The growing interest in pavement management systems (PMSs), both in South Mrica and internationally, has been in response to a shift in importance from the construction of new roads to the maintenance of the existing paved network coupled with increasingly restrictive road funding. In order to develop a balanced expenditure programme for the national roads of South Africa there is a need to predict the rate of deterioration of a pavement and the nature of the changes in its condition so that the timing, type and cost of maintenance needs could be estimated. Internationally these expected changes in pavement condition are predicted by pavement deterioration models, which normally are algorithms developed mathematically or from a study of pavement deterioration. Since no usable pavement deterioration models existed locally, it was necessary to evaluate overseas literature on pavement deterioration prediction models with the aim of identifying models possibly applicable to the national roads of South Africa. Only deterioration models developed from the deterioration results of inservice pavements under a normal traffic spectrum were evaluated. Models developed from accelerated testing were avoided since these models virtually eliminated long·term effects (these are primarily environmental but also include effects of the rest periods between loads), and that the unrepresentative traffic loading regimes can distort the behaviour of the pavement materials, which is often stress dependent. Models developed from the following studies were evaluated: • AASHO Road Test • The Kenya study • Brazil-UNDP study (HDM-ill models) • Texas study Of all the above models studied that were developed from major studies it was concluded that the incremental models developed during the Brazil study, were the most appropriate for further evaluation under South African conditions. A sensitivity analysis was conducted on the HDM-III models to evaluate their sensitivity to changes in the different parameters comprising each model. The results obtained from the sensitivity analysis indicate that the incremental roughness prediction model incorporated into the HDM-III model tends to be insensitive to changes in most parameters. Accuracy ranges for input data were, however, also identified for parameters which indicated an increase in sensitivity in certain ranges. The local applicability of the HDM-III deterioration models were finally evaluated by comparing HDM-III model predictions with the actually observed deterioration values of a selected number of national road pavement sections. To enable the above comparison, a validation procedure had to be developed according to which the format of existing data could be transformed to that required by the HDM-ill model, as well as additional information be calculated. From the comparison it was concluded that the HDM-III models are capable of accurately predicting the observed deterioration on South African national roads, but that for most models calibration is needed for local conditions. Guidelines regarding recommended calibration factor ranges for the different HDM-ill models are given. Finally it is recommended that the HDM-III models should be considered for incorporation into a balanced expenditure programme for the national roads of South Africa. / Dissertation (MEng)--University of Pretoria, 1993. / gm2014 / Civil Engineering / MEng / Unrestricted

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Planning road maintenance and development activities, prioritizing road construction, reconstruction and rehabilitation works, performing project economic evaluations, forecasting road operation expenditures and road user effect always requires prediction of pavement behaviour. For this purpose models of pavement deterioration are used. Commonly they are integrated in more sophisticated computational systems, known as Pavement Management systems (PMS) and Highway Development and Management systems (HDM). The implementation of these systems started in Lithuania shortly after the Re-establishment of the Independence. This was triggered by the violent drop in financing of the road sector, forcing to change obsolete road management strategy and planning approach. The need for project economic evaluations, transport modelling and long term pavement performance forecasts grew up after Lithuania joined the European Union and this brought the opportunity for financing road infrastructure projects from the EU funds. At present only on the Lithuanian state road network the total value of implemented projects, justified by evaluations and modelling with PMS, amounts to about 500 million Litas annually. Any Pavement Management System is just as good as the models within it, used for pavement deterioration prediction. The performed test calculations show that prediction models, currently used for this purpose in PMS we have in Lithuania, are of doubtful accuracy and have to be improved... [to full text]

The dynamics around managing urban infrastructure are changing dramatically. Today???s infrastructure management challenges ??? in the wake of shrinking coffers and stricter stakeholders??? requirements ??? include finding better condition assessment tools and prediction models, and effective and intelligent use of hard-earn data to ensure the sustainability of urban infrastructure systems. Wastewater collection networks ??? an important and critical component of urban infrastructure ??? have been neglected, and as a result, municipalities in North America and other parts of the world have accrued significant liabilities and infrastructure deficits. To reduce cost of ownership, to cope with heighten accountability, and to provide reliable and sustainable service, these systems need to be managed in an effective and intelligent manner. The overall objective of this research is to present a new strategic management framework and related tools to support multi-perspective maintenance, rehabilitation and replacement (M, R&R) planning for wastewater collection networks. The principal objectives of this research include: (1) Developing a comprehensive wastewater collection network asset database consisting of high quality condition assessment data to support the work presented in this thesis, as well as, the future research in this area. (2) Proposing a framework and related system to aggregate heterogeneous data from municipal wastewater collection networks to develop better understanding of their historical and future performance. (3) Developing statistical models to understand the deterioration of wastewater pipelines. (4) To investigate how strategic management principles and theories can be applied to effectively manage wastewater collection networks, and propose a new management framework and related system. (5) Demonstrating the application of strategic management framework and economic principles along with the proposed deterioration model to develop long-term financial sustainability plans for wastewater collection networks. A relational database application, WatBAMS (Waterloo Buried Asset Management System), consisting of high quality data from the City of Niagara Falls wastewater collection system is developed. The wastewater pipelines??? inspections were completed using a relatively new Side Scanner and Evaluation Technology camera that has advantages over the traditional Closed Circuit Television cameras. Appropriate quality assurance and quality control procedures were developed and adopted to capture, store and analyze the condition assessment data. To aggregate heterogeneous data from municipal wastewater collection systems, a data integration framework based on data warehousing approach is proposed. A prototype application, BAMS (Buried Asset Management System), based on XML technologies and specifications shows implementation of the proposed framework. Using wastewater pipelines condition assessment data from the City of Niagara Falls wastewater collection network, the limitations of ordinary and binary logistic regression methodologies for deterioration modeling of wastewater pipelines are demonstrated. Two new empirical models based on ordinal regression modeling technique are proposed. A new multi-perspective ??? that is, operational/technical, social/political, regulatory, and finance ??? strategic management framework based on modified balanced-scorecard model is developed. The proposed framework is based on the findings of the first Canadian National Asset Management workshop held in Hamilton, Ontario in 2007. The application of balanced-scorecard model along with additional management tools, such as strategy maps, dashboard reports and business intelligence applications, is presented using data from the City of Niagara Falls. Using economic principles and example management scenarios, application of Monte Carlo simulation technique along with the proposed deterioration model is presented to forecast financial requirements for long-term M, R&R plans for wastewater collection networks. A myriad of asset management systems and frameworks were found for transportation infrastructure. However, to date few efforts have been concentrated on understanding the performance behaviour of wastewater collection systems, and developing effective and intelligent M, R&R strategies. Incomplete inventories, and scarcity and poor quality of existing datasets on wastewater collection systems were found to be critical and limiting issues in conducting research in this field. It was found that the existing deterioration models either violated model assumptions or assumptions could not be verified due to limited and questionable quality data. The degradation of Reinforced Concrete pipes was found to be affected by age, whereas, for Vitrified Clay pipes, the degradation was not age dependent. The results of financial simulation model show that the City of Niagara Falls can save millions of dollars, in the long-term, by following a pro-active M, R&R strategy. The work presented in this thesis provides an insight into how an effective and intelligent management system can be developed for wastewater collection networks. The proposed framework and related system will lead to the sustainability of wastewater collection networks and assist municipal public works departments to proactively manage their wastewater collection networks.

