Philipp Grimmeisen, Artur Karimov, M. Diaconeasa, A. Morozov
Probabilistic Risk Assessment (PRA) is an indispensable technology to evaluate the risk, dependability, and resilience characteristics of safety-critical systems. Therefore, PRA uses widely adopted methods, such as classical event trees, fault trees, Markov chains, Bayesian networks, and their numerous combinations. To analyze challenging failure scenarios of modern, intelligent, autonomous, and highly dynamic Cyber-Physical Systems (CPS), the integration of multiple PRA methods is needed. This paper presents a PRA approach based on classical Event Tree Analysis (ETA) and Fault Tree Analysis (FTA) and provides the technical description of a new open-source software platform called OpenPRA. Besides, this paper describes a representative case study from the autonomous system domain, focusing on autonomous warehouse robots.
{"title":"Demonstration of a Limited Scope Probabilistic Risk Assessment for Autonomous Warehouse Robots With OpenPRA","authors":"Philipp Grimmeisen, Artur Karimov, M. Diaconeasa, A. Morozov","doi":"10.1115/imece2021-69998","DOIUrl":"https://doi.org/10.1115/imece2021-69998","url":null,"abstract":"\u0000 Probabilistic Risk Assessment (PRA) is an indispensable technology to evaluate the risk, dependability, and resilience characteristics of safety-critical systems. Therefore, PRA uses widely adopted methods, such as classical event trees, fault trees, Markov chains, Bayesian networks, and their numerous combinations. To analyze challenging failure scenarios of modern, intelligent, autonomous, and highly dynamic Cyber-Physical Systems (CPS), the integration of multiple PRA methods is needed. This paper presents a PRA approach based on classical Event Tree Analysis (ETA) and Fault Tree Analysis (FTA) and provides the technical description of a new open-source software platform called OpenPRA. Besides, this paper describes a representative case study from the autonomous system domain, focusing on autonomous warehouse robots.","PeriodicalId":146533,"journal":{"name":"Volume 13: Safety Engineering, Risk, and Reliability Analysis; Research Posters","volume":"518 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123107855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Roshan Sharma, Chiara Silvestri Dobrovolny, S. Hurlebaus, Maysam Kiani
The design of longitudinal barriers using reinforced concrete is typical in roadside safety design. Roadside safety hardware such as bridge rails, median barriers, and transitions are designed to safely contain and redirect impacting vehicles without imposing any significant risks to the occupants. As full-scale crash tests of new designs are expensive and time-consuming, finite element modeling and simulation of the impact event is often involved. In LS-DYNA, one of the most popular software in roadside design, there are multiple material models for concrete modeling and there is no specific guideline on the selection of the concrete material model. This paper evaluates the behavior of material models MAT_CSCM_CONCRETE and MAT_RHT during the study of truck platoon implications. The concrete erosion, deflection, and failure mechanism of two consecutive tractor-van trailer impacts into the barrier FEA models were analyzed to select a representative material model for further study.
{"title":"Reinforced Concrete Barrier Modeling In-Series Impacts in LS-DYNA","authors":"Roshan Sharma, Chiara Silvestri Dobrovolny, S. Hurlebaus, Maysam Kiani","doi":"10.1115/imece2021-66627","DOIUrl":"https://doi.org/10.1115/imece2021-66627","url":null,"abstract":"\u0000 The design of longitudinal barriers using reinforced concrete is typical in roadside safety design. Roadside safety hardware such as bridge rails, median barriers, and transitions are designed to safely contain and redirect impacting vehicles without imposing any significant risks to the occupants. As full-scale crash tests of new designs are expensive and time-consuming, finite element modeling and simulation of the impact event is often involved. In LS-DYNA, one of the most popular software in roadside design, there are multiple material models for concrete modeling and there is no specific guideline on the selection of the concrete material model. This paper evaluates the behavior of material models MAT_CSCM_CONCRETE and MAT_RHT during the study of truck platoon implications. The concrete erosion, deflection, and failure mechanism of two consecutive tractor-van trailer impacts into the barrier FEA models were analyzed to select a representative material model for further study.","PeriodicalId":146533,"journal":{"name":"Volume 13: Safety Engineering, Risk, and Reliability Analysis; Research Posters","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125892272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Gregory A. Langone, B. Davis, Nicholas A. Reisweber
Analytical impact models for steel penetration, such as the Alekseevskii-Tate and Lambert-Zukas models, are a combination of physics principles and empirically derived constants fit by trial data to represent a specific experimental condition. These models are very useful to predict material performance under single impact conditions of a non-deforming or hydrodynamic projectile given suitable experimental test data but were not developed to account for the effects associated with repeated impact loading. The uncertainty in multiple impact events comes from variability in the impact location, effected area after impact, inertia induced fracture, material response to heating, and many other factors. Because of the meaningful uncertainty in multiple impact modeling, it is useful to apply Bayesian updating to formally combine the predictive capacity of an impact model with limited available test data to improve the model’s accuracy for a specific application and better quantify the