Applying AI to structural safety monitoring and evaluation

Salvaneschi, Paolo; Cedei, M.; Lazzari, Marco

doi:10.1109/64.511774

Cited by 29 publications

(8 citation statements)

References 3 publications

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“…Traditionally, this is the role of a supervising engineer, but this has been taken over by developments of the monitoring system. Salvaneschi et al (1996) reported that the commercial implementation of the information system under the name of MIDAS had, since 1985, been managing data of 200 dams in 10 different countries.…”

Section: (A ) Damsmentioning

confidence: 99%

“…Recognizing the limitations of such a system, ENEL foresaw the need to integrate the formalized tools, such as MIDAS, with the non-formal information from historical observations and engineering judgment. Development of two AI applications, DAMSAFE and MISTRAL, is also described by Salvaneschi et al (1996). MISTRAL is a real-time system that considers groups of effects with or without relation to influences.…”

Section: (A ) Damsmentioning

confidence: 99%

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Structural health monitoring of civil infrastructure

Brownjohn

2006

Phil. Trans. R. Soc. A.

736

396

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Structural health monitoring (SHM) is a term increasingly used in the last decade to describe a range of systems implemented on full-scale civil infrastructures and whose purposes are to assist and inform operators about continued 'fitness for purpose' of structures under gradual or sudden changes to their state, to learn about either or both of the load and response mechanisms. Arguably, various forms of SHM have been employed in civil infrastructure for at least half a century, but it is only in the last decade or two that computer-based systems are being designed for the purpose of assisting owners/operators of ageing infrastructure with timely information for their continued safe and economic operation. This paper describes the motivations for and recent history of SHM applications to various forms of civil infrastructure and provides case studies on specific types of structure. It ends with a discussion of the present state-of-the-art and future developments in terms of instrumentation, data acquisition, communication systems and data mining and presentation procedures for diagnosis of infrastructural 'health'.

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Section: (A ) Damsmentioning

confidence: 99%

Section: (A ) Damsmentioning

confidence: 99%

Structural health monitoring of civil infrastructure

Brownjohn

2006

Phil. Trans. R. Soc. A.

736

396

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“…This automatically received data from a system for monitoring and performs diagnosis of the state of the dam. As a registered trade mark for CESI, It has been installed on several dams in the country and even abroad and also on landslides under Eyelet, and on monuments under Kaleidos [9].…”

Section: Related Workmentioning

confidence: 99%

Developing an Expert System to Assist Businesses in Financial Decision Making to Enhance Efficiency

Inusah¹,

Acheampong²

2018

IJCA

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This research proposes developing an expert system to assist businesses in financial decision making. It is a more technological means of storing and using the knowledge of the human expert. This helps in minimizing cost and inconveniences in hiring experts. No business can survive without effective and efficient management. The major activity in management is decision making. The efficiency of management in decision making is based on speed, accuracy and how easy it is for the decisions to be implemented. This is a herculean task for an average expert. As an exploratory research, the responses of all the fifteen (15) managers were carefully analyzed to get a solution to the problem. With a target of using one day in making a decision, only sixty percent (60%) of human decisions were accurate whiles hundred percent (100%) of the decisions on expert systems were accurate. The speed of the human expert is one (1) decision for every seven (7) days representing 0.14 decisions per day whiles that of the expert system is one (1) decision per day. Only two (2) decisions out of every five (5) decision of the human decision were easy to implement while all the five (5) decisions were easy to implement using the expert system due to the clarity and consistency in its results. The cost of hiring the human expert and the bureaucracy in decision making can be eliminated if an expert system is used. Also, interoperability (systems developed to pick data automatically from other systems) minimizes the errors in data entry.

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“…They carry out rule matching on each input electronic medical record in order to chase down the disease which fits these diagnosis rules best and make a diagnose for the disease. This kind of methods have made great achievements in the field of medical auxiliary diagnosis [18][19][20] . These models hope to imitate the logical reasoning process in the diagnosis procedure of a doctor which makes its diagnosis more logical.…”

mentioning

confidence: 99%

Clinical Assistant Diagnosis for Electronic Medical Record Based on Convolutional Neural Network

Yang

Huang

Jiang

et al. 2018

Sci Rep

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Automatically extracting useful information from electronic medical records along with conducting disease diagnoses is a promising task for both clinical decision support(CDS) and neural language processing(NLP). Most of the existing systems are based on artificially constructed knowledge bases, and then auxiliary diagnosis is done by rule matching. In this study, we present a clinical intelligent decision approach based on Convolutional Neural Networks(CNN), which can automatically extract high-level semantic information of electronic medical records and then perform automatic diagnosis without artificial construction of rules or knowledge bases. We use collected 18,590 copies of the real-world clinical electronic medical records to train and test the proposed model. Experimental results show that the proposed model can achieve 98.67% accuracy and 96.02% recall, which strongly supports that using convolutional neural network to automatically learn high-level semantic features of electronic medical records and then conduct assist diagnosis is feasible and effective.

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Applying AI to structural safety monitoring and evaluation

Cited by 29 publications

References 3 publications

Structural health monitoring of civil infrastructure

Structural health monitoring of civil infrastructure

Developing an Expert System to Assist Businesses in Financial Decision Making to Enhance Efficiency

Clinical Assistant Diagnosis for Electronic Medical Record Based on Convolutional Neural Network

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