Uncertainty is an essentially challenging for safe construction and long-term stability of geotechnical engineering.The inverse analysis is commonly utilized to determine the physico-mechanical parameters.However,conv...Uncertainty is an essentially challenging for safe construction and long-term stability of geotechnical engineering.The inverse analysis is commonly utilized to determine the physico-mechanical parameters.However,conventional inverse analysis cannot deal with uncertainty in geotechnical and geological systems.In this study,a framework was developed to evaluate and quantify uncertainty in inverse analysis based on the reduced-order model(ROM)and probabilistic programming.The ROM was utilized to capture the mechanical and deformation properties of surrounding rock mass in geomechanical problems.Probabilistic programming was employed to evaluate uncertainty during construction in geotechnical engineering.A circular tunnel was then used to illustrate the proposed framework using analytical and numerical solution.The results show that the geomechanical parameters and associated uncertainty can be properly obtained and the proposed framework can capture the mechanical behaviors under uncertainty.Then,a slope case was employed to demonstrate the performance of the developed framework.The results prove that the proposed framework provides a scientific,feasible,and effective tool to characterize the properties and physical mechanism of geomaterials under uncertainty in geotechnical engineering problems.展开更多
For probabilistic programs,there is some work for qualitative and quantitative analysis about expec-tation or mean,such as expected termination time,and expected cost analysis.However,another non-trivial issue is abou...For probabilistic programs,there is some work for qualitative and quantitative analysis about expec-tation or mean,such as expected termination time,and expected cost analysis.However,another non-trivial issue is about tail bounds(i.e.,upper bounds of tail probabilities),which can provide high-probability guarantees to extreme events.In this work,we focus on the problem of tail-bound cost analysis over nondeterministic proba-bilistic programs,which aims to automatically obtain the tail bound of resource usages over such programs.To achieve this goal,we present a novel approach,combined with a suitable concentration inequality,to derive the tail bound of accumulated cost until program termination.Our approach can handle both positive and negative costs.Moreover,our approach enables an automated template-based synthesis of supermartingales and leads to an efficient polynomial-time algorithm.To show the effectiveness of our approach,we present experimental results on various programs and make a comparison with state-of-the-art tools.展开更多
Currently, agent-based computing is an active research area, and great efforts have been made towards the agent-oriented programming both from a theoretical and practical view. However, most of them assume that there ...Currently, agent-based computing is an active research area, and great efforts have been made towards the agent-oriented programming both from a theoretical and practical view. However, most of them assume that there is no uncertainty in agents＇ mental state and their environment. In other words, under this assumption agent developers are just allowed to specify how his agent acts when the agent is 100% sure about what is true/false. In this paper, this unrealistic assumption is removed and a new agent-oriented probabilistic logic programming language is proposed, which can deal with uncertain information about the world. The programming language is based on a combination of features of probabilistic logic programming and imperative programming.展开更多
In this paper a successive approximation method for solving probabilistic constrained programs is proposed. At each iteration of this method only few linear programs on a normal scale have to be solved. An error bound...In this paper a successive approximation method for solving probabilistic constrained programs is proposed. At each iteration of this method only few linear programs on a normal scale have to be solved. An error bound for the optimal value is given.展开更多
基金The authors gratefully acknowledge the support from the National Natural Science Foundation of China(Grant No.42377174)the Natural Science Foundation of Shandong Province,China(Grant No.ZR2022ME198)the Open Research Fund of State Key Laboratory of Geomechanics and Geotechnical Engineering,Institute of Rock and Soil Mechanics,Chinese Academy of Sciences(Grant No.Z020006).
文摘Uncertainty is an essentially challenging for safe construction and long-term stability of geotechnical engineering.The inverse analysis is commonly utilized to determine the physico-mechanical parameters.However,conventional inverse analysis cannot deal with uncertainty in geotechnical and geological systems.In this study,a framework was developed to evaluate and quantify uncertainty in inverse analysis based on the reduced-order model(ROM)and probabilistic programming.The ROM was utilized to capture the mechanical and deformation properties of surrounding rock mass in geomechanical problems.Probabilistic programming was employed to evaluate uncertainty during construction in geotechnical engineering.A circular tunnel was then used to illustrate the proposed framework using analytical and numerical solution.The results show that the geomechanical parameters and associated uncertainty can be properly obtained and the proposed framework can capture the mechanical behaviors under uncertainty.Then,a slope case was employed to demonstrate the performance of the developed framework.The results prove that the proposed framework provides a scientific,feasible,and effective tool to characterize the properties and physical mechanism of geomaterials under uncertainty in geotechnical engineering problems.
基金the National Natural Science Foundation of China(Nos.61802254,62072299,and 61772336)。
文摘For probabilistic programs,there is some work for qualitative and quantitative analysis about expec-tation or mean,such as expected termination time,and expected cost analysis.However,another non-trivial issue is about tail bounds(i.e.,upper bounds of tail probabilities),which can provide high-probability guarantees to extreme events.In this work,we focus on the problem of tail-bound cost analysis over nondeterministic proba-bilistic programs,which aims to automatically obtain the tail bound of resource usages over such programs.To achieve this goal,we present a novel approach,combined with a suitable concentration inequality,to derive the tail bound of accumulated cost until program termination.Our approach can handle both positive and negative costs.Moreover,our approach enables an automated template-based synthesis of supermartingales and leads to an efficient polynomial-time algorithm.To show the effectiveness of our approach,we present experimental results on various programs and make a comparison with state-of-the-art tools.
基金This work is supported by the National Natural Science Foundation of China under Grand No. 60496322 and the Chinese Ministry of Education under Grand No. 05JZD720.4001.
文摘Currently, agent-based computing is an active research area, and great efforts have been made towards the agent-oriented programming both from a theoretical and practical view. However, most of them assume that there is no uncertainty in agents＇ mental state and their environment. In other words, under this assumption agent developers are just allowed to specify how his agent acts when the agent is 100% sure about what is true/false. In this paper, this unrealistic assumption is removed and a new agent-oriented probabilistic logic programming language is proposed, which can deal with uncertain information about the world. The programming language is based on a combination of features of probabilistic logic programming and imperative programming.
文摘In this paper a successive approximation method for solving probabilistic constrained programs is proposed. At each iteration of this method only few linear programs on a normal scale have to be solved. An error bound for the optimal value is given.
