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209 Research products, page 1 of 21

  • Canada
  • Natural Sciences and Engineering Research Council of Canada
  • Transport Research

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  • Closed Access
    Authors: 
    Ali Zirahi; Ali Haddadnia; Mohammadjavad Mohammadi; Bahareh Azinfar; Mohsen Zirrahi; Hassan Hassanzadeh; Jalal Abedi;
    Publisher: Elsevier BV
    Project: NSERC

    Abstract Environmental impact and economics of Carbon Dioxide (CO2) and bitumen transportation are among the major challenges of oil sands operations. We propose and assess a new approach to address these important challenges by using diluted bitumen (DilBit) as a carrier for large-scale CO2 transportation. The proposed approach provides a unique prospect to significantly reduce the cost of CO2 transportation from the carbon capture and storage (CCS) value chain, facilitate more efficient detection of DilBit spills from pipelines, utilize CO2, and improve public perception of both oil sands and CCS operations. These opportunities will offer the possibility of sustaining access to oil resources while reducing environmental impact and improving the economics of CCS and oil sands operations. Through experimental measurements, we have shown that 80–200 kg of CO2 per m3 of DilBit can be dissolved and transported. We also report simulation results from the simultaneous transportation of DilBit and CO2 and DilBit spill detection through monitoring concentration of leaked CO2.

  • Closed Access
    Authors: 
    Zainab Almheiri; Mohamed A. Meguid; Tarek Zayed;
    Publisher: Elsevier BV
    Project: NSERC

    Population growth and urbanization worldwide entail the need for continuous renewal plans for urban water distribution networks. Hence, understanding the long-term performance and predicting the service life of water pipelines are essential for facilitating early replacement, avoiding economic losses, and ensuring safe transportation of drinking water from treatment plants to consumers. However, developing a suitable model that can be used for cases where data are insufficient or incomplete remains challenging. Herein, a new advanced meta-learning paradigm based on deep neural networks is introduced. The developed model is used to predict the risk index of pipe failure. The effects of different factors that are considered essential for the deterioration modeling of water pipelines are first examined. The factors include seasonal climatic variation, chlorine content, traffic conditions, pipe material, and the spatial characteristics of water pipes. The results suggest that these factors contribute to estimating the likelihood of failure in water distribution pipelines. The presence of chlorine residual and the number of traffic lanes are the most critical factors, followed by road type, spatial characteristics, month index, traffic type, precipitation, temperature, number of breaks, and pipe depth. The proposed approach can accommodate limited, high-dimensional, and partially observed data and can be applied to any water distribution system.

  • Closed Access
    Authors: 
    Sonia Ghatora; Mayank Kumar; Mahdi Vaezi; Amit Kumar; David C. Bressler;
    Publisher: Elsevier BV
    Project: NSERC

    Abstract Pipeline transport of biomass is an economically viable and technically feasible approach to replace conventional truck delivery approach and make the biomass-based energy industry more competitive with fossil fuel-based plants. A 25 m long and 50 mm diameter closed-circuit pipeline facility was fabricated to experimentally investigate the mechanical and chemical feasibility of transporting agricultural residue biomass-water mixtures (slurries) through pipelines. This research used the pipeline facility to study the loss of sugars (glucose and xylose) while pipelining wheat straw-water mixtures. The release of similar sugars was also measured in shake-flask cultures under controlled conditions. The output of this research is important for bio-processing facilities as a high sugar content slurry would improve the yield of biofuels produced from pipelined lignocellulosic materials. After several hours of recirculating throughout the pipeline, as well as shaking in the flask, a drop in sugar concentration was detected. A microbiological analysis performed on both slurries proved the decline to be due to microbial proliferation. Accordingly, diethyl pyrocarbonate oxidizing antimicrobial agent and glutaraldehyde and bronopol non-oxidizing agents were alternatively tested to restrict microbial proliferation. These agents demonstrated reduced sugar loss and, in turn, showed an enhancement in the yield of glucose and xylose. This research aims at maximizing possible sugar release through mechanical action throughout the pipeline in the presence of antimicrobial compounds, which would increase the yield of biofuel produced from pipelined agricultural residue biomass.

