Long Ding

736 total citations
22 papers, 506 citations indexed

About

Long Ding is a scholar working on Statistics, Probability and Uncertainty, Radiological and Ultrasound Technology and Safety, Risk, Reliability and Quality. According to data from OpenAlex, Long Ding has authored 22 papers receiving a total of 506 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Statistics, Probability and Uncertainty, 11 papers in Radiological and Ultrasound Technology and 11 papers in Safety, Risk, Reliability and Quality. Recurrent topics in Long Ding's work include Risk and Safety Analysis (20 papers), Occupational Health and Safety Research (11 papers) and Combustion and Detonation Processes (6 papers). Long Ding is often cited by papers focused on Risk and Safety Analysis (20 papers), Occupational Health and Safety Research (11 papers) and Combustion and Detonation Processes (6 papers). Long Ding collaborates with scholars based in China, Canada and United States. Long Ding's co-authors include Faisal Khan, Jie Ji, Xiaoxue Guo, Jie Ji, Qi Tong, Valerio Cozzani, Xiao‐Hua Li, Jiansong Wu, Jiping Zhu and Jiping Zhu and has published in prestigious journals such as Reliability Engineering & System Safety, Risk Analysis and Safety Science.

In The Last Decade

Long Ding

19 papers receiving 491 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Long Ding China 11 378 200 160 109 57 22 506
V. R. Renjith India 10 352 0.9× 182 0.9× 127 0.8× 58 0.5× 63 1.1× 33 525
Jiansong Wu China 8 230 0.6× 95 0.5× 90 0.6× 63 0.6× 59 1.0× 10 353
Ulrich Hauptmanns Germany 12 384 1.0× 169 0.8× 137 0.9× 123 1.1× 15 0.3× 38 533
Mohammed Taleb‐Berrouane Canada 14 452 1.2× 185 0.9× 132 0.8× 78 0.7× 108 1.9× 20 746
Jian Kang China 15 180 0.5× 80 0.4× 63 0.4× 116 1.1× 157 2.8× 46 563
Seyed Javad Hashemi Canada 12 300 0.8× 144 0.7× 81 0.5× 25 0.2× 66 1.2× 21 464
Snorre Sklet Norway 12 761 2.0× 658 3.3× 183 1.1× 51 0.5× 111 1.9× 17 865
Xiaojie Tian China 9 308 0.8× 140 0.7× 86 0.5× 37 0.3× 131 2.3× 17 543
Jinkun Men China 14 184 0.5× 74 0.4× 54 0.3× 84 0.8× 39 0.7× 30 402
Samith Rathnayaka Canada 8 759 2.0× 486 2.4× 188 1.2× 168 1.5× 129 2.3× 8 939

Countries citing papers authored by Long Ding

Since Specialization
Citations

This map shows the geographic impact of Long Ding's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Long Ding with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Long Ding more than expected).

Fields of papers citing papers by Long Ding

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Long Ding. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Long Ding. The network helps show where Long Ding may publish in the future.

Co-authorship network of co-authors of Long Ding

This figure shows the co-authorship network connecting the top 25 collaborators of Long Ding. A scholar is included among the top collaborators of Long Ding based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Long Ding. Long Ding is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
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Ding, Long, et al.. (2025). A novel integrated multi-source heterogeneous uncertainty fusion framework for reducing the complexity of uncertainty characterization in domino effect risk assessment. Journal of Loss Prevention in the Process Industries. 99. 105804–105804. 1 indexed citations
3.
Ma, Qiang, Long Ding, & Jie Ji. (2025). Probabilistic Assessment for Structural Failure Severity of Aero‐Engine Components in Fire Scenarios Based on Cumulative Failure Frequency. Quality and Reliability Engineering International. 42(2). 723–739.
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Ding, Long, et al.. (2024). Building reliability of risk assessment of domino effects in chemical tank farm through an improved uncertainty analysis method. Reliability Engineering & System Safety. 252. 110388–110388. 10 indexed citations
6.
Ding, Long, et al.. (2023). A Combined Method to Build Bayesian Network for Fire Risk Assessment of Historical Buildings. Fire Technology. 59(6). 3525–3563. 9 indexed citations
7.
Guo, Xiaoxue, Long Ding, Jie Ji, & Valerio Cozzani. (2022). A cost-effective optimization model of safety investment allocation for risk reduction of domino effects. Reliability Engineering & System Safety. 225. 108584–108584. 17 indexed citations
8.
Guo, Xiaoxue, et al.. (2022). A dynamic individual risk management method considering spatial and temporal synergistic effect of toxic substance leakage and fire accidents. Process Safety and Environmental Protection. 169. 238–251. 5 indexed citations
9.
Ding, Long, Haowei Hu, & Jie Ji. (2022). Holistic Value-at-Risk Assessment Framework for Fire Risk Assessment of Heritage Buildings Based on Analytic Hierarchy Process and Text Mining. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part A Civil Engineering. 9(1). 2 indexed citations
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Guo, Xiaoxue, et al.. (2021). Fuzzy Bayesian network based on an improved similarity aggregation method for risk assessment of storage tank accident. Process Safety and Environmental Protection. 149. 817–830. 114 indexed citations
13.
Ding, Long, Faisal Khan, & Jie Ji. (2021). A novel vulnerability model considering synergistic effect of fire and overpressure in chemical processing facilities. Reliability Engineering & System Safety. 217. 108081–108081. 50 indexed citations
14.
Guo, Xiaoxue, Jie Ji, Faisal Khan, & Long Ding. (2021). Withdrawal notice to “Fuzzy Bayesian network based on an improved similarity aggregation method for risk assessment of storage tank accident” [PSEP 144 (2020) 242–252]. Process Safety and Environmental Protection. 149. 1031–1031. 4 indexed citations
15.
Ding, Long, Faisal Khan, & Jie Ji. (2020). Risk-based safety measure allocation to prevent and mitigate storage fire hazards. Process Safety and Environmental Protection. 135. 282–293. 70 indexed citations
16.
Ding, Long, Jie Ji, & Faisal Khan. (2020). Combining uncertainty reasoning and deterministic modeling for risk analysis of fire-induced domino effects. Safety Science. 129. 104802–104802. 34 indexed citations
17.
Ding, Long, Faisal Khan, & Jie Ji. (2020). A novel approach for domino effects modeling and risk analysis based on synergistic effect and accident evidence. Reliability Engineering & System Safety. 203. 107109–107109. 40 indexed citations
18.
Ji, Jie, et al.. (2020). Fire risk assessment in cotton storage based on fuzzy comprehensive evaluation and Bayesian network. Fire and Materials. 44(5). 683–692. 11 indexed citations
19.
Ding, Long, Faisal Khan, Xiaoxue Guo, & Jie Ji. (2020). A novel approach to reduce fire-induced domino effect risk by leveraging loading/unloading demands in chemical industrial parks. Process Safety and Environmental Protection. 146. 610–619. 26 indexed citations
20.
Ding, Long, et al.. (2019). Quantitative fire risk assessment of cotton storage and a criticality analysis of risk control strategies. Fire and Materials. 44(2). 165–179. 22 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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