uncertainty in the estimates. In this report, existing experimental data for impacts of 0.223 caliber ammunition against AR500 steel panels with 2-inch ballistic rubber is used for Bayesian updating. The existing data from the U.S. Army Aberdeen Test Center was gathered by shooting a steel plate while cycling through sixteen independent locations until one location is perforated. The total number of shots delivered to the plate was recorded as the number of shots to failure. Because sixteen independent plate locations were fired on, however, there is useful data from both locations where failure was not reached and those that were perforated. After creating the prior distribution of plate failure for a range of total impacts test data from all 48 locations is incorporated using Bayes’ Theorem to create a posterior distribution which represents an updated model for plate failure. The posterior density of plate failure strength — measured in number of shots at the failure location — can then be used as one parameter in a model to determine the safe allowable total number of impacts on the target of interest. This future model must also consider parameters such as the distribution of shots across the plate and the area affected by each impact while making assumptions about the practical variability in impact velocity and obliquity. A model of this type will inform decision makers to develop safe inspection criteria and utilize a safe number of impacts in training for current and future ammunition.
{"title":"Application of Bayesian Calibration to Improve Multiple Ballistic Impact Modeling","authors":"Gregory A. Langone, B. Davis, Nicholas A. Reisweber","doi":"10.1115/imece2021-70716","DOIUrl":"https://doi.org/10.1115/imece2021-70716","url":null,"abstract":"\u0000 Analytical impact models for steel penetration, such as the Alekseevskii-Tate and Lambert-Zukas models, are a combination of physics principles and empirically derived constants fit by trial data to represent a specific experimental condition. These models are very useful to predict material performance under single impact conditions of a non-deforming or hydrodynamic projectile given suitable experimental test data but were not developed to account for the effects associated with repeated impact loading. The uncertainty in multiple impact events comes from variability in the impact location, effected area after impact, inertia induced fracture, material response to heating, and many other factors. Because of the meaningful uncertainty in multiple impact modeling, it is useful to apply Bayesian updating to formally combine the predictive capacity of an impact model with limited available test data to improve the model’s accuracy for a specific application and better quantify the uncertainty in the estimates. In this report, existing experimental data for impacts of 0.223 caliber ammunition against AR500 steel panels with 2-inch ballistic rubber is used for Bayesian updating. The existing data from the U.S. Army Aberdeen Test Center was gathered by shooting a steel plate while cycling through sixteen independent locations until one location is perforated. The total number of shots delivered to the plate was recorded as the number of shots to failure. Because sixteen independent plate locations were fired on, however, there is useful data from both locations where failure was not reached and those that were perforated. After creating the prior distribution of plate failure for a range of total impacts test data from all 48 locations is incorporated using Bayes’ Theorem to create a posterior distribution which represents an updated model for plate failure. The posterior density of plate failure strength — measured in number of shots at the failure location — can then be used as one parameter in a model to determine the safe allowable total number of impacts on the target of interest. This future model must also consider parameters such as the distribution of shots across the plate and the area affected by each impact while making assumptions about the practical variability in impact velocity and obliquity. A model of this type will inform decision makers to develop safe inspection criteria and utilize a safe number of impacts in training for current and future ammunition.","PeriodicalId":146533,"journal":{"name":"Volume 13: Safety Engineering, Risk, and Reliability Analysis; Research Posters","volume":"411 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123557248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We have witnessed remarkable technology advancements and competitions in autonomous and connected vehicles. There has been a vigorous technological development effort in the past few years to introduce self-driving smart vehicles for a connected world. If applied correctly, these technologies can enable solutions to help city transportation systems improve the economics of transportation, environmental concerns, and quality of life for everyone. The problem requires a holistic approach. While technological development has been intriguing, competitive, and vigorous, the industry’s advances on the issues of safety, risk, and reliability have been dismal. Despite its limited uses and manufacturers’ stated goal of making autonomous cars demonstrably safer than an average human-controlled car, several accidents and near-misses have already occurred. The mean distance driven to an unsafe condition, near miss or accident has been far shorter than the conventional road vehicles. While the public at large is intrigued about these technologies, the safety concerns are profound. This article is aimed to review the safety of AV systems from design and manufacturing, society and ethics, advancement of the safety/reliability technologies assessing their readiness and a review from academic point of view to understand the area for further research.
