不確定性を考慮した鋼構造物の確率的最適化手法

京都大学新制・課程博士博士(工学)甲第24575号工博第5081号新制||工||1973(附属図書館)京都大学大学院工学研究科建築学専攻(主査)教授 大崎 純, 教授 池田 芳樹, 准教授 藤田 皓平学位規則第4条第1項該当Doctor of Philosophy (Engineering)Kyoto UniversityDFA

Gaussian mixture model for robust design optimization of planar steel frames

A new method is presented for an application of the Gaussian mixture model (GMM) to a multi-objective robust design optimization (RDO) of planar steel frame structures under aleatory (stochastic) uncertainty in material properties, external loads, and discrete design variables. Uncertainty in the discrete design variables is modeled in the wide range between the smallest and largest values in the catalog of the cross-sectional areas. A weighted sum of Gaussians is statistically trained based on the sampled training data to capture an underlying joint probability distribution function (PDF) of random input variables and the corresponding structural response. A simple regression function for predicting the structural response can be found by extracting the information from a conditional PDF, which is directly derived from the captured joint PDF. A multi-objective RDO problem is formulated with three objective functions, namely, the total mass of the structure, and the mean and variance values of the maximum inter-story drift under some constraints on design strength and serviceability requirements. The optimization problem is solved using a multi-objective genetic algorithm utilizing the trained GMM for calculating the statistical values of objective and constraint functions to obtain Pareto-optimal solutions. Since the three objective functions are highly conflicting, the best trade-off solution is desired and found from the obtained Pareto-optimal solutions by performing fuzzy-based compromise programming. The robustness and feasibility of the proposed method for finding the RDO of planar steel frame structures with discrete variables are demonstrated through two design examples

Proximal-exploration multi-objective Bayesian optimization for inverse identification of cyclic constitutive law of structural steels

Despite its importance in seismic response analysis, solving an inverse problem to identify the cyclic elastoplastic parameters for structural steels using conventional optimization algorithms still demands a substantial computational cost of repeatedly carrying out many nonlinear analyses. The parameters are commonly identified based on experimental measures from a single loading history, leading them to be biased and giving inaccurate predictions of structural behavior under other loading histories. To address these issues, we formulate a multi-objective inverse problem that simultaneously minimizes the error functions representing the differences between simulated responses and those measured experimentally from various cyclic tests of a steel specimen or a structural component. We then propose proximal-exploration multi-objective Bayesian optimization (MOBO) for solving the formulated inverse problem, resulting in an approximate Pareto front of parameters while limiting the number of costly simulations. MOBO sorts an initial Pareto front and constructs Gaussian process (GP) models for the error functions from a training dataset. It then relies on the hypervolume of the current solutions, the GP models, and a proximal exploration surrounding the current best compromise parameters to formulate an acquisition function that guides the improvement of the current solutions intelligently. Two identification examples show that the parameters obtained from the multi-objective inverse problem can reduce the bias induced using a single loading history for identification. The robustness of MOBO as well as a good prediction performance of the best compromise solution of identified parameters are demonstrated

Multi-fidelity Bayesian Optimization in Engineering Design

Resided at the intersection of multi-fidelity optimization (MFO) and Bayesian optimization (BO), MF BO has found a niche in solving expensive engineering design optimization problems, thanks to its advantages in incorporating physical and mathematical understandings of the problems, saving resources, addressing exploitation-exploration trade-off, considering uncertainty, and processing parallel computing. The increasing number of works dedicated to MF BO suggests the need for a comprehensive review of this advanced optimization technique. In this paper, we survey recent developments of two essential ingredients of MF BO: Gaussian process (GP) based MF surrogates and acquisition functions. We first categorize the existing MF modeling methods and MFO strategies to locate MF BO in a large family of surrogate-based optimization and MFO algorithms. We then exploit the common properties shared between the methods from each ingredient of MF BO to describe important GP-based MF surrogate models and review various acquisition functions. By doing so, we expect to provide a structured understanding of MF BO. Finally, we attempt to reveal important aspects that require further research for applications of MF BO in solving intricate yet important design optimization problems, including constrained optimization, high-dimensional optimization, optimization under uncertainty, and multi-objective optimization

Institutionalism and its effect on labour forecasting in Vietnamese firms

This study examines factors that influence firm forecasting regarding their labour expansion in Vietnam. Conventional wisdom has it that foreign-owned and large private firms make more accurate forecasts since they have more resources and experience than their smaller and state-owned counterparts. However, this study empirically shows that state-owned and small firms make more accurate forecasting values. There are two possibilities that can explain this counterintuitive result: (1) the institutional incompleteness in the post-communist economy and (2) systematic underestimation of their own performance by foreign and large private firms, which results from the institutional complexity in Vietnam. These unique findings provide valuable information for both academia and practitioners

Does foreign ownership impact accounting conservatism adoption in Vietnam?