  • Open Access
    Authors: 
    Ce Zhang; Ehsan Nateghinia; Luis F. Miranda-Moreno; Lijun Sun;
    Publisher: Elsevier BV
    Project: NSERC

    Abstract Pavement distresses, including cracking and disintegration, deteriorate road user’s comfort, damage vehicles, increase evasive maneuvers, and increase emissions. Transportation agencies spend a significant portion of their budget to monitor and maintain road pavements. Pavement distress can be identified through manual surveys, i.e., visual inspections of pavement images captured by an inspection vehicle. To reduce manual inspection costs, research and industry have moved quietly towards the development and implementation of automated road surface monitoring systems. Considering the latest research developments, the objective of this work is to propose and evaluate a methodology for automated detection and classification of pavement distress types using Convolutional Neural Networks (CNN) and a low-cost video data collection strategy. In this work, pavement distress types are categorized as linear or longitudinal crack, network crack, fatigue crack or pothole, patch, and pavement marking. The models are trained and tested based on an image dataset collected from Montreal’s road pavements. A sensitivity analysis is carried on for evaluating different regularization scenarios and data generation strategies especially scaling and partitioning the input image. The detection rate and classification accuracy of the proposed approach with the trained CNN model reaches 83.8% over the test set, which is promising compared with the literature. More specifically, the F1-scores for “pothole”, “patch”, “marking”, “crack-linear” and “crack-network” classes are 0.808, 0.802, 0.860, 0.796, and 0.813, respectively. However, by merging linear and network crack classes, the F1-score over the merged class increases to 0.916.

  • Open Access
    Authors: 
    Mudasser Seraj; Tony Z. Qiu;
    Publisher: Hindawi Limited
    Project: NSERC

    Weaving sections are components of highway networks that introduce a heightened likelihood for bottlenecks and collisions. Automated vehicle technology could address this as it holds considerable promise for transportation mobility and safety improvements. However, the implications of combining automated vehicles (AuVs) with traditional human-driven vehicles (HuVs) in weaving freeway sections have not been quantitatively measured. To address this gap, this paper objectively experimented with bidirectional (i.e., longitudinal and lateral) motion dynamics in a microscopic modeling framework to measure the mobility and safety implications for mixed traffic movement in a freeway weaving section. Our research begins by establishing a multilane microscopic model for studied vehicle types (i.e., AuV and HuV) from model predictive control with the provision to form a CACC platoon of AuV vehicles. The proposed modeling framework was tested first with HuV only on a two-lane weaving section and validated using standardized macroscopic parameters from the Highway Capacity Manual. This model was then applied to incrementally expand the AuV share for varying inflow rates of traffic. Simulation results showed that the maximum flow rate through the weaving section was attained at a 65% AuV share. At the same time, steadiness in the average speed of traffic was experienced with increasing AuV share. The results also revealed that a 95% AuV share could reduce potential conflicts by 94.28%. Finally, the results of simulated scenarios were consolidated and scaled to report expected mobility and safety outcomes from the prevailing traffic state and the optimal AuV share for the current inflow rate in weaving sections.

  • Authors: 
    Changqing Gong; Wenxing Zhou;
    Publisher: Informa UK Limited
    Project: NSERC

    The multi-objective optimisation technique utilising genetic algorithms is employed to develop the optimal maintenance strategy for corroding oil and gas pipelines. The objective functions of the o...

  • Closed Access
    Authors: 
    M. R. Sakr; Mohamed T. Bassuoni; A. Ghazy;
    Publisher: SAGE Publications
    Project: NSERC

