Complexity Engineering

QCM Applied to Bridge Monitoring and Maintenance

Bridges, regardless of type, must maintain their performance throughout their useful life. Maintaining these features requires continuous monitoring of its state of conservation so that the resources necessary to keep the bridge in service can be optimized, decide the optimal time for its replacement (from an economic perspective) or control the risks of maintaining its use in such a way as to avoid accidents or sudden collapses.

It is widely known that damage to a structure, both from a global perspective, or if the study is reduced to a particular element, does not respond to a linear function in time but rather evolves in an exponential way, mildly at the beginning of its useful life and with deterioration accelerating in more advanced phases. A classic example of this behavior can be observed in the corrosion of reinforcement in concrete, while the concrete around rebars are intact there is a slight deterioration of the steel, but if for some reason concrete protection fails (cracking, modification of the pH, etc.) corrosion of reinforcement can advance quickly and a drastic reducing of the resistant capacities of the reinforcement can occur.

Source: Prof. Javier León González

The methodologies that are usually used in most countries for the management of the structural health of bridges are based on periodic inspections of the entire structure. There are different types of inspections depending on their detail and specificity, from which both qualitative and quantitative information is obtained, the first kind of them being the majority in many cases.

Due the fact that deterioration of bridges is a continuous phenomenon, classic methodologies develop different models of damage evolution both to characterize the behavior of the bridge between inspections as well as to carry out prognostics that allow determining how it will evolve until the moment of carrying out the next inspection, quantifying the resources necessary for its maintenance in service conditions.

Except in a small number of cases, in which mechanistic models can be applied, in which a complete theoretical framework can be developed on the phenomenon under study, it is necessary to develop models starting from generally scarce and incomplete data of physical phenomena on which there is no deep knowledge, not even of the variables or parameters involved. This is why it is not uncommon to make mistakes in estimating the remaining useful life of a structure or as to the evolution of certain damages that are known to be already present (source of possible significant personal and economic damages).

In the last two decades, as complement to classical methodologies, application of various Structural Health Monitoring (SHM) techniques to the world of civil infrastructures, and to bridges in particular, has increased notably. These techniques are based on the systematic, continuous and automated monitoring of structures from the data of certain parameters collected from the structure itself from a network of sensors installed on it. It is a new approach in that it attempts to characterize the complete behavior of the structure based on a continuous knowledge of certain discrete spatial parameters, instead of the classical methodology based on temporally discrete knowledge but continuous in space.

There are numerous techniques used in the SHM, some based on the calibration of numerical models from the data of the structure and others based solely on the interpretation of data without any structural knowledge.

In the case of model-based techniques, prognostic exercises are complex, or perhaps imprecise, given that an acceptable level of precision requires continuous calibration of the models. On the other hand, techniques based solely on data are based on the recognition of patterns or the ability to recognize anomalous or significantly different behaviors between the data of two specific moments in time (normally a large amount of data of the structure is required both in whole situation as damaged, data that are not normally available or must be generated via modeling).

In general, SHM techniques suffer from the need to have a very large quantity of data and the lack of ability to discern whether the changes in the structure that could be detected are due to the appearance of real damage or are changes in the control parameters generated by external agents (the variation of some modal parameters of the structures with climatic variables such as temperature, for example, is known). It is precisely here where the application of the Quantitative Complexity Management (QCM) techniques developed by ONTONIX take on special interest, by basing the analysis of the Bridge+Enviroment system, based solely on raw data collected from the structure, without the need to generate data through modeling or having to resort to pattern recognition techniques or damage evolution models.

Complexity is an intrinsic characterization of a given system and based solely on its elements and the relationships between them. Continuous monitoring of complexity is based on streaming real time and raw data, and its variations objectively determine when a system is undergoing changes which may lead to its not meeting the requirements of service. Monitoring of complexity allows anomaly anticipation, whereby sudden changes in the slope of the complexity curve anticipate moments of crisis.

These techniques have been tested in finite element models of a bridge subjected to ordinary railway loads, adopting as control variables the vertical accelerations recorded at 20 control points located in 4 measurement sections located on the deck – see figure below.

An example of the transient acceleration recorded at a particular sensor position is illustrated below.

In the image below the complexity of the bridge without damage is shown, together with the corresponding Complexity Map. The information is obtained from sensors under the mentioned transient train load.

Subsequently, the Bridge has been subjected to the same railway loads but reducing the mechanical properties of some of its elements to simulate some damage to the structure (corrosion in a longitudinal element or corrosion or partial breaks in the joints of certain transverse elements). The locations of the defects are illustrated in the image below.

The resulting complexity value is substantially higher and the Complexity Profile, which ranks the various sensors, indicates the sensors closest to the damaged elements.

Analyzing models with damages, which can be characterized qualitatively between moderate and severe, it is appreciated that both the complexity and the entropy of the system increase. This increase is logical if one takes into account that entropy is related to the uncertainty in the interactions between elements. Uncertainty that increases as the system approaches a situation close to loss of functionality.

The conclusion of this study is that the application of QCM techniques to the monitoring of bridges allows to establish an objective index of structural health without the need to resort to damage evolution modeling or numerical modeling of structural behavior, modeling that inevitably generates certain loss of information that may be relevant in advanced states of damage.

The above article is an extract from an MS dissertation thesis, “Quantitative Complexity Management, Aplicaciòn a la Monitorizaciòn de Puentes” by Miguel Rupérez Astarloa, at UNED University, Madrid 26-th June, 2021. Blog written by Miguel Rupérez Astarloa.

Established originally in 2005 in the USA, Ontonix is a technology company headquartered in Como, Italy. The unusual technology and solutions developed by Ontonix focus on countering what most threatens safety, advanced products, critical infrastructures, or IT network security - the rapid growth of complexity. In 2007 the company received recognition by being selected as Gartner's Cool Vendor. What makes Ontonix different from all those companies and research centers who claim to manage complexity is that we have a complexity metric. This means that we MEASURE complexity. We detect anomalies in complex defense systems without using Machine Learning for one very good reason: our clients don’t have the luxury of multiple examples of failures necessary to teach software to recognize them. We identify anomalies without having seen them before. Sometimes, you must get it right the first and only time!

1 comment on “QCM Applied to Bridge Monitoring and Maintenance

  1. Mike Sheh

    Great stuff! Timely too!


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