Road maintenance planning, an essential component of road asset management, preserves the integrity of road networks. Current state of the art pavement management systems exercise optimisation tools, pavement deterioration models, and intervention criteria to forecast the future maintenance requirements of a road network. These tools have been utilised to forecast future maintenance requirements of road networks; however, with this current approach to pavement management, uncertainties associated with the failure of individual sections of road may not always be accounted for explicitly, and therefore the susceptibility of a road network to failure is unknown. Predicting the probability of the end of life of a road pavement involves wholly understanding possible modes of failure and utilising suitable computational techniques, so that engineering knowledge can be well represented in data driven models. To this end, this thesis describes the development of a diagnostic approach that infers engineering knowledge into computational models, to quantify the probability of failure of road pavements and identify the most likely causes of failure. To do so, this research developed a number of failure charts that capture engineering knowledge, such as citing influential failure factors of road pavements including the influence from external environments and internal pavement attributes. Engineering knowledge on road pavement failure was obtained from three sources: literature describing the fundamentals of pavement design and common causes of road failure, expert knowledge from the industry identifying relationships between failure mechanisms and causes, and a data analysis to obtain site-specific causes such as road environments and material properties. Each chart presents a possible failure path, detailing a set of factors contributing to failure. A comparative study evaluated the performance of five classification modelling approaches in order to determine the most suitable technique for this research. Based on performance and user interpretability criteria, the study identified one based on support vector machines as the most suitable. The developed prototype system, consisting of a failure system for rutting, fatigue cracking, and shear, performed well in both the development phase and network testing of the system utilising data from the New Zealand Long-term Pavement Performance Programme. A case study focussing on rural New Zealand roads was carried out, which demonstrated the use of this tool in network and project level applications.

This book gathers the proceedings of an international conference held at Empa (Swiss Federal Laboratories for materials Science and Technology) in Dübendorf, Switzerland, in July 2020. The conference series was established by the International Society of Maintenance and Rehabilitation of Transport Infrastructure (iSMARTi) for promoting and discussing state-of-the-art design, maintenance, rehabilitation and management of pavements. The inaugural conference was held at Mackenzie Presbyterian University in Sao Paulo, Brazil, in 2000. The series has steadily grown over the past 20 years, with installments hosted in various countries all over the world. The respective contributions share the latest insights from research and practice in the maintenance and rehabilitation of pavements, and discuss advanced materials, technologies and solutions for achieving an even more sustainable and environmentally friendly infrastructure.

Developing countries in the tropics have different natural conditions and different institutional and financial situations to industrialized countries. However, most textbooks on highway engineering are based on experience from industrialized countries with temperate climates, and deal only with specific problems. Road Engineering for Development (published as Highway and Traffic Engineering in Developing Countries in its first edition) provides a comprehensive description of the planning, design, construction and maintenance of roads in developing countries. It covers a wide range of technical and non-technical problems that may confront road engineers working in this area. The technical content of the book has been fully updated and current development issues are focused on. Designed as a fundamental text for civil engineering students this book also offers a broad, practical view of the subject for practising engineers. It has been written with the assistance of a number of world-renowned specialist professional engineers with many years experience in Africa, the Middle East, Asia and Central America.

This second edition of An Introduction to Predictive Maintenance helps plant, process, maintenance and reliability managers and engineers to develop and implement a comprehensive maintenance management program, providing proven strategies for regularly monitoring critical process equipment and systems, predicting machine failures, and scheduling maintenance accordingly. Since the publication of the first edition in 1990, there have been many changes in both technology and methodology, including financial implications, the role of a maintenance organization, predictive maintenance techniques, various analyses, and maintenance of the program itself. This revision includes a complete update of the applicable chapters from the first edition as well as six additional chapters outlining the most recent information available. Having already been implemented and maintained successfully in hundreds of manufacturing and process plants worldwide, the practices detailed in this second edition of An Introduction to Predictive Maintenance will save plants and corporations, as well as U.S. industry as a whole, billions of dollars by minimizing unexpected equipment failures and its resultant high maintenance cost while increasing productivity. - A comprehensive introduction to a system of monitoring critical industrial equipment - Optimize the availability of process machinery and greatly reduce the cost of maintenance - Provides the means to improve product quality, productivity and profitability of manufacturing and production plants