    Protection of the surface layer of concrete is essential for achieving durability and functionality of concrete elements during their service life. In this paper, an effort is made to utilize colloidal nano-silica (5%–50%) and a synthesized nanocomposite as superficial treatments for concrete; silane was used as the neat resin to disperse nano-montmorillonite particles at different dosages (5% and 10%). The coatings were applied to a typical concrete mixture used for residential concrete in North America. The transport properties of the treated concrete were characterized using the rapid chloride penetrability test and the absorption/desorption percentages. Moreover, concrete was evaluated under severe durability exposure involving physical salt attack (PSA), which is a wetting/drying regime responsible for surface damage of concrete elements subjected to continuous salt supply along with cyclic ambient conditions. Deterioration was visually assessed and quantified using mass change. In addition, thermal and microscopy analyses were performed on concrete specimens to elucidate the mechanisms of enhancement by surface treatment. The results showed that increasing the concentration of nano-silica particles in the colloid led to an improved performance of concrete, with the 50% loading ratio achieving the least penetration depth, absorption/desorption percentage, and mass loss of concrete under aggravated PSA. For the silane/nano-clay composite, the low dosage of nano-clay was adequate to mitigate the damage caused by PSA on concrete.

  • Closed Access
    Authors: 
    Jerry Ding; Maryam Kamgarpour; Sean Summers; Alessandro Abate; John Lygeros; Claire J. Tomlin;
    Country: Switzerland
    Project: EC | MANTRAS (249295), NSERC , EC | MOVES (257005)

    We describe a framework for analyzing probabilistic reachability and safety problems for discrete time stochastic hybrid systems within a dynamic games setting. In particular, we consider finite horizon zero-sum stochastic games in which a control has the objective of reaching a target set while avoiding an unsafe set in the hybrid state space, and a rational adversary has the opposing objective. We derive an algorithm for computing the maximal probability of achieving the control objective, subject to the worst-case adversary behavior. From this algorithm, sufficient conditions of optimality are also derived for the synthesis of optimal control policies and worst-case disturbance strategies. These results are then specialized to the safety problem, in which the control objective is to remain within a safe set. We illustrate our modeling framework and computational approach using both a tutorial example with jump Markov dynamics and a practical application in the domain of air traffic management.

  • Closed Access
    Authors: 
    Navjot Singh; Sreekanta Das; Peter Song; Nader Yoosef-Ghodsi;
    Publisher: ASME International
    Project: NSERC

    Abstract Wrinkle defects can be complex pipeline deformities to assess and can present the potential to initiate a pipeline release incident as a result of fatigue failure due to pressure cycling, if not dealt with accordingly. Specifically, the stress distribution arising due to applied loads such as internal pressure can vary rapidly due to the complex shape along the wrinkle profile, which may introduce complexities in subsequent assessments such as fatigue life analysis. This paper presents a methodology using numerical simulation for evaluating stress concentration factors of wrinkle defects of varying geometries. A nonlinear finite element model is developed to evaluate stress concentration factors induced by wrinkle defects within steel pipelines subjected to internal pressure. Afterward, data from full-scale laboratory tests for the wrinkled pipe specimens subjected to cyclic pressure fatigue loading are analyzed to evaluate stress concentration factors for comparable wrinkle profiles. Lastly, a comparison between the results of the stress concentration factors evaluated using the finite element method and test data is provided, followed by a brief discussion of potential sources of discrepancies between results obtained from these methods.

  • Closed Access
    Authors: 
    Giovanni Di Lullo; Abayomi Olufemi Oni; Eskinder Gemechu; Amit Kumar;
    Publisher: Elsevier BV
    Project: NSERC

    Abstract As demand for natural gas increases, it is important to understand its life cycle emission intensity. A framework is developed for performing bottom-up greenhouse gas (GHG) life cycle assessment of natural gas transmission pipelines to assist project environmental assessments. Three large-scale pipelines in Canada were examined: Alliance mainline (50 million m3/d, 3000 km), Prince Rupert phase 1 (PR1, 57 million m3/d, 878 km), and Prince Rupert phase 2 (PR2, 102 million m3/d, 878 km). Fundamental engineering principles are used for calculation accuracy, with a sensitivity analysis to identify key parameters. The model boundary includes pipeline construction, operation, and decommissioning. The resulting transportation GHG emission intensities are 1.49, 0.77, and 1.78 gCO2eq/GJ.km for the Alliance, PR1, and PR2 projects, respectively. The operating phase represents 78%–95% of the overall emissions. Operating at higher pressures could reduce emission intensity by up to 49% by increasing flow efficiency. The research provides a user-friendly open-source template that can be used to examine alternative scenarios.

