{"id":88546,"date":"2024-10-18T06:56:12","date_gmt":"2024-10-18T06:56:12","guid":{"rendered":"https:\/\/pdfstandards.shop\/product\/uncategorized\/asce-9780784476864-2012\/"},"modified":"2024-10-24T20:19:59","modified_gmt":"2024-10-24T20:19:59","slug":"asce-9780784476864-2012","status":"publish","type":"product","link":"https:\/\/pdfstandards.shop\/product\/publishers\/asce\/asce-9780784476864-2012\/","title":{"rendered":"ASCE 9780784476864 2012"},"content":{"rendered":"
LNMech 2 presents the main concepts, formulations, and recent advances in the use of a mathematical-mechanical modeling process to predict the responses of a real structural system in its environment.<\/p>\n
PDF Pages<\/th>\n | PDF Title<\/th>\n<\/tr>\n | ||||||
---|---|---|---|---|---|---|---|
1<\/td>\n | Cover <\/td>\n<\/tr>\n | ||||||
6<\/td>\n | Contents <\/td>\n<\/tr>\n | ||||||
10<\/td>\n | 1 Introduction <\/td>\n<\/tr>\n | ||||||
12<\/td>\n | 2 Short overview of probabilistic modeling of uncertainties and related topics 2.1 Uncertainty and variability <\/td>\n<\/tr>\n | ||||||
13<\/td>\n | 2.2 Types of approach for probabilistic modeling of uncertainties <\/td>\n<\/tr>\n | ||||||
15<\/td>\n | 2.3 Types of representation for the probabilistic modeling of uncertainties <\/td>\n<\/tr>\n | ||||||
18<\/td>\n | 2.4 Construction of prior probability models using the maximum entropy principle under the constraints defined by the available information <\/td>\n<\/tr>\n | ||||||
20<\/td>\n | 2.5 Random Matrix Theory <\/td>\n<\/tr>\n | ||||||
24<\/td>\n | 2.6 Propagation of uncertainties and methods to solve the stochastic dynamical equations <\/td>\n<\/tr>\n | ||||||
26<\/td>\n | 2.7 Identification of the prior and posterior probability models of uncertainties <\/td>\n<\/tr>\n | ||||||
28<\/td>\n | 2.8 Robust updating of computational models and robust design with uncertain computational models <\/td>\n<\/tr>\n | ||||||
30<\/td>\n | 3 Parametric probabilistic approach to uncertainties in computational structural dynamics 3.1 Introduction of the mean computational model in computational structural dynamics <\/td>\n<\/tr>\n | ||||||
31<\/td>\n | 3.2 Introduction of the reduced mean computational model <\/td>\n<\/tr>\n | ||||||
33<\/td>\n | 3.3 Methodology for the parametric probabilistic approach of modelparameter uncertainties <\/td>\n<\/tr>\n | ||||||
34<\/td>\n | 3.4 Construction of the prior probability model of model-parameter uncertainties <\/td>\n<\/tr>\n | ||||||
35<\/td>\n | 3.5 Estimation of the parameters of the prior probability model of the uncertain model parameter <\/td>\n<\/tr>\n | ||||||
36<\/td>\n | 3.6 Posterior probability model of uncertainties using output-predictionerror method and the Bayesian method <\/td>\n<\/tr>\n | ||||||
38<\/td>\n | 4 Nonparametric probabilistic approach to uncertainties in computational structural dynamics 4.1 Methodology to take into account both the model-parameter uncertainties and the model uncertainties (modeling errors) <\/td>\n<\/tr>\n | ||||||
39<\/td>\n | 4.2 Construction of the prior probability model of the random matrices <\/td>\n<\/tr>\n | ||||||
40<\/td>\n | 4.3 Estimation of the parameters of the prior probability model of uncertainties <\/td>\n<\/tr>\n | ||||||
41<\/td>\n | 4.4 Comments about the applications and the validation of the nonparametric probabilistic approach of uncertainties <\/td>\n<\/tr>\n | ||||||
44<\/td>\n | 5 Generalized probabilistic approach to uncertainties in computational structural dynamics 5.1 Methodology of the generalized probabilistic approach <\/td>\n<\/tr>\n | ||||||
46<\/td>\n | 5.2 Construction of the prior probability model of the random matrices 5.3 Estimation of the parameters of the prior probability model of uncertainties <\/td>\n<\/tr>\n | ||||||
