ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering

Aims & Scope

The ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering disseminates research findings, best practices and concerns, and discussion and debate on risk and uncertainty related issues.

The journal reports on the full range of risk and uncertainty analysis state-of-art and state-of-practice relating to mechanical engineering, including but not limited to risk quantification based on hazard identification, scenario development and rate quantification, consequence assessment, valuations, perception, communication, risk-informed decision making, uncertainty analysis and modeling, and other related areas.

Specific topic areas including, but not limited to: • Risk Analysis Methods • Uncertainty Analysis and Quantification • Management, Financial, and Insurance Issues • Computational Methods • Applications areas include every aspect of mechanical engineering systems such as: - Mechanical Assets and Infrastructure - Human Factors, Accidents and Disasters - Materials and Electromechanical - Energy including Renewables - Manufacturing - Sustainability and Resilience - Water Resources, Coastal and Ocean Systems - Climate Risk and Adaptation - Bioengineering - Nuclear Engineering - Information Storage and Processing Including Big Data - Project Management and Construction Engineering

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	2.3
2024	1.80

SJR (SCImago Journal Rank)

Year	Value
2024	0.545

Quartile

Year	Value
2024	Q2

Journal Rank

Year	Value
2024	10668

Journal Citation Indicator

Year	Value
2024	304

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Engineering and Social Sciences, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Digital Twins: State-of-the-Art and Future Directions for Modeling and Simulation in Engineering Dynamics Applications

  Citation: 182

  Authors: D. J., K., R. J., P.

• Optimizing Predictive Maintenance With Machine Learning for Reliability Improvement

  Citation: 46

  Authors: Yali

• Autonomous Vehicle Accident Data Analysis: California OL 316 Reports: 2015–2020

  Citation: 40

  Authors: Roger L.

• On Digital Twins, Mirrors, and Virtualizations: Frameworks for Model Verification and Validation

  Citation: 31

  Authors: K., E. J., R. J., D. J., P.

• Comparison of the HAZOP, FMEA, FRAM, and STPA Methods for the Hazard Analysis of Automatic Emergency Brake Systems

  Citation: 30

  Authors: Liangliang, Yan-Fu, Enrico

• Pipeline Inspection Gauge Position Estimation Using Inertial Measurement Unit, Odometer, and a Set of Reference Stations

  Citation: 28

  Authors: Md Sheruzzaman, Mamoun F.

• Experimental Time-Domain Vibration-Based Fault Diagnosis of Centrifugal Pumps Using Support Vector Machine

  Citation: 26

  Authors: Janani Shruti, Rajiv

• Resilience of Metrorail Networks: Quantification With Washington, DC as a Case Study

  Citation: 23

  Authors: Yalda, Bilal M., Yanjie, Dongming, Hongwei

• Resilience Analysis Framework for Interconnected Critical Infrastructures

  Citation: 23

  Authors: X., E., E.

• Uncertainty Quantification for Additive Manufacturing Process Improvement: Recent Advances

  Citation: 23

  Authors: Sankaran, Paromita, Zhen

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