search
Include:
The following results are related to Canada. Are you interested to view more results? Visit OpenAIRE - Explore.
209 Research products, page 1 of 21
  • Closed Access
    Authors: 
    Ali Zirahi; Ali Haddadnia; Mohammadjavad Mohammadi; Bahareh Azinfar; Mohsen Zirrahi; Hassan Hassanzadeh; Jalal Abedi;
    Publisher: Elsevier BV
    Project: NSERC

    Abstract Environmental impact and economics of Carbon Dioxide (CO2) and bitumen transportation are among the major challenges of oil sands operations. We propose and assess a new approach to address these important challenges by using diluted bitumen (DilBit) as a carrier for large-scale CO2 transportation. The proposed approach provides a unique prospect to significantly reduce the cost of CO2 transportation from the carbon capture and storage (CCS) value chain, facilitate more efficient detection of DilBit spills from pipelines, utilize CO2, and improve public perception of both oil sands and CCS operations. These opportunities will offer the possibility of sustaining access to oil resources while reducing environmental impact and improving the economics of CCS and oil sands operations. Through experimental measurements, we have shown that 80–200 kg of CO2 per m3 of DilBit can be dissolved and transported. We also report simulation results from the simultaneous transportation of DilBit and CO2 and DilBit spill detection through monitoring concentration of leaked CO2.

  • Closed Access
    Authors: 
    Zainab Almheiri; Mohamed A. Meguid; Tarek Zayed;
    Publisher: Elsevier BV
    Project: NSERC

    Population growth and urbanization worldwide entail the need for continuous renewal plans for urban water distribution networks. Hence, understanding the long-term performance and predicting the service life of water pipelines are essential for facilitating early replacement, avoiding economic losses, and ensuring safe transportation of drinking water from treatment plants to consumers. However, developing a suitable model that can be used for cases where data are insufficient or incomplete remains challenging. Herein, a new advanced meta-learning paradigm based on deep neural networks is introduced. The developed model is used to predict the risk index of pipe failure. The effects of different factors that are considered essential for the deterioration modeling of water pipelines are first examined. The factors include seasonal climatic variation, chlorine content, traffic conditions, pipe material, and the spatial characteristics of water pipes. The results suggest that these factors contribute to estimating the likelihood of failure in water distribution pipelines. The presence of chlorine residual and the number of traffic lanes are the most critical factors, followed by road type, spatial characteristics, month index, traffic type, precipitation, temperature, number of breaks, and pipe depth. The proposed approach can accommodate limited, high-dimensional, and partially observed data and can be applied to any water distribution system.

  • Closed Access
    Authors: 
    Sonia Ghatora; Mayank Kumar; Mahdi Vaezi; Amit Kumar; David C. Bressler;
    Publisher: Elsevier BV
    Project: NSERC

    Abstract Pipeline transport of biomass is an economically viable and technically feasible approach to replace conventional truck delivery approach and make the biomass-based energy industry more competitive with fossil fuel-based plants. A 25 m long and 50 mm diameter closed-circuit pipeline facility was fabricated to experimentally investigate the mechanical and chemical feasibility of transporting agricultural residue biomass-water mixtures (slurries) through pipelines. This research used the pipeline facility to study the loss of sugars (glucose and xylose) while pipelining wheat straw-water mixtures. The release of similar sugars was also measured in shake-flask cultures under controlled conditions. The output of this research is important for bio-processing facilities as a high sugar content slurry would improve the yield of biofuels produced from pipelined lignocellulosic materials. After several hours of recirculating throughout the pipeline, as well as shaking in the flask, a drop in sugar concentration was detected. A microbiological analysis performed on both slurries proved the decline to be due to microbial proliferation. Accordingly, diethyl pyrocarbonate oxidizing antimicrobial agent and glutaraldehyde and bronopol non-oxidizing agents were alternatively tested to restrict microbial proliferation. These agents demonstrated reduced sugar loss and, in turn, showed an enhancement in the yield of glucose and xylose. This research aims at maximizing possible sugar release through mechanical action throughout the pipeline in the presence of antimicrobial compounds, which would increase the yield of biofuel produced from pipelined agricultural residue biomass.

