do the same. A respondent to a questionnaire or in an interview may commit the same bias o protect his own interests. Like the case of the science teachers in the high schools of Province A, they may respond that the science facilities in their respective schools are adequate although they are not just to protect the good names of their own

schools. A respondent, if asked how many science books he has read, may say that he has read many although he has read only a few to protect his name. Hence, if there is a way of checking the veracity of presented data by investigation, observation, or otherwise, this should be done to insure the accuracy of the conclusion based upon the data under consideration.

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