47<\/td>\n | 5.4 Posterior probability model of uncertainties using the Bayesian method <\/td>\n<\/tr>\n | ||||||
50<\/td>\n | 6 Nonparametric probabilistic approach to uncertainties in structural-acoustic models for the low- and medium-frequency ranges <\/td>\n<\/tr>\n | ||||||
51<\/td>\n | 6.1 Reduced mean structural-acoustic model <\/td>\n<\/tr>\n | ||||||
55<\/td>\n | 6.2 Stochastic reduced-order model of the computational structuralacoustic model using the nonparametric probabilistic approach of uncertainties <\/td>\n<\/tr>\n | ||||||
56<\/td>\n | 6.3 Construction of the prior probability model of uncertainties <\/td>\n<\/tr>\n | ||||||
58<\/td>\n | 6.4 Model parameters, stochastic solver and convergence analysis 6.5 Estimation of the parameters of the prior probability model of uncertainties <\/td>\n<\/tr>\n | ||||||
59<\/td>\n | 6.6 Comments about the applications and the experimental validation of the nonparametric probabilistic approach of uncertainties in structural acoustics <\/td>\n<\/tr>\n | ||||||
60<\/td>\n | 7 Nonparametric probabilistic approach to uncertainties in computational nonlinear structural dynamics <\/td>\n<\/tr>\n | ||||||
61<\/td>\n | 7.1 Nonlinear equation for 3D geometrically nonlinear elasticity 7.2 Nonlinear reduced mean model <\/td>\n<\/tr>\n | ||||||
63<\/td>\n | 7.3 Algebraic properties of the nonlinear stiffnesses 7.4 Stochastic reduced-order model of the nonlinear dynamical system using the nonparametric probabilistic approach of uncertainties <\/td>\n<\/tr>\n | ||||||
65<\/td>\n | 7.5 Comments about the applications of the nonparametric probabilistic approach of uncertainties in computational nonlinear structural dynamics <\/td>\n<\/tr>\n | ||||||
66<\/td>\n | 8 Identification of high-dimension polynomial chaos expansions with random coefficients for non-Gaussian tensor-valued random fields using partial and limited experimental data <\/td>\n<\/tr>\n | ||||||
67<\/td>\n | 8.1 Definition of the problemto be solved <\/td>\n<\/tr>\n | ||||||
69<\/td>\n | 8.2 Construction of a family of prior algebraic probability models (PAPM) for the tensor-valued random field in elasticity theory <\/td>\n<\/tr>\n | ||||||
79<\/td>\n | 8.3 Methodology for the identification of a high-dimension polynomial chaos expansion using partial and limited experimental data <\/td>\n<\/tr>\n | ||||||
85<\/td>\n | 8.4 Computational aspects for constructing realizations of polynomial chaos in high dimension <\/td>\n<\/tr>\n | ||||||
87<\/td>\n | 8.5 Prior probability model of the random VVC <\/td>\n<\/tr>\n | ||||||
90<\/td>\n | 8.6 Posterior probability model of the random VVC using the classical Bayesian approach <\/td>\n<\/tr>\n | ||||||
95<\/td>\n | 8.7 Posterior probability model of the random VVC using a new approach derived from the Bayesian approach <\/td>\n<\/tr>\n | ||||||
97<\/td>\n | 8.8 Comments about the applications concerning the identification of polynomial chaos expansions of random fields using experimental data <\/td>\n<\/tr>\n | ||||||
98<\/td>\n | 9 Conclusion <\/td>\n<\/tr>\n | ||||||
100<\/td>\n | References <\/td>\n<\/tr>\n | ||||||
118<\/td>\n | Index A B C <\/td>\n<\/tr>\n | ||||||
119<\/td>\n | D E <\/td>\n<\/tr>\n | ||||||
120<\/td>\n | F <\/td>\n<\/tr>\n | ||||||
121<\/td>\n | G H <\/td>\n<\/tr>\n | ||||||
123<\/td>\n | K L <\/td>\n<\/tr>\n | ||||||
124<\/td>\n | M <\/td>\n<\/tr>\n | ||||||
125<\/td>\n | N <\/td>\n<\/tr>\n | ||||||
127<\/td>\n | O P <\/td>\n<\/tr>\n | ||||||
132<\/td>\n | R <\/td>\n<\/tr>\n | ||||||
133<\/td>\n | S T U <\/td>\n<\/tr>\n | ||||||
134<\/td>\n | V <\/td>\n<\/tr>\n<\/table>\n","protected":false},"excerpt":{"rendered":" Stochastic Models of Uncertainties in Computational Mechanics<\/b><\/p>\n |