Identifying Risk Factors Influencing Traffic Accidents for Baghdad Expressways

Joni, Hasan H.; Jasim, Mustafa

doi:10.30684/etj.2021.131184.1011

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…Empirical data analysis and statistical analysis have been commonly used methods to identify and calculate risk factors (Friedman, 2001;Friedman and Meulman, 2003;Joni and Jasim, 2022;Lieggio Júnior, 2008;Yang et al, 2010).Some authors have also employed other techniques like failure mode and criticality analysis (Ammar et al, 2014), multicriteria analysis in a fuzzy environment (Cordeiro et al, 2014;Yang et al, 2018), and the use of Quality Function Deployment (QFD) (Cordeiro et al, 2014).…”

Section: Risk Calculation In the Rctmentioning

confidence: 99%

Bayesian Modelling Applied to Evaluate Road Cargo Transport Risks

Teles¹,

Librantz

Neto³

et al. 2023

Preprint

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In recent years, applying quantitative risk analysis in Road cargo transport (RCT) has yielded successful results in assessing risks associated with industrial industries. Recent studies have highlighted the significance of different modes of transportation (such as roads, rails, pipelines, and inland waterways)for hazardous materials, emphasizing their role in determining risk levels. Among these modes, road transport via trucks stands out as a crucial component for economic development and is commonly employed for transporting various types of cargo. To effectively evaluate the risk level of a given activity within RCT, it is essential to determine the severity index for each potential situation that may arise during the transportation process. In this context, this research aims to investigate and calculate the risks associated with RCT by employing Bayesian Networks. Computational models were implemented using Bayesian Network software, and data input was carried out using Microsoft Excel®spreadsheets. The methodology employed for this study entailed field research with the involvement of experts and academic sources, along with the utilization of a systematic literature review. Additionally, the Delphi technique was applied, followed by a survey. The results pointed that the proposed model could effectively aid in identifying the level of risk involved in RCT operations across different scenarios. Furthermore, this model enables managers to evaluate the probability of one or more risk factors occurring during operations, facilitating the implementation of more efficient mitigation measures.

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Section: Risk Calculation In the Rctmentioning

confidence: 99%

Bayesian Modelling Applied to Evaluate Road Cargo Transport Risks

Teles¹,

Librantz

Neto³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Evaluating Rutting Resistance of Rejuvenated Recycled Hot-Mix Asphalt Mixtures Using Different Types of Recycling Agents

et al. 2022

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Growing environmental pollution worldwide is mostly caused by the accumulation of different types of liquid and solid wastes. Therefore, policies in developed countries seek to support the concept of waste recycling due to its significant impact on the environmental footprint. Hot-mix asphalt mixtures (HMA) with reclaimed asphalt pavement (RAP) have shown great performance under rutting. However, incorporating a high percentage of RAP (>25%) is a challenging issue due to the increased stiffness of the resulting mixture. The stiffness problem is resolved by employing different types of commercial and noncommercial rejuvenators. In this study, three types of noncommercial rejuvenators (waste cooking oil (WCO), waste engine oil (WEO), and date seed oil (DSO)) were used, in addition to one type of commercial rejuvenator. Three percentages of RAP (20%, 40%, and 60%) were utilized. Mixing proportions for the noncommercial additives were set as 0–10% for mixtures with 20% RAP, 12.5–17.5% for mixtures with 40% RAP, and 17.5–20% for mixtures with 60% RAP. In addition, mixing proportions for the commercial additive were set as 0.5–1.0% for mixtures with 20% RAP, 1.0–1.5% for mixtures with 40% RAP, and 1.5–2.0% for mixtures with 60% RAP. The rutting performance of the generated mixtures was indicated first by using the rutting index (G*/sin δ) for the combined binders and then evaluated using the Hamburg wheel-track test. The results showed that the rejuvenated mixtures with the commercial additive at 20 and 60% RAP performed well compared to the control mixture, whereas the rejuvenated ones at 40% RAP performed well with noncommercial additives in comparison to the control mixture. Furthermore, the optimum percentages for each type of the used additives were obtained, depending on their respective performance, as 10%, 12.5%, and 17.5% of WCO, 10%, 12.5–17.5%, and 17.5% of WEO, <10%, 12.5%, and 17.5% of DSO, and 0.5–1.0%, 1.0%, and 1.5–2.0% of the commercial rejuvenator, corresponding to the three adopted percentages of RAP.

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Identifying Risk Factors Influencing Traffic Accidents for Baghdad Expressways

Cited by 2 publications

References 13 publications

Bayesian Modelling Applied to Evaluate Road Cargo Transport Risks

Bayesian Modelling Applied to Evaluate Road Cargo Transport Risks

Evaluating Rutting Resistance of Rejuvenated Recycled Hot-Mix Asphalt Mixtures Using Different Types of Recycling Agents

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