{"title":"Safety Technology Advancements for Autonomous Cars; Prospective of Manufacturing, Regulatory and Society","authors":"Mohammad Pourgol Mohamad, Amin Pourgol Mohamad","doi":"10.1115/imece2021-70802","DOIUrl":"https://doi.org/10.1115/imece2021-70802","url":null,"abstract":"\u0000 We have witnessed remarkable technology advancements and competitions in autonomous and connected vehicles. There has been a vigorous technological development effort in the past few years to introduce self-driving smart vehicles for a connected world. If applied correctly, these technologies can enable solutions to help city transportation systems improve the economics of transportation, environmental concerns, and quality of life for everyone. The problem requires a holistic approach. While technological development has been intriguing, competitive, and vigorous, the industry’s advances on the issues of safety, risk, and reliability have been dismal. Despite its limited uses and manufacturers’ stated goal of making autonomous cars demonstrably safer than an average human-controlled car, several accidents and near-misses have already occurred. The mean distance driven to an unsafe condition, near miss or accident has been far shorter than the conventional road vehicles. While the public at large is intrigued about these technologies, the safety concerns are profound. This article is aimed to review the safety of AV systems from design and manufacturing, society and ethics, advancement of the safety/reliability technologies assessing their readiness and a review from academic point of view to understand the area for further research.","PeriodicalId":146533,"journal":{"name":"Volume 13: Safety Engineering, Risk, and Reliability Analysis; Research Posters","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130465822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lincan Yan, D. Yantek, C. DeGennaro, Rohan D. Fernando
Federal regulations require refuge alternatives (RAs) in underground coal mines to provide a life-sustaining environment for miners trapped underground when escape is impossible. A breathable air supply is among those requirements. For built-in-place (BIP) RAs, a borehole air supply (BAS) is commonly used to supply fresh air from the surface. It is assumed that the fresh air has an oxygen concentration of 20.9%. Federal regulations require that such a BAS must supply fresh air at 12.5 cfm or more per person to maintain the oxygen concentration between 18.5% to 23% and carbon dioxide level below the 1% limit specified. However, it is unclear whether 12.5 cfm is indeed needed to maintain this carbon dioxide level. The minimal fresh air flow (FAF) rate needed to maintain the 1% CO2 level will depend on multiple factors, including the number of people and the volume of the BIP RA. In the past, to predict the interior CO2 concentration in an occupied RA, 96-hour tests were performed using a physical human breathing simulator. However, given the infinite possibility of the combinations (number of people, size of the BIP RA), it would be impractical to fully investigate the range of parameters that can affect the CO2 concentration using physical tests. In this paper, researchers at the National Institute for Occupational Safety and Health (NIOSH) developed a model that can predict how the %CO2 in an occupied confined space changes with time given the number of occupants and the fresh air flow (FAF) rate. The model was then compared to and validated with test data. The benchmarked model can be used to predict the %CO2 for any number of people and FAF rate without conducting a 96-hour test. The methodology used in this model can also be used to estimate other gas levels within a confined space.
