Structural Monitoring using Agent-based Simulation K. Smarsly1 and D. Hartmann2 1
Institute for Computational Engineering (ICE), Ruhr-University at Bochum, Universitätsstr. 150, D-44780 Bochum, Germany; PH +49-234-32-26174; FAX +49-234-3206174; email:
[email protected] 2 Professor, Institute for Computational Engineering (ICE), Ruhr-University at Bochum, Universitätsstr. 150, D-44780 Bochum, Germany; PH +49-234-32-23047; FAX +49-234-32-14292; email:
[email protected] Abstract Structural monitoring is a distributed engineering task, frequently using visual methods or investigating local properties of a structure. However, with respect to tighter economic limits, it is becoming evident that in the next few years a paradigm shift is required from manually based to actively computer-controlled monitoring; appropriate automated Structural Health Monitoring Systems allow highly accurate identification of existing structural behaviors including possible uncertainties with regard to materials, loads as well as aging. Furthermore, the structural evolution with respect to deteriorations and damages can be precisely observed and the emergence of new degradations can be detected expeditiously. By installing a number of sensors at specified locations to monitor relevant parameters continually, it is possible to obtain an insight into the structure's state and the structural evolution, in real-time. This paper presents the development of an agent-based monitoring system for safety-relevant engineering structures that is currently being developed at the Institute for Computational Engineering (ICE): An artificial organization of several, cooperating software agents, which solve the distributed monitoring problem by using specific knowledge and by interacting both with other agents and with human experts involved in monitoring. Key Words Agent technology, databases, data mining, Gaia methodology, Petri nets, statistical data analysis, structural health monitoring. Introduction Maintenance of existing structures is becoming one of the most important industrial sectors within the building industry in the developed countries, particularly in Germany. Aging, altered utilizations and unanticipated service loads as well as increasing environmental impacts are reasons for degradations of structures and, coherently, rapidly increasing costs of maintenance and reconstruction. This is in contrast to reduced investments in new construction of structures. Reliable, holistic Structural Health Monitoring Systems (SHMS) may help to lower the costs of maintenance of structures, in particular engineering structures, since revitalization costs can be calcu-
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lated economically and efficiently applied: Structural anomalies as well as their progressions will be directly identified. As a result, the magnitude of damages and also the efforts in maintenance can be enormously decreased by duly intervention. An appropriate SHMS allows an almost accurate identification of a structure’s behavior including possible uncertainties (frequently differing from those assumed when the structure was designed): Automatically analyzing the acquired data, damage can be detected at its early stage and the reliability of structures assessed in real-time. Meanwhile, the significance of automated monitoring of structures during their whole lifespan is also being increasingly recognized in practice. Consequently, the human experts (geodesists, geologists, etc.) taking part in monitoring will be supported purposefully in processing their specific tasks, using increased knowledge concerning the structure’s state; mandatory controls will be simplified to a great extent and visual inspections will be objectified substantially (Smarsly 2003). Within this research project, an autonomous SHMS for safety-relevant civil engineering structures is being currently developed. In this context, monitoring is regarded as a distributed engineering task. An adequate solution paradigm for structural monitoring, using distributed-collaborative processing, is the agent technology. By that, software agents – representing cooperating, concurrent working software units – can be used efficiently to solve monitoring problems, even those that have a highly inherent-distributed character. In total, the agent-based approach allows an exact simulation of the real world circumstances – thus, simulation means automated processing of tasks relevant to structural monitoring by software agents, supporting the human experts which are involved in monitoring. Decomposition of the monitoring problem Although the SHMS, introduced in this paper, will be adaptable to different types of structures (towers, buildings, dams, tunnels, etc.), in the first instance the focus lies on the monitoring of bridges, in particular reinforced concrete road bridges. Bridges are subjected to a wide variety of uncertainties with regard to materials, loads as well as aging, during their lifespan; in the field of bridge monitoring, the quantity of measuring data to be acquired is easily manageable. Beyond that, monitoring of bridges offers the advantage that, generally, the structural system responses of bridges are closer connected to the impacting loads, as, for example, those of solid buildings like dams. Therefore, taking into account the correlation between acquired impacting and reacting measured parameters, an expeditious – and also significant – evaluation of the SHMS and its components is possible. Problem analysis In order to develop an autonomous, agent-based SHMS adaptable on different structures, primarily the following processes and structures are to be abstracted, universally modeled and, then, mapped by software agents: - The operational, organizational structures of the enterprises consigned to structural monitoring, - the working processes necessary for a holistic monitoring and - the measuring infrastructures used.
