Real-Time Intelligent Decision Support System for Bridges Structures Behavior Prediction

Quintela, Hélder; Santos, Manuel Filipe; Cortez, Paulo

doi:10.1007/978-3-540-77002-2_11

Cited by 5 publications

(4 citation statements)

References 9 publications

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“…For instance, Yin (2010) developed an intelligent decision support system that quantifies the inspection data and evaluates the deterioration of the existing bridges, in addition to providing an optimum bridge monitoring plan for advanced management according to the project budget and timeline [12]. Quintela (2007), as another example, presents a real-time decision support system for civil engineering structures that makes use of prediction models using artificial neural networks and data mining techniques. The system occupies real-time sensors to verify the accuracy of the employed prediction models [20].…”

Section: Figurementioning

confidence: 99%

“…Quintela (2007), as another example, presents a real-time decision support system for civil engineering structures that makes use of prediction models using artificial neural networks and data mining techniques. The system occupies real-time sensors to verify the accuracy of the employed prediction models [20]. In a different angle, Jiao (2013) proposed an unsupervised performance evaluation strategy for bridges using fuzzy clustering on health monitoring data [21].…”

Section: Figurementioning

confidence: 99%

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A Novel Decision Support System for Long-Term Management of Bridge Networks

2021

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Developing a bridge management strategy at the network level with efficient use of capital is very important for optimal infrastructure remediation. This paper introduces a novel decision support system that considers many aspects of bridge management and successfully implements the investigated methodology in a web-based platform. The proposed decision support system uses advanced prediction models, decision trees, and incremental machine learning algorithms to generate an optimal decision strategy. The system aims to achieve adaptive and flexible decision making while entailing powerful utilization of nondestructive evaluation (NDE) methods. The NDE data integration and visualization allow automatic retrieval of inspection results and overlaying the defects on a 3D bridge model. Furthermore, a deep learning-based damage growth prediction model estimates the future condition of the bridge elements and utilizes this information in the decision-making process. The decision ranking takes into account a wide range of factors including structural safety, serviceability, rehabilitation cost, life cycle cost, and societal and political factors to generate optimal maintenance strategies with multiple decision alternatives. This study aims to bring a complementary solution to currently in-use systems with the utilization of advanced machine-learning models and NDE data integration while still equipped with main bridge management functions of bridge management systems and capable of transferring data to other systems.

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Section: Figurementioning

confidence: 99%

Section: Figurementioning

confidence: 99%

A Novel Decision Support System for Long-Term Management of Bridge Networks

2021

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“…use DM techniques to do the automatic assessment of barrage water quality. A real-time decision support system for civil engineering structures is presented in Quintela, Santos, and Cortez (2007). Lappas (2009) presents applications to societal benefit areas such as helpdesks and recommendation systems, digital libraries, e-learning, security and crime investigation, e-government services, and e-politics and edemocracy.…”

Section: Data Mining and Knowledge Discovery In Databasesmentioning

confidence: 99%

Extending QMBE Language with Clustering

Azevedo

Santos

2013

International Journal of Decision Support System Technology

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Business Intelligence (BI) is an important area of the Decision Support Systems (DSS) discipline. Over the past years, the evolution in this area has been considerable. Similarly, in the last years, there has been a huge growth and consolidation of the Data Mining (DM) field. DM is being used with success in BI systems, but a truly DM integration with BI is lacking. This creates a gap between DM and BI systems. With the purpose of closing this gap a new DM language for BI, named as Query-Models-By-Example (QMBE), was envisaged and implemented with success, but addressing only classification rules. This paper presents an extension of QMBE language to include clustering. This represents one more step towards the integration of DM with BI, which constitutes an important issue.

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“…due to smoke).It should be noted that the examination of such monitoring are among the innovative and pioneering in the world. The few published work in this area include among others [2], [5][6].…”

Section: Introductionmentioning

confidence: 99%

Selected methods of measuring deformations as a part of a system for monitoring structures

Baszkiewicz

Kolanowski

Krembuszewski

et al. 2014

The 9th International Conference "Environmental Engineering 2014"

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Real-time evaluation of deformations, like stress, strain, displacements of structure components can provide essential information about the state of the structure. It's very important to assess correctly the condition of the structure, to identify its most important parts to be measured. In order to achieve this goal, it is necessary to choose proper methods of measurements and right instruments. The geodetic techniques of acquiring information about deformations have recently strongly intended for remote methods. This trend can be observed for example in using reflectors for distance measurements, interferometry, scanning or remote sensing methods. Very promising are also approaches employing instruments like clearance gauge or dial gauge. Evaluation of structure deformations is quite sophisticated. It cannot be carried out by geodesists alone on every day routine. The paper presents different measurement methods of deformations for different structures ranging from buildings to bridges, together with the attempt to determine the most suitable ones for each of them. The best technique is chosen based on the accuracy requirements, length of the measurement time and the total cost of the survey including its data processing. Finally the paper takes into consideration that some of the methods need to be backed up by alternative ones and proposes the idea of combining techniques to achieve the goal.

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Real-Time Intelligent Decision Support System for Bridges Structures Behavior Prediction

Cited by 5 publications

References 9 publications

A Novel Decision Support System for Long-Term Management of Bridge Networks

A Novel Decision Support System for Long-Term Management of Bridge Networks

Extending QMBE Language with Clustering

Selected methods of measuring deformations as a part of a system for monitoring structures

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