Deterministic design and a priori parameters are used in traditional optimization approaches. The material characteristics of solid wood are not deterministic in reality. Hence, realistic optimization and simulation methods need to take the uncertainties of parameters into account. The uncertainty characteristics of wood are mainly originated in natural variation. In addition to this, incertitudes from lack of knowledge are inherent. Accordingly, the aleatoric approach of randomness can be expanded to a polymorphic uncertainty model. Fuzzy probability-based randomness is used in this work. Therefore, the epistemic approach of fuzziness is taken into account. The distribution functions of random variables are parametrized by fuzzy variables. So coupling of both, aleatoric and epistemic uncertainties, is involved. Interactions of fuzzy variables and crosscorrelations of random variables are considered among and within the parameters. Crosscorrelated random fields are used to represent spatial variation of material parameters. The autocovariance structures are modeled structurally dependent on the tree trunk axes. Finite element method is applied as deterministic basic solution of a loaded timber structure. A local orthotropic material formulation with respect to specifically located tree trunk axes is used. The optimal positions of the tree trunk axes for each wooden log are examined as design parameters. Polymorphic uncertainty is used to describe a priori parameters. The developed methods for uncertainty analysis are embedded in an automated and parallelized optimization processing. An analysis of a two-tier glulam beam, according to a purlin of a timber roof construction, is shown as numerical example for the optimization framework.
{"title":"Multi-Objective Optimization of Tree Trunk Axes in Glulam Beam Design Considering Fuzzy Probability-Based Random Fields","authors":"F. N. Schietzold, W. Graf, M. Kaliske","doi":"10.1115/1.4050370","DOIUrl":"https://doi.org/10.1115/1.4050370","url":null,"abstract":"\u0000 Deterministic design and a priori parameters are used in traditional optimization approaches. The material characteristics of solid wood are not deterministic in reality. Hence, realistic optimization and simulation methods need to take the uncertainties of parameters into account. The uncertainty characteristics of wood are mainly originated in natural variation. In addition to this, incertitudes from lack of knowledge are inherent. Accordingly, the aleatoric approach of randomness can be expanded to a polymorphic uncertainty model. Fuzzy probability-based randomness is used in this work. Therefore, the epistemic approach of fuzziness is taken into account. The distribution functions of random variables are parametrized by fuzzy variables. So coupling of both, aleatoric and epistemic uncertainties, is involved. Interactions of fuzzy variables and crosscorrelations of random variables are considered among and within the parameters. Crosscorrelated random fields are used to represent spatial variation of material parameters. The autocovariance structures are modeled structurally dependent on the tree trunk axes. Finite element method is applied as deterministic basic solution of a loaded timber structure. A local orthotropic material formulation with respect to specifically located tree trunk axes is used. The optimal positions of the tree trunk axes for each wooden log are examined as design parameters. Polymorphic uncertainty is used to describe a priori parameters. The developed methods for uncertainty analysis are embedded in an automated and parallelized optimization processing. An analysis of a two-tier glulam beam, according to a purlin of a timber roof construction, is shown as numerical example for the optimization framework.","PeriodicalId":44694,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B-Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86726821","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}
Spencer T Hallowell, S. Arwade, C. Qiao, A. Myers, W. Pang
As offshore wind development is in its infancy along the U.S. Atlantic Coast challenges arise due to the effects of strong storms such as hurricanes. Breaking waves on offshore structures induced by hurricanes are of particular concern to offshore structures due to high magnitude impulse loads caused by wave slamming. Prediction of breaking wave hazards is important in offshore design for load cases using long mean return periods of environmental conditions. A breaking wave hazard estimation model (BWHEM) is introduced that provides a means for assessing breaking hazard at long mean return periods over a large domain along the U.S. Atlantic Coast. The BWHEM combines commonly used breaking criteria with the Inverse First Order Method of producing environmental contours and is applied in a numerical study using a catalog of stochastic hurricanes. The result of the study shows that breaking wave hazard estimation is highly sensitive to the breaking criteria chosen. Criteria including wave steepness and seafloor slope were found to predict breaking conditions at shorter return periods than criteria with only wave height and water depth taken into consideration. Breaking hazard was found to be most important for locations closer to the coast, where breaking was predicted to occur at lower mean return periods than locations further offshore.