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Organizational structures of monitoring enterprises. A functional analysis of the operational, organizational structures of monitoring enterprises indicates that the formal organizations are, generally, characterized by a largely hierarchical and, partially, by a heterarchical structure – albeit many enterprises are being reorganized currently, in particular in Germany: Each member (instance) within the organization obeys exactly its superior authority that is authorized to issue instructions to its inferior instances. Hence, jurisdictions as well as chains of instructions are clearly partitioned and, according to fig. 1, each instance is responsible for this “sub-tree” within the formal organization, whose root it is. Figure 1 exemplarily shows the abstracted operational, organizational structure of enterprises involved in monitoring. Therefore, regarding the conception and design of the agent-based SHMS, it is purposeful to arrange the different software agents – representing the several organization by congruent mapping of the human experts involved – in a modularhierarchical architecture characterized by delegation. In addition, it has to be taken into account that the hierarchical structure, exemplarily represented in figure 1, may not be limited to a defined number of layers but also be modifiable and extendible with regard to the respective enterprise considered.
Figure 1. Abstraction of the operational, organizational structures of enterprises consigned to structural monitoring
Working processes. A universal formalization of the operating processes, necessary for an appropriate structural monitoring, results in three interacting main processes that are present at every monitoring task and needs to be mapped by software agents: - data acquisition (including data management) - data analysis (online/offline), diagnosis and prognosis of the structural behavior (including the initiation of appropriate procedures if anomalies occured) - documentation of the accomplished monitoring tasks (further processing) Fig. 2 introduces these main processes using an abstract view on the monitoring problem: The first process to be carried out, the data acquisition, covers the acquisition of relevant parameters concerning the static as well as the operational safety of a structure. Hence, the structure has to be provided with a measuring and control system (to be depicted in detail later on), that is adapted to its type, dimension, position and utilization.
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Figure 2. Simplified illustration of main monitoring processes
In practice, the automated data analysis – both online and offline – consists of several subprocesses with different responsibilities that are frequently carried out by different, cooperating experts in spatially distributed departments. Consequently, only an adequate composition of the individual partial results will ensure a comprehensive and professional data analysis. With respect to automated processing, the data analysis, a highly non-trivial process, can be realized by several, more or less suitable methods; at this juncture, attention has to be paid to a set of basic options concerning the particular monitoring objective: Depending on (i) the extent of monitoring (global/local), (ii) the considered structural response (static/dynamic), (iii) the type of acquired parameters (mechanical/physical/chemical/environmental/service loads), (iv) the data acquisition (manual/online/offline) and (v) the periodicity of the measurements (periodic/continuous), different appropriate analyzing and processing frequencies can be defined. Customarily, these frequencies differ between short minuteby-minute intervals for verifying the plausibility of the acquired data and long temporal intervals of several years, compulsory for general inspections, for example in bridge monitoring. Appropriate analyzing techniques, then, are closely connected to the analyzing frequency and should be chosen depending whether a structure’s long-term or shortterm behavior is of interest. In general, numerous established instruments have recently been developed, capable of analyzing measured data efficiently, such as classification trees, Neural and Probabilistic Networks, Fuzzy Systems, etc. With respect to an automated, computer-based implementation – besides structural mechanical methods for analyzing both the long- and the short-term behavior using numerical models – suitable approaches, in particular, are based on statistical methods: On the one hand, the wide range of “classical” statistical methods can help to analyze the short-term behavior using regression techniques, correlations and time series. On the other hand, Data Mining and Machine Learning techniques (Witten 2000) – technically based on statistical methods – allow for an accurate identification and prediction of trends concerning the long-term behavior of a structure. A capable SHMS must cover these methods for analyzing the short-term as well as the long-term behavior. Moreover, it must systematically make these methods available to the human experts involved in monitoring, which also place emphasis on different time horizons and different structural views. After analyzing the data, the results of the data analysis along with further characteristic parameters and, possibly, additional (visual) inspections, can be used as
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a basis for a structural diagnosis and, afterwards, for a prognosis of deterioration progressions. The concluding process of documentation and further processing of the analysis results will usually be accomplished by using well-defined rules. However, with respect to different monitoring tasks, the documentation requirements differ, substantially: Basically, an annual safety report for dams, for example, differs both in form and content from the documentation of the bridge main inspection mentioned before. Measuring and control infrastructure. Data acquisition within an automated monitoring system is based on an extensive measuring infrastructure. In this paper, systems composed of two substantial components – sensors and data loggers – will be considered: The sensors, installed inside or at a structure, are controlled by data loggers via electronic impulses. After having received an impulse, the sensors return a signal which can be a measurement of voltage, resistance or frequency. The data logger, then, scales the signal into a value, and either stores it in an internal memory or transfers it to a local database. In order to systematically acquire all relevant parameters encompassing both static and operational safety, each structure must be provided with a measuring and control infrastructure, which then has to be adapted to the type, size and location of the structure. Furthermore, the design of the measuring infrastructure has to consider that the structure and its embedding environment should form a unity. Hence, besides the locations of expected weak points of a structure, also the natural environment of the structure should be provided with sensors. In some specific fields of structural monitoring there are different standards for conventional inspections and guidelines for the configuration of measuring devices (e.g. German DIN Group 1999, German Association for Water, Wastewater and Waste 1991). However, in most cases there are no compulsory requirements managing automated monitoring and, in particular, the instrumentation of structures with sensors. In this case, the instrumentation of structures strongly depends on the individual objective of monitoring. Fig. 3 shows temperature sensors (T) and displacement transducers (W) in the cross-section of the Schöps valley bridge (fig. 4) – a reinforced concrete bridge – near Görlitz (Germany) at its bearing (cp. Kaschner 2000): In this monitoring project, the objective was to get detailed information about the correlation between the displacements of the bridge superstructure and its temperature in order to verify guidelines concerning the effect of the application of reinforced elastomer bearings on the structural behavior of bridges.