  • Open Access
    Authors: 
    Ce Zhang; Ehsan Nateghinia; Luis F. Miranda-Moreno; Lijun Sun;
    Publisher: Elsevier BV
    Project: NSERC

    Abstract Pavement distresses, including cracking and disintegration, deteriorate road user’s comfort, damage vehicles, increase evasive maneuvers, and increase emissions. Transportation agencies spend a significant portion of their budget to monitor and maintain road pavements. Pavement distress can be identified through manual surveys, i.e., visual inspections of pavement images captured by an inspection vehicle. To reduce manual inspection costs, research and industry have moved quietly towards the development and implementation of automated road surface monitoring systems. Considering the latest research developments, the objective of this work is to propose and evaluate a methodology for automated detection and classification of pavement distress types using Convolutional Neural Networks (CNN) and a low-cost video data collection strategy. In this work, pavement distress types are categorized as linear or longitudinal crack, network crack, fatigue crack or pothole, patch, and pavement marking. The models are trained and tested based on an image dataset collected from Montreal’s road pavements. A sensitivity analysis is carried on for evaluating different regularization scenarios and data generation strategies especially scaling and partitioning the input image. The detection rate and classification accuracy of the proposed approach with the trained CNN model reaches 83.8% over the test set, which is promising compared with the literature. More specifically, the F1-scores for “pothole”, “patch”, “marking”, “crack-linear” and “crack-network” classes are 0.808, 0.802, 0.860, 0.796, and 0.813, respectively. However, by merging linear and network crack classes, the F1-score over the merged class increases to 0.916.

  • Open Access
    Authors: 
    Mudasser Seraj; Tony Z. Qiu;
    Publisher: Hindawi Limited
    Project: NSERC

    Weaving sections are components of highway networks that introduce a heightened likelihood for bottlenecks and collisions. Automated vehicle technology could address this as it holds considerable promise for transportation mobility and safety improvements. However, the implications of combining automated vehicles (AuVs) with traditional human-driven vehicles (HuVs) in weaving freeway sections have not been quantitatively measured. To address this gap, this paper objectively experimented with bidirectional (i.e., longitudinal and lateral) motion dynamics in a microscopic modeling framework to measure the mobility and safety implications for mixed traffic movement in a freeway weaving section. Our research begins by establishing a multilane microscopic model for studied vehicle types (i.e., AuV and HuV) from model predictive control with the provision to form a CACC platoon of AuV vehicles. The proposed modeling framework was tested first with HuV only on a two-lane weaving section and validated using standardized macroscopic parameters from the Highway Capacity Manual. This model was then applied to incrementally expand the AuV share for varying inflow rates of traffic. Simulation results showed that the maximum flow rate through the weaving section was attained at a 65% AuV share. At the same time, steadiness in the average speed of traffic was experienced with increasing AuV share. The results also revealed that a 95% AuV share could reduce potential conflicts by 94.28%. Finally, the results of simulated scenarios were consolidated and scaled to report expected mobility and safety outcomes from the prevailing traffic state and the optimal AuV share for the current inflow rate in weaving sections.

  • Authors: 
    Changqing Gong; Wenxing Zhou;
    Publisher: Informa UK Limited
    Project: NSERC

    The multi-objective optimisation technique utilising genetic algorithms is employed to develop the optimal maintenance strategy for corroding oil and gas pipelines. The objective functions of the o...

  • Closed Access
    Authors: 
    M. R. Sakr; Mohamed T. Bassuoni; A. Ghazy;
    Publisher: SAGE Publications
    Project: NSERC

