Structural health monitoring (SHM) is increasingly being used in the field of bridge engineering, and the technology for monitoring bridges has undergone a radical change. It has evolved from the initial local monitoring and assessment, which relied mainly on manual work, to the current all-round and full-time intelligent assessment provided by intelligent monitoring systems. This paper reviews the development of structural health monitoring technology in the civil engineering field and examines two current AI methods in bridge structural health monitoring, namely knowledge-driven and data-driven approaches. The advantages and disadvantages of these two AI methods are analyzed, and future development trends are also discussed. The overview results reveal that knowledge-driven methods have the advantages of interpretability and stability. However, their current application is limited, and significant technical bottlenecks remain. On the other hand, the data-driven approach demonstrates higher efficiency and accuracy. Nevertheless, it is characterized by instability and insecurity due to its "black-box" nature, which hinders its ability to explain the internal operation mechanism. Given these findings, the hybrid knowledge-data-driven approach emerges as a potential solution. This approach can effectively integrate the advantages of both knowledge-driven and data-driven methods while avoiding their respective disadvantages. Consequently, the hybrid approach proves to be more stable, safe, and efficient in practical applications.

Advancing Bridge Structural Health Monitoring: Insights into Knowledge-Driven and Data-Driven Approaches

Pilot factor is worth considering when analyzing the causes of civil aviation accidents. This study introduces a data-driven Bayesian network (BN) approach to investigating the joint causal effects of pilot and other factors on civil aviation safety. A total number of 163 individual pilot-related accidents in the National Transportation Safety Board (NTSB) aviation accident database from 2008 to 2020 are analyzed, focusing on eliciting the causal effects of various potential risk factors, including pilot factors, on civil aviation accidents. The modeling of the interdependency among the risk influencing factors (RIFs) and their causal contributory effect on the accident outcome is structured by a tree augmented network (TAN) and validated by sensitivity analysis. The novelty of this study is to incorporate pilot factors derived from the civil aviation accident database into risk analysis, combined with other external factors. The results indicate that weather conditions and flight phases are more correlated with casualty types of civil aviation accidents than pilot action and decision, and three other pilot factors only contribute to fatal injury in civil aviation accidents.

/pdf/incorporation-of-pilot-factors-into-risk-analysis-of-civil-10nqs7ag.pdf

Incorporation of Pilot Factors into Risk Analysis of Civil Aviation Accidents from 2008 to 2020: A Data-Driven Bayesian Network Approach

This paper introduces an Expert System acting as an intelligent agent to interrogate past aircraft occurrence data to support the aircraft occurrence investigators. This Expert System can potentially identify similar occurrences that provide indicators of contributing factors that may otherwise have been missed. This intelligence comes from the Expert System’s ability to learn from past occurrence data using the combined Fuzzy Logic System (FLS) inference technique and AcciMaps to emulate the aircraft occurrence investigators’ evidence analytical reason model. The learned expert knowledge is presented graphically and interactively to the user (investigator) using the AcciMaps. The paper includes an overview of the Australian Transport Safety Bureau (ATSB) investigation evidence analysis workflow, popular mainstream inference techniques used in Expert Systems, evaluation of the proposed Fuzzy Logic System (FLS) inference technique and AcciMap, including an example study. The FLS inference technique has met the Expert System requirements to support aviation occurrence investigations. 

Designing an expert system to support aviation occurrence investigations

In the contemporary global market, organizations face intense competition requiring the deployment of information technology (IT) for effective decision-making and to maintain market presence. This review article emphasizes the crucial role of information technologies in organizational decision-making processes. It outlines the evolution of data management tools, especially in the context of dynamic and contingent data management, and explores the adoption and implementation of technologies for information control within organizations. By analyzing various methodologies and results from existing research, the article illustrates the increasing reliance on IT to enhance organizational processes, decision-making, and overall competitiveness. Furthermore, it delves into the systematic review approach to understand the significance and application of information management technologies in organizational contexts, offering insights for entrepreneurs and academics. The review concludes with the assertion that proper information management through technology is vital for organizations to remain competitive in the ever-changing market landscape.

Technologies applied to information control in organizations: A review

Fires frequently contribute to aircraft accidents, leading to significant damage, injuries, and fatalities. Nevertheless, the increasing complexity of modern large aircraft structures, along with the utilization of new aviation fuels and materials, has further complicated aircraft fire accidents. Therefore, the application of scientific methodologies and technological advancements is crucial for investigating and determining the causes of aircraft fires. This paper analyzes and discusses various techniques employed in the investigation of aircraft fires, encompassing fire behavior characterization, analysis of aviation material debris, and evaluation of ignition energy. Additionally, it presents an overview and comprehensive discussion of the scientific investigation process employed in aircraft fire incidents. Lastly, this paper presents recent case studies of typical aircraft fires, including crash fires, engine fires, electrical failures, equipment failures, and fires caused by external sources. This study has three overarching themes. The study provides a comprehensive review of the current research on aircraft fire-related issues while highlighting the investigation process and scientific analysis methods employed in aircraft fire incidents. Case studies demonstrate technical investigations of typical aircraft fire incidents are provided. These cases exemplify the complexity involved in aircraft fire investigations and emphasize the criticality of analyzing failure particulars in understanding fire incidents. This paper promote the advancement of technical investigations of aircraft fire incidents by advocating for the updating of research findings and investigation practices related to aircraft fires. 

A Review of Aircraft Fire Accident Investigation Techniques: Research, Process, and Cases

The International Civil Aviation Organisation (ICAO) audits its Member States for safety oversight and monitors its Effective Implementation (EI). The global average EI was 68.83% in 2019, with 46% audited Member States achieving 75% EI (2022 target); however, an ICAO (2016 – 2018) audit highlighted six Annex 13 non-compliance issues. One issue was that more than 60% of Member States do not have a comprehensive and detailed investigator training program, contributing to many shortcomings that include a lack of essential and volatile evidence preservation, investigation management, investigation reporting and/or safety recommendations. This paper proposes an Expert System that captures knowledge in aircraft accident investigation generated over many years and allows aircraft investigators to share, access and interrogate accumulated knowledge to support the aircraft accident investigation process. The Expert System will improve the evidence analysing timeline, conclusion consistency and accuracy and support the on-the-job “field” training of evidence analysis through self-discovery.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/F5578C2BC91FC03D07904B428356EF41/S0001924021000117a.pdf/div-class-title-an-expert-system-framework-to-support-aircraft-accident-and-incident-investigations-div.pdf

An expert system framework to support aircraft accident and incident investigations

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Designing an expert system to support aviation occurrence investigations

An expert system framework to support aircraft accident and incident investigations