Current Bridge Management System (BMS) Research

Long span suspension bridges are normally the vital links in the nation-wide transport system. These structures are very flexible and vulnerable to various types of loads, especially dynamic loads such as earthquakes and wind. Continuous monitoring of the structure is indispensable to ensuring traffic safety and structural soundness. Monitoring can also help reveal the real performance under severe loading, which can provide extremely useful information for understanding structure performance (Fujino et al. 2000). Data collected from monitoring procedures will form the most important part in a BMS. In a BMS, a BDMS acts as a sub-system for supplying timely bridge performance to a BMS.

Faced by factors such as increasing concerns about transport safety due to a series of bridge collapses and failures in recent years, and the fact that there are more than 10,000 new bridge defects reported each year, FHWA of the USA issued the Intermodal Surface Transportation Efficiency Act (ISTEA) in 1991. This aimed to improve land transportation safety and efficiency, and mandated the implementation of a quantitative computerised BMS by 1996 (http://www.tfhrc.gov/, http://www.bts.gov/).

A number of BMSs are now being developed and applied in various countries. The most advanced of these is the PONTIS system developed in the USA in 1991 (Thompson 1993). PONTIS is a network-level BMS, compris ing three main modules: recommendations; optimisation and improvement to optimise budgets; and programmes for maintenance and improvement of a state’s full inventory of up to 42,000 structures. This research was motivated by ISTEA, a series of highly publicised bridge failures, and emerging professional consensus within the US A on what essential ingredients a BMS should contain. PONTIS is in fact a planning tool; a decision support system which helps bridge managers to make use of the vast database of bridge inspections and the information provided by other agencies to make more informed policy and programming decisions.

In 1988, the UK Department of Transport (DOT) started a major programme of bridge inspection on the trunk road network, in accordance with the Department’s Bridge

Chapter 2 GPS-Based Bridge Deformation Monitoring

Assessment Code (Das 1997). The DO T first published an assessment code for older bridges in 1967. This formed the basis of an assessment called Operation Bridge

Guard which considered highway bridges with a capability less than that required by

the then national bridge design standard. When carrying out an assessment programme involving a large number of structures, it is necessary to provide assessing engineers with reliable tools and information on both historical and current situations to enable consistent engineering assessment. The latest technical information, which will give a more realistic picture of actual structural strength, should be made widely available. This emphasises the importance of adopting standard quantitative and accurate bridge inspection methods to feed a BMS database with timely, accurate, and reliable bridge condition information which is then given to the field engineers (Harding et al. 1996). It is well known that the analysis results that the system could provide are only as good as the original information. No matter how sophisticated and elaborate the analysis is and no matter how elegant the algorithms employed are, in the final analysis, the recommended decisions cannot be any better than the data upon which they are based.

In the past, most BMS researches have focused on the implementation of the system as well as the improvement of the mathematical models. Very little work has been published on improving the quality of the information that is required for these models or how to narrow the gap between the data required for the models and current inspection processes. In the USA in the 1990s, improvements in data acquisition technology for BMS became the research focus in the National Corporative Highway Research Program (NCHRP 1990).

Today these data are based almost entirely on the visual inspection of a bridge’s condition. A bridge is given a rating according to indications of deterioration and distress. Whilst the rating of bridge element is defined in quantitative and engineering terms, this inspection system can only record condition on a overall rating, and does not provide detailed description of the condition that gave rise to the rating. There is no mechanism currently in place to record any other inspection information other than the condition rating in a current BMS. For example, the deterioration that does not manifest some visible symptom is not detected or quantified. This is especially true for certain types of hidden deterioration, such as corrosion of reinforcement in

concrete or cumulative fatigue loading in steel bridges. If the information about a bridge is inaccurate, the resulting analyses will not be optimal and totally wrong conclusions could be even reached from such an analysis.

Nondestructive Evaluation (NDE) testing including GPS technology, is the branch of engineering which is concerned the detection and evaluation of flaws in structural materials. Since bridge flaws can affect the serviceability of the materials or structures, NDE is an important approach in guaranteeing safe operation of structures as well as realising structure quality control and assessing bridge life span. The flaws may be cracks or corrosions in welds and castings, or variations in structural properties that can lead to loss of strength or failure in- service which cannot be detected by visual inspection.

For a BMS, NDE testing is used for in-service inspection and structural condition monitoring. It is also used for the measurement of components and spacing and for the measurement of physical properties such as hardness and internal stress. The essential feature of NDE is that the test process itself produces no deleterious effects on the material or structure under test (http://www.bindt.org/NDT.html).

By using NDE technique and also visual inspection, data about bridge performance are collected from a number of sources/nodes and transferred to a BMS database. A sophisticated analysis of the data is then performed. This generates a prioritised programme for bridge replacement and maintenance strategies for various funding and resource scenarios. It can predict the bridge deterioration over time and provide powerful decision-support tools to help formulate the best program for bridge management. The implementation of such a system will have tremendous benefit for bridge authorities. These procedures form the conceptual basis of a ‘Smart Bridge’ (Oshima et al. 2000). It is worth pointing out that as on e of NDE techniques GPS can be employed to detect geometric deformations of the monitored bridge. However, GPS cannot be used to detect the potential material corrosions and fatigues directly. The viability of GPS applications in these areas is the current research of a joint project between Cranfield University and the IESSG (Meo et al. 2002).

Chapter 2 GPS-Based Bridge Deformation Monitoring