{"title":"Breaking Wave Hazard Estimation Model for the U.S. Atlantic Coast","authors":"Spencer T Hallowell, S. Arwade, C. Qiao, A. Myers, W. Pang","doi":"10.1115/1.4051161","DOIUrl":"https://doi.org/10.1115/1.4051161","url":null,"abstract":"\u0000 As offshore wind development is in its infancy along the U.S. Atlantic Coast challenges arise due to the effects of strong storms such as hurricanes. Breaking waves on offshore structures induced by hurricanes are of particular concern to offshore structures due to high magnitude impulse loads caused by wave slamming. Prediction of breaking wave hazards is important in offshore design for load cases using long mean return periods of environmental conditions. A breaking wave hazard estimation model (BWHEM) is introduced that provides a means for assessing breaking hazard at long mean return periods over a large domain along the U.S. Atlantic Coast. The BWHEM combines commonly used breaking criteria with the Inverse First Order Method of producing environmental contours and is applied in a numerical study using a catalog of stochastic hurricanes. The result of the study shows that breaking wave hazard estimation is highly sensitive to the breaking criteria chosen. Criteria including wave steepness and seafloor slope were found to predict breaking conditions at shorter return periods than criteria with only wave height and water depth taken into consideration. Breaking hazard was found to be most important for locations closer to the coast, where breaking was predicted to occur at lower mean return periods than locations further offshore.","PeriodicalId":44694,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B-Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2021-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79627774","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}
F. Judge, E. Lyden, M. O’Shea, B. Flannery, Jimmy Murphy
This research presents a methodology for carrying out uncertainty analysis on measurements made during wave basin testing of an oscillating water column wave energy converter. Values are determined for type A and type B uncertainty for each parameter of interest, and uncertainty is propagated using the Monte Carlo method to obtain an overall expanded uncertainty with a 95% confidence level associated with the capture width ratio of the device. An investigation into the impact of reflections on the experimental results reveals the importance of identifying the incident and combined wave field at each measurement location used to determine device performance, in order to avoid misleading results.
{"title":"Uncertainty in Wave Basin Testing of a Fixed Oscillating Water Column Wave Energy Converter","authors":"F. Judge, E. Lyden, M. O’Shea, B. Flannery, Jimmy Murphy","doi":"10.1115/1.4051164","DOIUrl":"https://doi.org/10.1115/1.4051164","url":null,"abstract":"\u0000 This research presents a methodology for carrying out uncertainty analysis on measurements made during wave basin testing of an oscillating water column wave energy converter. Values are determined for type A and type B uncertainty for each parameter of interest, and uncertainty is propagated using the Monte Carlo method to obtain an overall expanded uncertainty with a 95% confidence level associated with the capture width ratio of the device. An investigation into the impact of reflections on the experimental results reveals the importance of identifying the incident and combined wave field at each measurement location used to determine device performance, in order to avoid misleading results.","PeriodicalId":44694,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B-Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2021-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82682315","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}
Abraham Nispel, S. Ekwaro-Osire, J. Dias, A. Cunha
This study aims to address the question: can the structural reliability of an offshore wind turbine (OWT) under fatigue loading conditions be predicted more consistently? To respond to that question this study addresses the following specific aims: (1) to obtain a systematic approach that takes into consideration the amount of information available for the uncertainty modeling of the model input parameters and (2) to determine the impact of the most sensitive input parameters on the structural reliability of the OWT through a surrogate model. First, a coupled model to determine the fatigue life of the support structure considering the soil-structure interaction under 15 different loading conditions was developed. Second, a sensitivity scheme using two global analyses was developed to consistently establish the most and least important input parameters of the model. Third, systematic uncertainty quantification (UQ) scheme was employed to model the uncertainties of model input parameters based on their available—data-driven and physics-informed—information. Finally, the impact of the proposed UQ framework on the OWT structural reliability was evaluated through the estimation of the probability of failure of the structure based on the fatigue limit state design criterion. The results show high sensitivity for the wind speed and moderate sensitivity for parameters usually considered as deterministic values in design standards. Additionally, it is shown that applying systematic UQ not only produces a more efficient and better approximation of the fatigue life under uncertainty, but also a more accurate estimation of the structural reliability of offshore wind turbine's structure during conceptual design. Consequently, more reliable, and robust estimations of the structural designs for large offshore wind turbines with limited information may be achieved during the early stages of design.