Figure 3. Configuration of measuring devices at the Schöps valley bridge (Görlitz, Germany)
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Figure 4. Schöps valley bridge (taken from http://www.kodersdorf.de)
System Design Following the preliminarily developed problem decomposition, the agent-based SHMS is designed based on the Gaia methodology for agent-oriented analysis and design (Wooldridge 2000); after different approaches towards agent-based analysis and design have failed to adequately capture an agent's autonomous nature as well as its complex interactions, Gaia successfully introduces a general methodology that supports systematically the analysis and the design process of both the individual agent structures and the agent society: An agent system, in terms of Gaia, is composed of various roles interacting in an organized, artificial society. Thus, different abstract models are developed defining agents’ roles and their interactions within the agent system, in order to transform these abstract models, later on, into models at a sufficiently low level of abstraction so that they can be easily implemented. Before presenting details concerning the agent-based SHMS, figure 5 tries to depict graphically the intended agent organization. The architecture of the agent system, according to the previously developed decomposition of the monitoring problem, is primarily characterized by the utilization of the following agent categories: project agents, process agents (task agents), wrapper agents and cooperation agents.
Figure 5. Formalization of the agent organization
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Project agents The project management agent owns information (i) about the overall structure – as far as required for monitoring – and (ii) about the monitoring tasks to be carried out. Consequently, a project management agent handles the coordination of all interacting software agents within the agent system. Furthermore, it is designed that a transition and generalization to other problem domains is possible, e. g. monitoring of tunnels, etc. In other words, for each monitoring project, one project management agent is responsible. Fig. 6 shows, as a part of a developed Gaia roles model, the role of the project management agent with its permissions and responsibilities from where it emerged. The coordination of the agents is realized, in detail, by using high-level Petri nets. Petri nets are a graphical and mathematical tool for describing and analyzing systems that are characterized, for example, as being concurrent and distributed (Petri 1962). Depending on the present monitoring workflow, the project management agent, firstly, generates Petri nets mapping this workflow. The application of such generically constructed nets allows for a uncomplicated coordination of the software agents, for an efficient modification of the workflow and, moreover, for a capable administration of the resources available in the system.
Figure 6. Project management role as a part of the Gaia roles model
Process agents The second category of agents are the process agents. As described before, the main processes in structural monitoring encompass (i) data acquisition, (ii) data analysis and, also, (iii) reports concerning the structure’s state. To execute this tasks, the process agents are introduced: Parameters relevant for evaluating the structural behavior, are continuously acquired by the data acquisition agent and, then, permanently stored in a database (see fig. 5). After acquiring the data, the analysis agent autonomously (or after being instructed by a human expert) analyzes the measured data regarding both the short-term and the long-term behavior of the structure by using different analysis modules. In case of a detected anomalous structural behavior the human experts will be informed. For analyzing the short-term behavior, in a first step a statistical data analysis based
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on multiple regression has been implemented (fig. 7 shows the short-term analysis of measured data of the Schöps valley bridge introduced before). For analyzing the longterm behavior, a module using Data Mining techniques is currently being developed. The last main process – the documentation of the accomplished monitoring tasks – is carried out by the report agent that simply creates reports concerning the behavior of the structure.
Figure 7. Visualization of measured data within the automated data analysis
Wrapper agents According to fig. 5, each of the process agents interacts with wrapper agents that encapsulate, e. g., the electronic measuring equipment and databases used as well as external software, like FE- and CAD-programs. To make the encapsulated software and hardware available to the other agents, the wrapper agents provide specific services, accessible by other agents. To give a short example, fig. 8 shows a graphical interface to communicate with the database agent in order to administrate the encapsulated databases manually. In detail, hierarchical as well as relational databases can be included into the agent system and, as a result, used by software agents. Here, the hierarchical database Xindice from the Apache Software Foundation as well as the relational database MySQL from the MySQL AB have been included.