{"title":"Mathematical Modeling for Carbon Dioxide Level Within Confined Spaces","authors":"Lincan Yan, D. Yantek, C. DeGennaro, Rohan D. Fernando","doi":"10.1115/imece2021-68452","DOIUrl":"https://doi.org/10.1115/imece2021-68452","url":null,"abstract":"\u0000 Federal regulations require refuge alternatives (RAs) in underground coal mines to provide a life-sustaining environment for miners trapped underground when escape is impossible. A breathable air supply is among those requirements. For built-in-place (BIP) RAs, a borehole air supply (BAS) is commonly used to supply fresh air from the surface. It is assumed that the fresh air has an oxygen concentration of 20.9%. Federal regulations require that such a BAS must supply fresh air at 12.5 cfm or more per person to maintain the oxygen concentration between 18.5% to 23% and carbon dioxide level below the 1% limit specified. However, it is unclear whether 12.5 cfm is indeed needed to maintain this carbon dioxide level. The minimal fresh air flow (FAF) rate needed to maintain the 1% CO2 level will depend on multiple factors, including the number of people and the volume of the BIP RA. In the past, to predict the interior CO2 concentration in an occupied RA, 96-hour tests were performed using a physical human breathing simulator. However, given the infinite possibility of the combinations (number of people, size of the BIP RA), it would be impractical to fully investigate the range of parameters that can affect the CO2 concentration using physical tests.\u0000 In this paper, researchers at the National Institute for Occupational Safety and Health (NIOSH) developed a model that can predict how the %CO2 in an occupied confined space changes with time given the number of occupants and the fresh air flow (FAF) rate. The model was then compared to and validated with test data. The benchmarked model can be used to predict the %CO2 for any number of people and FAF rate without conducting a 96-hour test. The methodology used in this model can also be used to estimate other gas levels within a confined space.","PeriodicalId":146533,"journal":{"name":"Volume 13: Safety Engineering, Risk, and Reliability Analysis; Research Posters","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131062330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we tested and compared the failure loading conditions for allowed pressure over prototypes of smart toilet seats with custom shapes and materials (Polypropylene homopolymer (PPH) & Polymethyl methacrylate (PMMA)). These tests were conducted to identify the allowable maximum loading condition on these prototypes. The main challenge in designing these tests, was the application of load, specific to the custom shape of the prototype seats. A custom loading platform was designed to facilitate the application of a distributed force, to find the maximum allowable weight to identify any weak/critical sections, and failing point/s for each of the designs. However, because of the deflection of the seats, which causes a nonlinear condition for compression analysis, combined with their complex shapes, simulation of the models in SolidWorks was not very accurate. To circumvent this shortcoming an actual test bed was built and used a high accuracy electromechanical tensile & compression testing machine to subject and compare the maximum allowable loading of the seats. The results showed the seats passed the compression tests but have some differences in the locations for failures and maximum allowable pressure.
{"title":"Compression Analysis Tests for Prototypes Made of Different Polymers","authors":"Taher Deemyad, Vincent Akula, Anish Sebastian","doi":"10.1115/imece2021-68096","DOIUrl":"https://doi.org/10.1115/imece2021-68096","url":null,"abstract":"\u0000 In this paper, we tested and compared the failure loading conditions for allowed pressure over prototypes of smart toilet seats with custom shapes and materials (Polypropylene homopolymer (PPH) & Polymethyl methacrylate (PMMA)). These tests were conducted to identify the allowable maximum loading condition on these prototypes. The main challenge in designing these tests, was the application of load, specific to the custom shape of the prototype seats. A custom loading platform was designed to facilitate the application of a distributed force, to find the maximum allowable weight to identify any weak/critical sections, and failing point/s for each of the designs. However, because of the deflection of the seats, which causes a nonlinear condition for compression analysis, combined with their complex shapes, simulation of the models in SolidWorks was not very accurate. To circumvent this shortcoming an actual test bed was built and used a high accuracy electromechanical tensile & compression testing machine to subject and compare the maximum allowable loading of the seats. The results showed the seats passed the compression tests but have some differences in the locations for failures and maximum allowable pressure.","PeriodicalId":146533,"journal":{"name":"Volume 13: Safety Engineering, Risk, and Reliability Analysis; Research Posters","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127659323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Melinda Stevens, Samuel Arellano, Diego Rodriguez, James Wilson, Zady Gutierrez, Noah Trudell, Hamed Momeni, A. Ebrahimkhanlou
Surface cracks in concrete structures are often indicators of more substantial damage and may negatively affect the durability of a structure. To ensure the soundness of these structures, surface cracks should be quickly detected; this project proposes a robot with the ability to detect, map, and fill surface cracks. The robot will use a Bayesian network to fuse the multi-sensor data provided via an RGB camera, a stereo infrared depth sensor, and a LIDAR sensor. It will also be fitted with a newly designed piston-driven syringe system to inject a concrete filler material in a controlled manner. A non-captive lead screw and stepper motor drive the piston along with the syringe, and an arm with two degrees of freedom will allow the robot to position the injector along a crack accurately. To control the arm, the Bayesian network and sensor systems will work in unison to determine when a crack has been filled in a satisfying manner, ensuring a degree of uniformity and consistency in the repaired concrete surface.