{"title":"Uncertainty Quantification for Fatigue Life of Offshore Wind Turbine Structure","authors":"Abraham Nispel, S. Ekwaro-Osire, J. Dias, A. Cunha","doi":"10.1115/1.4051162","DOIUrl":"https://doi.org/10.1115/1.4051162","url":null,"abstract":"\u0000 This study aims to address the question: can the structural reliability of an offshore wind turbine (OWT) under fatigue loading conditions be predicted more consistently? To respond to that question this study addresses the following specific aims: (1) to obtain a systematic approach that takes into consideration the amount of information available for the uncertainty modeling of the model input parameters and (2) to determine the impact of the most sensitive input parameters on the structural reliability of the OWT through a surrogate model. First, a coupled model to determine the fatigue life of the support structure considering the soil-structure interaction under 15 different loading conditions was developed. Second, a sensitivity scheme using two global analyses was developed to consistently establish the most and least important input parameters of the model. Third, systematic uncertainty quantification (UQ) scheme was employed to model the uncertainties of model input parameters based on their available—data-driven and physics-informed—information. Finally, the impact of the proposed UQ framework on the OWT structural reliability was evaluated through the estimation of the probability of failure of the structure based on the fatigue limit state design criterion. The results show high sensitivity for the wind speed and moderate sensitivity for parameters usually considered as deterministic values in design standards. Additionally, it is shown that applying systematic UQ not only produces a more efficient and better approximation of the fatigue life under uncertainty, but also a more accurate estimation of the structural reliability of offshore wind turbine's structure during conceptual design. Consequently, more reliable, and robust estimations of the structural designs for large offshore wind turbines with limited information may be achieved during the early stages of design.","PeriodicalId":44694,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B-Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2021-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84758053","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}
M. Devin, Bryony DuPont, Spencer T Hallowell, S. Arwade
Commercial floating offshore wind projects are expected to emerge in the U.S. by the end of this decade. Currently, however, high costs for the technology limit its commercial viability, and a lack of data regarding system reliability heightens project risk. This work presents an optimization algorithm to examine the tradeoffs between cost and reliability for a floating offshore wind array that uses shared anchoring. Combining a multivariable genetic algorithm with elements of Bayesian optimization, the optimization algorithm selectively increases anchor strengths to minimize the added costs of failure for a large floating wind farm in the Gulf of Maine under survival load conditions. The algorithm uses an evaluation function that computes the probability of mooring system failure, then calculates the expected maintenance costs of a failure via a Monte Carlo method. A cost sensitivity analysis is also performed to compare results for a range of maintenance cost profiles. The results indicate that virtually all of the farm's anchors are strengthened in the minimum cost solution. Anchor strength is increased between 5 and 35% depending on farm location, with anchor strength nearest the export cable being increased the most. The optimal solutions maintain a failure probability of 1.25%, demonstrating the tradeoff point between cost and reliability. System reliability was found to be particularly sensitive to changes in turbine costs and downtime, suggesting further research into floating offshore wind turbine failure modes in extreme loading conditions could be particularly impactful in reducing project uncertainty.