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Figure 8. Administration of databases used in the agent system
Cooperation agents The final agent category consists of cooperation agents that represent the specific organization of the human experts involved in monitoring – they simulate the behavior of the human actors and, by that, accomplish their tasks, autonomously. Hence, each expert is supported in solving specific tasks by a cooperation agent regarded as a “personal assistant”. Consequently, the cooperation agents provide the interface between a user and the agent system. Attention is paid to the fact that specific knowledge and expertise of the individual experts has to be incorporated into the agent system. Here, specific knowledge and expertise – for example, concerning the workflow of the tasks to be carried out by a particular human expert – is captured by the cooperation agents taking over the part of the corresponding human actor. Fig. 9 contains, as an excerpt of the so called Gaia acquaintance model, the communication paths between the cooperation agents; congruently arranged with the corresponding network of human actors, each cooperation agent basically communicates with that agent located in the layer beyond it, and vice versa. Also, communication takes place within one layer, e.g. different cooperation agents, mapping several specialist engineers (GeodesistAgent, GeologistAgent, etc.) and exchanging information in order to solve their specific monitoring tasks efficiently.
Figure 9. Communication links between cooperation agents
Summary This paper is to demonstrate an approach towards the decomposition of monitoring problems and, subsequently, the design of a monitoring system for security-relevant engineering structures based on software agents. Thereby, it was shown, how (i) the organizational structures of the institutions, entrusted with the structural monitoring, (ii) the working processes, necessary for a holistic monitoring, and (iii) the measuring infrastructures can be formalized and abstracted in order to obtain a generally ac-
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cepted formulation of these monitoring problems. Following this formalization, the decomposition of the problems and their adaptation on different software agents has been outlined. As shown, the agent paradigm is a powerful technology to solve distributed tasks efficiently by using single, autonomous “problem solving units”. These units process particular problems by having, in fact limited, but specific knowledge about their problem domains. The solutions of these individual problems, subsequently, will be composed to form a main solution. Currently, several software agents have already been prototypically implemented1. Thereby, the particular agents have been consistently developed on the basis of the Gaia methodology for agent-oriented analysis and design. However, as already outlined in the paper, the overall system is still being developed and the holistic agent-based composition of all partial solutions is still pending. References German DIN Group, Standards committee for Civil Engineering (1999): “DIN 1076: Engineering structures in connection with roads; inspection and test”, release 11/99 – ICS 93.010, replaces release 3/83, Berlin, Germany. German Association for Water, Wastewater and Waste – DWA (1991): “Meß- und Kontrolleinrichtungen zur Überprüfung der Standsicherheit von Staumauern und Staudämmen” – DVWK-Merkblätter, Essen, Germany. JADE (2004) – Java Agent Development Framework, Version 3.2. Tilab, Telecom Italia, Turin, Italy. Kaschner, R. (2000): “Flexible bearing of bridge superstructures: Triebischseitental structure measurements and bridge over the Weisser Schöps/Displacement measurements on the Sülte valley bridge – Final reports”. German Federal Highway Research Institute (Bundesanstalt für Straßenwesen), Bergisch Gladbach, Germany. Petri, C. A. (1962): “Kommunikation mit Automaten”. Ph.D. Thesis, University of Bonn, Germany. Smarsly, K., Bilek, J., Mittrup, I. and Hartmann, D. (2003). "Agent-based concepts for the holistic modeling of concurrent processes in structural engineering". ISPE, 10th International Conference on Concurrent Engineering: Research and Application. Madeira, Portugal. Concurrent Engineering – Advanced Design, Production and Management Systems, 47-53. Smarsly, K., Mittrup, I., Hartmann, D. et al. (2003). "An Agent-based Approach to Dam Monitoring". CIB, 20th CIB W78 Conference on Information Technology For Construction. Waiheke Island, Auckland, New Zealand. Construction IT Bridging the Distance – CIB Report, 239-246. Witten, I. H. et al. (2000): “Data Mining”. M. Kaufmann Pub., San Francisco, USA. Wooldridge, M., Jennings N. R., Kinny, D. (2000): "The Gaia Methodology for Agent-Oriented Analysis and Design". Autonomous Agents and Multi-Agent Systems, 3, 285-312. Kluwer Academic Publishers, Dordrecht, The Netherlands. 1
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Using Java 1.5.0 and the agent development framework JADE (JADE 2004).
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