This study investigates the effects of foreign ownership on accounting conservatism adoption in Vietnam. Although foreign ownership is found to have a positive relationship with accounting conservatism in Korea (An, 2015), there is still no general agreement on it. In this regard, the purpose of this study is to shed more light on the association between foreign ownership and accounting conservatism. Using data from Vietnamese firms listed on stock exchanges, the study finds that in contrast to the findings of An, foreign ownership is negatively associated with accounting conservatism. This result supports the transient hypothesis of foreign ownership, indicating that foreign investors with the low level of ownership do not have significant incentives to oversee managers, thus not influencing financial reporting quality. © 2017 Prague Development Center.Internal Grant Agency of FaME; TBU - The Relationship between Concentration Ownership and Financial Reporting Quality [IGA/FaME/2017/004

How Much State Ownership Do Hybrid Firms Need for Better Performance?

Hybrid ownership – sharing partial business ownership with the state – is a new form of political connections that entrepreneurs in developing countries may employ to improve their access to key resources. This study investigates hybrid ownership as a strategic decision of entrepreneurs running small businesses in Vietnam – a transition economy. Utilising the resource dependence theory and legitimacy viewpoint, we propose and evidently show that increased state ownership in hybrid firms leads to improved performance. However, increasing state ownership beyond a minority share threshold harms firm performance due to the presence of agency costs. Also, the involvement of the state in firm governance reduces the benefits gained from having state ownership

Groundwater contamination with nitrogenous compounds in Kumamoto Prefecture and Hanoi City : Present conditions and adopted countermeasures

Joint Research on Environmental Science and Technology for the Eart

WATER QUALITY SURVEY OF VIETNAM

Joint Research on Environmental Science and Technology for the Eart

Intergenerational effects of violence on women's perinatal wellbeing and infant health outcomes: evidence from a birth cohort study in Central Vietnam.

BACKGROUND: Girls exposed to violence have a high risk of being victimized as adults and are more likely than non-abused women to have children who are treated violently. This intergenerational transmission may be especially serious when women suffer violence during pregnancy and early motherhood, as it impairs maternal wellbeing and infant health and development. This study examined the intergenerational effects of being exposed to childhood maltreatment (CM) and prenatal intimate partner violence (p-IPV) on perinatal mental distress and birth outcomes in central Vietnam. METHODS: A birth cohort study in Hue City, Vietnam was conducted with 150 women in the third trimester of pregnancy (Wave 1) and 3 months after childbirth (Wave 2). Using multivariable logistic regression models, augmented inverse-probability-weighted estimators and structural equation modelling (SEM), we analyzed a theoretical model by evaluating adjusted risk differences and pathways between CM, p-IPV and subsequent perinatal adversity and indicators of infant health problems. RESULTS: One in two pregnant women experienced at least one form of CM (55.03%) and one in ten pregnant women experienced both CM and p-IPV (10.67%). Mothers who experienced p-IPV or witnessed IPV as a child were approximately twice as likely to experience poor mental health during pregnancy [ARR 1.94, 95% CI (1.20-3.15)]. Infants had a two-fold higher risk of adverse birth outcomes (low birth weight, preterm birth, admission to neonatal intensive care) [ARR 2.45 95% CI (1.42, 4.25)] if their mothers experienced any form of p-IPV, with greater risk if their mothers were exposed to both CM and p-IPV [ARR 3.45 95% CI (1.40, 8.53)]. Notably, significant pathways to p-IPV were found via adverse childhood experience (ACE) events (β = 0.13), neighborhood disorder (β = 0.14) and partner support (β = - 1.3). CONCLUSION: These results emphasize the detrimental and prolonged nature of the effect of violence during childhood and pregnancy. Exposure to childhood maltreatment and violence during pregnancy increases the risk of maternal mental health difficulties and adverse birth outcomes. Antenatal care systems need to be responsive to women's previous experiences of violence and maternal mental health. The significant protective role of partner support and social support should also be considered when designing tailored interventions to address violence during pregnancy