{"title":"Optimizing the Cost and Reliability of Shared Anchors in an Array of Floating Offshore Wind Turbines","authors":"M. Devin, Bryony DuPont, Spencer T Hallowell, S. Arwade","doi":"10.1115/1.4051163","DOIUrl":"https://doi.org/10.1115/1.4051163","url":null,"abstract":"\u0000 Commercial floating offshore wind projects are expected to emerge in the U.S. by the end of this decade. Currently, however, high costs for the technology limit its commercial viability, and a lack of data regarding system reliability heightens project risk. This work presents an optimization algorithm to examine the tradeoffs between cost and reliability for a floating offshore wind array that uses shared anchoring. Combining a multivariable genetic algorithm with elements of Bayesian optimization, the optimization algorithm selectively increases anchor strengths to minimize the added costs of failure for a large floating wind farm in the Gulf of Maine under survival load conditions. The algorithm uses an evaluation function that computes the probability of mooring system failure, then calculates the expected maintenance costs of a failure via a Monte Carlo method. A cost sensitivity analysis is also performed to compare results for a range of maintenance cost profiles. The results indicate that virtually all of the farm's anchors are strengthened in the minimum cost solution. Anchor strength is increased between 5 and 35% depending on farm location, with anchor strength nearest the export cable being increased the most. The optimal solutions maintain a failure probability of 1.25%, demonstrating the tradeoff point between cost and reliability. System reliability was found to be particularly sensitive to changes in turbine costs and downtime, suggesting further research into floating offshore wind turbine failure modes in extreme loading conditions could be particularly impactful in reducing project uncertainty.","PeriodicalId":44694,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B-Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2021-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84286608","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 recent years, Machine Learning (ML) techniques have gained popularity in Structural Health Monitoring (SHM). These have been particularly used for damage detection in a wide range of engineering applications such as wind turbine blades. The outcomes of previous research studies in this area have demonstrated the capabilities of ML for robust damage detection. However, the primary challenge facing ML in SHM is the lack of interpretability of the prediction models hindering the broader implementation of these techniques. For this purpose, this study integrates the novel Shapley Additive exPlanations (SHAP) method into a ML-based damage detection process as a tool for introducing interpretability and, thus, build evidence for reliable decision-making in SHM applications. The SHAP method is based on coalitional game theory and adds global and local interpretability to ML-based models by computing the marginal contribution of each feature. The contribution is used to understand the nature of damage indices (DIs). The applicability of the SHAP method is first demonstrated on a simple lumped mass-spring-damper system with simulated temperature variabilities. Later, the SHAP method has been evaluated on data from an in-operation V27 wind turbine with artificially introduced damage in one of its blades. The results show the relationship between the environmental and operational variabilities (EOVs) and their direct influence on the damage indices. This ultimately helps to understand the difference between false positives caused by EOVs and true positives resulting from damage in the structure.
{"title":"Interpretable machine learning in damage detection using Shapley Additive Explanations","authors":"Artur Movsessian, D. Cava, D. Tcherniak","doi":"10.31224/osf.io/96yf5","DOIUrl":"https://doi.org/10.31224/osf.io/96yf5","url":null,"abstract":"In recent years, Machine Learning (ML) techniques have gained popularity in Structural Health Monitoring (SHM). These have been particularly used for damage detection in a wide range of engineering applications such as wind turbine blades. The outcomes of previous research studies in this area have demonstrated the capabilities of ML for robust damage detection. However, the primary challenge facing ML in SHM is the lack of interpretability of the prediction models hindering the broader implementation of these techniques. For this purpose, this study integrates the novel Shapley Additive exPlanations (SHAP) method into a ML-based damage detection process as a tool for introducing interpretability and, thus, build evidence for reliable decision-making in SHM applications. The SHAP method is based on coalitional game theory and adds global and local interpretability to ML-based models by computing the marginal contribution of each feature. The contribution is used to understand the nature of damage indices (DIs). The applicability of the SHAP method is first demonstrated on a simple lumped mass-spring-damper system with simulated temperature variabilities. Later, the SHAP method has been evaluated on data from an in-operation V27 wind turbine with artificially introduced damage in one of its blades. The results show the relationship between the environmental and operational variabilities (EOVs) and their direct influence on the damage indices. This ultimately helps to understand the difference between false positives caused by EOVs and true positives resulting from damage in the structure.","PeriodicalId":44694,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B-Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79845083","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 study, we use possibility distribution as a basis for parameter uncertainty quantification in 1-D consolidation problems. A Possibility distribution is the onepoint coverage function of a random set and viewed as containing both partial ignorance and uncertainty. Vagueness and scarcity of information needed for characterizing the coefficient of consolidation in clay can be handled using possibility distributions. Possibility distributions can be constructed from existing data, or based on transformation of probability distributions. An attempt is made to set a systematic approach for estimating uncertainty propagation during the consolidation process. The measure of uncertainty is based on Klir’s definition (1995). We make comparisons with results obtained from other approaches (probabilistic...) and discuss the importance of using possibility distributions in this type of problems.