{"title":"Robotic-Based Repair of Concrete Structures: A Surface Crack Filler Robot","authors":"Melinda Stevens, Samuel Arellano, Diego Rodriguez, James Wilson, Zady Gutierrez, Noah Trudell, Hamed Momeni, A. Ebrahimkhanlou","doi":"10.1115/imece2021-72082","DOIUrl":"https://doi.org/10.1115/imece2021-72082","url":null,"abstract":"\u0000 Surface cracks in concrete structures are often indicators of more substantial damage and may negatively affect the durability of a structure. To ensure the soundness of these structures, surface cracks should be quickly detected; this project proposes a robot with the ability to detect, map, and fill surface cracks. The robot will use a Bayesian network to fuse the multi-sensor data provided via an RGB camera, a stereo infrared depth sensor, and a LIDAR sensor. It will also be fitted with a newly designed piston-driven syringe system to inject a concrete filler material in a controlled manner. A non-captive lead screw and stepper motor drive the piston along with the syringe, and an arm with two degrees of freedom will allow the robot to position the injector along a crack accurately. To control the arm, the Bayesian network and sensor systems will work in unison to determine when a crack has been filled in a satisfying manner, ensuring a degree of uniformity and consistency in the repaired concrete surface.","PeriodicalId":146533,"journal":{"name":"Volume 13: Safety Engineering, Risk, and Reliability Analysis; Research Posters","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133786103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Haoyee Yeong, Francis Iloeje, Eli Kindomba, Sunday Folorunso, Yafeng Li, Jing Zhang
In this work, we use a group project approach for a group of undergraduate students to design and develop a mechanical ventilator, in response to the COVID-19 pandemic. A student group project composed of a team of undergraduate students has successfully designed and fabricated a mechanical bag valve mask (BVM) ventilator prototype. It is lightweight with a single controller is driven, capable of volume adjustment, inexpensive, open-source, and designed for ease of fabrication, installation, and operation by the average user. The ventilator prototype also consists of 3D printed components and stored bought hardware. A finite element model was developed to analyze the deformation of the bag valve mask. Finally, the ventilator system is fully tested functioning properly.
{"title":"Design, Modeling, and Fabrication of a Ventilator Prototype - A Successful Student Project Story","authors":"Haoyee Yeong, Francis Iloeje, Eli Kindomba, Sunday Folorunso, Yafeng Li, Jing Zhang","doi":"10.1115/imece2021-72492","DOIUrl":"https://doi.org/10.1115/imece2021-72492","url":null,"abstract":"\u0000 In this work, we use a group project approach for a group of undergraduate students to design and develop a mechanical ventilator, in response to the COVID-19 pandemic. A student group project composed of a team of undergraduate students has successfully designed and fabricated a mechanical bag valve mask (BVM) ventilator prototype. It is lightweight with a single controller is driven, capable of volume adjustment, inexpensive, open-source, and designed for ease of fabrication, installation, and operation by the average user. The ventilator prototype also consists of 3D printed components and stored bought hardware. A finite element model was developed to analyze the deformation of the bag valve mask. Finally, the ventilator system is fully tested functioning properly.","PeriodicalId":146533,"journal":{"name":"Volume 13: Safety Engineering, Risk, and Reliability Analysis; Research Posters","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114862261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Railway transport is considered one of the most reliable, comfortable and safest modes of travel for both freight and passengers. Rail infrastructure assets (such as tracks, bridges, earthworks, tunnels and drainage systems) must be inspected and maintained on a regular basis in order to ensure that transport services are delivered in compliance with contractual and legal obligations. The maintenance of railway track structures is preventive in nature and includes the repair or replacement of certain components at pre-determined time intervals (in terms of years of operation) and/or usage rates (in terms of gross tonnage). Maintenance actions such as grinding and stone-blowing either restore the track profile to its original condition, i.e., “as good as new (AGAN)”, leave the track in almost the same condition as it was in prior to the inspection, i.e., “as bad as old (ABAO)”, or restore the track condition to a state somewhere between AGAN and ABAO, i.e., the so-called imperfect maintenance. The effect of an imperfect maintenance is often characterized by two classes of models, namely, failure-intensity reduction and age-reduction. However, the impact of imperfect repair on assets’ usage has not yet been addressed in the literature. In this paper, a usage-based imperfect preventive maintenance (PM) planning model is proposed for railway track superstructures, where the effect of an imperfect maintenance is described by a reduced amount of total accumulated million gross tons (MGT) passed over the rail line. A constrained nonlinear programming model is formulated to optimize the maintenance interval (i.e., usage rate between consecutive PMs) and the degree (quality) of repair actions. The total mean maintenance cost for a Weibull failure distribution model is derived and, then, the conditions required to make PM actions beneficial are discussed. A numerical case example is provided to show the effectiveness of the proposed PM planning method over the track renewal and replacement policy.