    Protection of the surface layer of concrete is essential for achieving durability and functionality of concrete elements during their service life. In this paper, an effort is made to utilize colloidal nano-silica (5%–50%) and a synthesized nanocomposite as superficial treatments for concrete; silane was used as the neat resin to disperse nano-montmorillonite particles at different dosages (5% and 10%). The coatings were applied to a typical concrete mixture used for residential concrete in North America. The transport properties of the treated concrete were characterized using the rapid chloride penetrability test and the absorption/desorption percentages. Moreover, concrete was evaluated under severe durability exposure involving physical salt attack (PSA), which is a wetting/drying regime responsible for surface damage of concrete elements subjected to continuous salt supply along with cyclic ambient conditions. Deterioration was visually assessed and quantified using mass change. In addition, thermal and microscopy analyses were performed on concrete specimens to elucidate the mechanisms of enhancement by surface treatment. The results showed that increasing the concentration of nano-silica particles in the colloid led to an improved performance of concrete, with the 50% loading ratio achieving the least penetration depth, absorption/desorption percentage, and mass loss of concrete under aggravated PSA. For the silane/nano-clay composite, the low dosage of nano-clay was adequate to mitigate the damage caused by PSA on concrete.

  • Closed Access
    Authors: 
    Jerry Ding; Maryam Kamgarpour; Sean Summers; Alessandro Abate; John Lygeros; Claire J. Tomlin;
    Country: Switzerland
    Project: EC | MANTRAS (249295), NSERC , EC | MOVES (257005)

    We describe a framework for analyzing probabilistic reachability and safety problems for discrete time stochastic hybrid systems within a dynamic games setting. In particular, we consider finite horizon zero-sum stochastic games in which a control has the objective of reaching a target set while avoiding an unsafe set in the hybrid state space, and a rational adversary has the opposing objective. We derive an algorithm for computing the maximal probability of achieving the control objective, subject to the worst-case adversary behavior. From this algorithm, sufficient conditions of optimality are also derived for the synthesis of optimal control policies and worst-case disturbance strategies. These results are then specialized to the safety problem, in which the control objective is to remain within a safe set. We illustrate our modeling framework and computational approach using both a tutorial example with jump Markov dynamics and a practical application in the domain of air traffic management.

  • Closed Access
    Authors: 
    Navjot Singh; Sreekanta Das; Peter Song; Nader Yoosef-Ghodsi;
    Publisher: ASME International
    Project: NSERC

    Abstract Wrinkle defects can be complex pipeline deformities to assess and can present the potential to initiate a pipeline release incident as a result of fatigue failure due to pressure cycling, if not dealt with accordingly. Specifically, the stress distribution arising due to applied loads such as internal pressure can vary rapidly due to the complex shape along the wrinkle profile, which may introduce complexities in subsequent assessments such as fatigue life analysis. This paper presents a methodology using numerical simulation for evaluating stress concentration factors of wrinkle defects of varying geometries. A nonlinear finite element model is developed to evaluate stress concentration factors induced by wrinkle defects within steel pipelines subjected to internal pressure. Afterward, data from full-scale laboratory tests for the wrinkled pipe specimens subjected to cyclic pressure fatigue loading are analyzed to evaluate stress concentration factors for comparable wrinkle profiles. Lastly, a comparison between the results of the stress concentration factors evaluated using the finite element method and test data is provided, followed by a brief discussion of potential sources of discrepancies between results obtained from these methods.

  • Closed Access
    Authors: 
    Giovanni Di Lullo; Abayomi Olufemi Oni; Eskinder Gemechu; Amit Kumar;
    Publisher: Elsevier BV
    Project: NSERC

    Abstract As demand for natural gas increases, it is important to understand its life cycle emission intensity. A framework is developed for performing bottom-up greenhouse gas (GHG) life cycle assessment of natural gas transmission pipelines to assist project environmental assessments. Three large-scale pipelines in Canada were examined: Alliance mainline (50 million m3/d, 3000 km), Prince Rupert phase 1 (PR1, 57 million m3/d, 878 km), and Prince Rupert phase 2 (PR2, 102 million m3/d, 878 km). Fundamental engineering principles are used for calculation accuracy, with a sensitivity analysis to identify key parameters. The model boundary includes pipeline construction, operation, and decommissioning. The resulting transportation GHG emission intensities are 1.49, 0.77, and 1.78 gCO2eq/GJ.km for the Alliance, PR1, and PR2 projects, respectively. The operating phase represents 78%–95% of the overall emissions. Operating at higher pressures could reduce emission intensity by up to 49% by increasing flow efficiency. The research provides a user-friendly open-source template that can be used to examine alternative scenarios.