{"title":"Possibilistic Uncertainty Quantification in One-Dimensional Consolidation Problems","authors":"D. Boumezerane","doi":"10.1115/1.4050164","DOIUrl":"https://doi.org/10.1115/1.4050164","url":null,"abstract":"In this study, we use possibility distribution as a basis for parameter uncertainty quantification in 1-D consolidation problems. A Possibility distribution is the onepoint coverage function of a random set and viewed as containing both partial ignorance and uncertainty. Vagueness and scarcity of information needed for characterizing the coefficient of consolidation in clay can be handled using possibility distributions. Possibility distributions can be constructed from existing data, or based on transformation of probability distributions. An attempt is made to set a systematic approach for estimating uncertainty propagation during the consolidation process. The measure of uncertainty is based on Klir’s definition (1995). We make comparisons with results obtained from other approaches (probabilistic...) and discuss the importance of using possibility distributions in this type of problems.","PeriodicalId":44694,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B-Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2021-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86660385","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}
AbstractCeramic matrix composites (CMCs) exhibit process-induced defects such as matrix porosity at multiple length scales that have a considerable influence on their mechanical and failure behavio...
{"title":"Effect of Chemical Vapor Infiltration Induced Matrix Porosity on the Mechanical Behavior of Ceramic Matrix Minicomposites ","authors":"A. M. Nagaraja, S. Gururaja","doi":"10.1115/1.4047465","DOIUrl":"https://doi.org/10.1115/1.4047465","url":null,"abstract":"AbstractCeramic matrix composites (CMCs) exhibit process-induced defects such as matrix porosity at multiple length scales that have a considerable influence on their mechanical and failure behavio...","PeriodicalId":44694,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B-Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84630368","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}
AbstractSurrogate models are efficient tools which have been successfully applied in structural reliability analysis, as an attempt to keep the computational costs acceptable. Among the surrogate m...
摘要替代模型是一种有效的工具，已成功地应用于结构可靠性分析，以保持计算成本可接受。在代理人中……
{"title":"Shallow and Deep Artificial Neural Networks for Structural Reliability Analysis","authors":"Wellison J. S. Gomes","doi":"10.1115/1.4047636","DOIUrl":"https://doi.org/10.1115/1.4047636","url":null,"abstract":"AbstractSurrogate models are efficient tools which have been successfully applied in structural reliability analysis, as an attempt to keep the computational costs acceptable. Among the surrogate m...","PeriodicalId":44694,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B-Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76411997","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}
AbstractDesign and testing of machine guards are provided by international standards in which the inadequacy/suitability of the tested materials for machine guards is obtained by the perforation/no...
{"title":"Finite Element Analysis for Impact Tests on Polycarbonate Safety Guards: Comparison With Experimental Data and Statistical Dispersion of Ballistic Limit","authors":"A. Stecconi, L. Landi","doi":"10.1115/1.4047464","DOIUrl":"https://doi.org/10.1115/1.4047464","url":null,"abstract":"AbstractDesign and testing of machine guards are provided by international standards in which the inadequacy/suitability of the tested materials for machine guards is obtained by the perforation/no...","PeriodicalId":44694,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B-Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88626303","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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