{"title":"An Imperfect Usage-Based Preventive Maintenance Planning Model for Railway Track Superstructures","authors":"F. Dinmohammadi, M. Shafiee, E. Zio","doi":"10.1115/imece2021-72955","DOIUrl":"https://doi.org/10.1115/imece2021-72955","url":null,"abstract":"\u0000 Railway transport is considered one of the most reliable, comfortable and safest modes of travel for both freight and passengers. Rail infrastructure assets (such as tracks, bridges, earthworks, tunnels and drainage systems) must be inspected and maintained on a regular basis in order to ensure that transport services are delivered in compliance with contractual and legal obligations. The maintenance of railway track structures is preventive in nature and includes the repair or replacement of certain components at pre-determined time intervals (in terms of years of operation) and/or usage rates (in terms of gross tonnage). Maintenance actions such as grinding and stone-blowing either restore the track profile to its original condition, i.e., “as good as new (AGAN)”, leave the track in almost the same condition as it was in prior to the inspection, i.e., “as bad as old (ABAO)”, or restore the track condition to a state somewhere between AGAN and ABAO, i.e., the so-called imperfect maintenance. The effect of an imperfect maintenance is often characterized by two classes of models, namely, failure-intensity reduction and age-reduction. However, the impact of imperfect repair on assets’ usage has not yet been addressed in the literature. In this paper, a usage-based imperfect preventive maintenance (PM) planning model is proposed for railway track superstructures, where the effect of an imperfect maintenance is described by a reduced amount of total accumulated million gross tons (MGT) passed over the rail line. A constrained nonlinear programming model is formulated to optimize the maintenance interval (i.e., usage rate between consecutive PMs) and the degree (quality) of repair actions. The total mean maintenance cost for a Weibull failure distribution model is derived and, then, the conditions required to make PM actions beneficial are discussed. A numerical case example is provided to show the effectiveness of the proposed PM planning method over the track renewal and replacement policy.","PeriodicalId":146533,"journal":{"name":"Volume 13: Safety Engineering, Risk, and Reliability Analysis; Research Posters","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114551691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents a novel probabilistic optimization approach to identify damage characteristics by using the frequency response function (FRF). The proposed approach has been developed to predict the probability of damage existence and to further identify salient details about damage location and damage severity in a probabilistic manner. The optimization problem has been developed as a function of measured and simulated frequency responses and is formulated in a multi-stage sequence to detect the probability of damage parameters including crack depth and crack location while minimizing uncertainties in the analysis outcomes. To demonstrate the proposed approach, a simply supported beam has been modeled with an open edge crack and characterized by using Linear Elastic Fracture Mechanics (LEFM). Several frequency responses obtained from the structure have been incorporated with different levels of noise to evaluate the robustness of the proposed algorithm. The algorithm has been tested through multiple simulations with various damage characteristics and different levels of noise. In all cases, the proposed algorithm has successfully predicted the presence of damage with a relatively high probability. Evaluation of the results demonstrates that the probabilistic optimization approach provides significant advantages over conventional deterministic methods for damage detection in structural health monitoring.
{"title":"Probabilistic Optimization Approach for Damage Identification Using Frequency Response","authors":"H. Altammar, S. Kaul, A. Dhingra","doi":"10.1115/imece2021-69162","DOIUrl":"https://doi.org/10.1115/imece2021-69162","url":null,"abstract":"\u0000 This paper presents a novel probabilistic optimization approach to identify damage characteristics by using the frequency response function (FRF). The proposed approach has been developed to predict the probability of damage existence and to further identify salient details about damage location and damage severity in a probabilistic manner. The optimization problem has been developed as a function of measured and simulated frequency responses and is formulated in a multi-stage sequence to detect the probability of damage parameters including crack depth and crack location while minimizing uncertainties in the analysis outcomes. To demonstrate the proposed approach, a simply supported beam has been modeled with an open edge crack and characterized by using Linear Elastic Fracture Mechanics (LEFM). Several frequency responses obtained from the structure have been incorporated with different levels of noise to evaluate the robustness of the proposed algorithm. The algorithm has been tested through multiple simulations with various damage characteristics and different levels of noise. In all cases, the proposed algorithm has successfully predicted the presence of damage with a relatively high probability. Evaluation of the results demonstrates that the probabilistic optimization approach provides significant advantages over conventional deterministic methods for damage detection in structural health monitoring.","PeriodicalId":146533,"journal":{"name":"Volume 13: Safety Engineering, Risk, and Reliability Analysis; Research Posters","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128304108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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