Mathematical Problems in Engineering

Advanced Data Analytics Techniques for Risk-Based Engineering Problems

Publishing date
01 Jun 2022
Submission deadline
14 Jan 2022

Lead Editor

1Urmia University of Technology, Urmia, Iran

2Munzur University, Tunceli, Turkey

This issue is now closed for submissions.

Advanced Data Analytics Techniques for Risk-Based Engineering Problems

This issue is now closed for submissions.


Currently, all real-world problems are accompanied by risks and uncertainties affecting the performance of various organizations. Addressing this inherent uncertainty can improve the performance of organizations, and production or service systems. Additionally, assessing failures and analyzing risks in a system can help managers and engineers reduce and control the negative effects of such risks. Given the high dependence on risk assessment and management of data, developing and introducing risk analysis approaches based on data analytics can provide more reliable and practical solutions for decision-makers. Data analytics is the process of examining and analyzing raw data to reach certain conclusions about that information. In doing so, forecasting, simulation, ranking, and clustering techniques are powerful tools available which are being used in this field. In addition to this, data science techniques based on mathematical and statistical approaches have a positive role in solving problems related to risk analysis.

The costs of damages related to risk have led organizations to use preventative approaches. In this regard, algorithms and systematic approaches of risk engineering can be very efficient in identifying risks, assessing and estimating the amount of damage until finding the best solutions to reduce the effect. Such approaches can enhance the quality of services and products and increase the safety of the system in an uncertain environment. As a result, the applications of systematic and comprehensive approaches in solving risk-based problems in organizations have attracted the attention of many managers and professionals. Given the inherent uncertainty and complexity of such problems, data analytics technique-based developed approaches can improve the performance of decision-makers in risk analysis and engineering by focusing on the critical failures or risks of the studied systems and helping them provide effective preventive measures.

The aim of this Special Issue is to introduce the applications of data analytics-based approaches to solve engineering problems associated with risk and recent advances in the development of risk analysis approaches. Original research and review articles are welcome.

Potential topics include but are not limited to the following:

  • Industrial and systems engineering
  • Product development and service design
  • Operations and production management
  • Quality engineering and performance improvement
  • Logistics engineering and supply chain management
  • Project engineering and management
  • Occupational and environmental safety engineering
  • Healthcare engineering and management
  • Resilience engineering and crisis management
Mathematical Problems in Engineering
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