The dynamics around managing urban infrastructure are changing dramatically. Today’s infrastructure management challenges – in the wake of shrinking coffers and stricter stakeholders’ requirements – include finding better condition assessment tools and prediction models, and effective and intelligent use of hard-earn data to ensure the sustainability of urban infrastructure systems. Wastewater collection networks – an important and critical component of urban infrastructure – have been neglected, and as a result, municipalities in North America and other parts of the world have accrued significant liabilities and infrastructure deficits. To reduce cost of ownership, to cope with heighten accountability, and to provide reliable and sustainable service, these systems need to be managed in an effective and intelligent manner. The overall objective of this research is to present a new strategic management framework and related tools to support multi-perspective maintenance, rehabilitation and replacement (M, R&R) planning for wastewater collection networks. The principal objectives of this research include: (1) Developing a comprehensive wastewater collection network asset database consisting of high quality condition assessment data to support the work presented in this thesis, as well as, the future research in this area. (2) Proposing a framework and related system to aggregate heterogeneous data from municipal wastewater collection networks to develop better understanding of their historical and future performance. (3) Developing statistical models to understand the deterioration of wastewater pipelines. (4) To investigate how strategic management principles and theories can be applied to effectively manage wastewater collection networks, and propose a new management framework and related system. (5) Demonstrating the application of strategic management framework and economic principles along with the proposed deterioration model to develop long-term financial sustainability plans for wastewater collection networks. A relational database application, WatBAMS (Waterloo Buried Asset Management System), consisting of high quality data from the City of Niagara Falls wastewater collection system is developed. The wastewater pipelines’ inspections were completed using a relatively new Side Scanner and Evaluation Technology camera that has advantages over the traditional Closed Circuit Television cameras. Appropriate quality assurance and quality control procedures were developed and adopted to capture, store and analyze the condition assessment data. To aggregate heterogeneous data from municipal wastewater collection systems, a data integration framework based on data warehousing approach is proposed. A prototype application, BAMS (Buried Asset Management System), based on XML technologies and specifications shows implementation of the proposed framework. Using wastewater pipelines condition assessment data from the City of Niagara Falls wastewater collection network, the limitations of ordinary and binary logistic regression methodologies for deterioration modeling of wastewater pipelines are demonstrated. Two new empirical models based on ordinal regression modeling technique are proposed. A new multi-perspective – that is, operational/technical, social/political, regulatory, and finance – strategic management framework based on modified balanced-scorecard model is developed. The proposed framework is based on the findings of the first Canadian National Asset Management workshop held in Hamilton, Ontario in 2007. The application of balanced-scorecard model along with additional management tools, such as strategy maps, dashboard reports and business intelligence applications, is presented using data from the City of Niagara Falls. Using economic principles and example management scenarios, application of Monte Carlo simulation technique along with the proposed deterioration model is presented to forecast financial requirements for long-term M, R&R plans for wastewater collection networks. A myriad of asset management systems and frameworks were found for transportation infrastructure. However, to date few efforts have been concentrated on understanding the performance behaviour of wastewater collection systems, and developing effective and intelligent M, R&R strategies. Incomplete inventories, and scarcity and poor quality of existing datasets on wastewater collection systems were found to be critical and limiting issues in conducting research in this field. It was found that the existing deterioration models either violated model assumptions or assumptions could not be verified due to limited and questionable quality data. The degradation of Reinforced Concrete pipes was found to be affected by age, whereas, for Vitrified Clay pipes, the degradation was not age dependent. The results of financial simulation model show that the City of Niagara Falls can save millions of dollars, in the long-term, by following a pro-active M, R&R strategy. The work presented in this thesis provides an insight into how an effective and intelligent management system can be developed for wastewater collection networks. The proposed framework and related system will lead to the sustainability of wastewater collection networks and assist municipal public works departments to proactively manage their wastewater collection networks.

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