An Efficient Approach to Reliability-based Topology Optimization for Continua under Material Uncertainty

This contribution presents a computationally efficient method for reliability-based topology optimization for continuum domains under material properties uncertainty. Material Young’s modulus is assumed to be lognormally distributed and correlated within the domain. The computational efficiency is achieved through estimating the response statistics with stochastic perturbation of second order, using these statistics to fit an appropriate distribution that follows the empirical distribution of the response, and employing an efficient gradient-based optimizer. Two widely-studied topology optimization problems are examined and the changes in the optimized topology is discussed for various levels of target reliability and correlation strength. Accuracy of the proposed algorithm is verified using Monte Carlo simulation

Stress-Based Topology Optimization of Steel-Frame Structures Using Members with Standard Cross Sections: Gradient-Based Approach

This article presents a computationally efficient methodology for stress-based topology optimization of steel frame structures with cross-sectional properties that are mapped from I-beam sections of a design manual. To account for the natural variability of the data, this mapping is achieved via quantile regression to derive continuous relationships between cross-sectional area (the design variable) and other section properties. These relationships are used for deriving the gradient of structural performance, which allows using computationally efficient gradient-based optimization schemes. Three frame structures are designed using the proposed algorithm, the resulting designs are compared with traditional compliance-based topology optimization algorithms, and changes in the designs are discussed. A comparison of stress distribution within the designed structures verified the effectiveness of the proposed methodology

An Efficient Approach to Reliability-based Topology Optimization for Continua under Material Uncertainty

This contribution presents a computationally efficient method for reliability-based topology optimization for continuum domains under material properties uncertainty. Material Young’s modulus is assumed to be lognormally distributed and correlated within the domain. The computational efficiency is achieved through estimating the response statistics with stochastic perturbation of second order, using these statistics to fit an appropriate distribution that follows the empirical distribution of the response, and employing an efficient gradient-based optimizer. Two widely-studied topology optimization problems are examined and the changes in the optimized topology is discussed for various levels of target reliability and correlation strength. Accuracy of the proposed algorithm is verified using Monte Carlo simulation

Numerical Evaluation of the Extended Endplate Moment Connection Subjected to Cyclic Loading

In this paper, the seismic behaviour of extended endplate moment connection is analysed using finite element method (FEM). First, an existing test setup is modelled and analysed using ANSYS computer program. The model is validated by comparing the results from the finite element with the experimental ones. Afterwards, by changing the dimensions of members of the connection, their effect on the overall seismic performance of connection is investigated. The results show that by enlarging the column depth and stiffening the connection, the seismic performance is improved and the thickness of endplate should be chosen in a way that its moment capacity is larger than the plastic moment of beam

Advancing Concrete Strength Prediction using Non-destructive Testing: Development and Verification of a Generalizable Model

Accurate prediction of concrete compressive strength is imperative for investigating the in-situ concrete quality. To avoid destructive testing, developing reliable predictive models for concrete compressive strength using nondestructive tests (NDTs) is an active area of research. However, many of the developed models are dependent on calibration and/or concrete past history (e.g. mixture proportion, curing history, concrete mechanical properties, etc.), which reduces their utility for in-situ predictions. This paper develops predictive models for concrete compressive strength that are independent of concrete past history. To this end, ultrasonic pulse velocity (UPV) and rebound hammer (RH) tests were performed on 84 concrete cylindrical samples. Next, compressive strengths were determined using destructive testing on these cylinders, and predictive models were developed using NDT results. Furthermore, to ensure generalizability to new data, all models were tested on independent data collected from six different research papers. The results support combined usage of UPV and RH in a quadratic polynomial model structure. Therefore, the final model was proposed based on combining models from a threefold cross-validation of the experimental data. This model predicted the independent data with very good accuracy. Finally, a concrete quality classification table using combined RH and UPV is proposed based on a variant of machine learning k-means clustering algorithm

Reliability-based Topology Optimization of Trusses with Stochastic Stiffness

A new method is proposed for reliability-based topology optimization of truss structures with random geometric imperfections and material variability. Such imperfections and variability, which may result from manufacturing processes, are assumed to be small in relation to the truss dimensions and mean material properties and normally distributed. Extensive numerical evidence suggests that the trusses, when optimized in terms of a displacement-based demand metric, are characterized by randomness in the stiffness that follow the Gumbel distribution. Based on this observation, it was possible to derive analytical expressions for the structural reliability, enabling the formulation of a computationally efficient single-loop reliability-based topology optimization algorithm. Response statistics are estimated using a second-order perturbation expansion of the stiffness matrix and design sensitivities are derived so that they can be directly used by gradient-based optimizers. Several examples illustrate the accuracy of the perturbation expressions and the applicability of the method for developing optimal designs that meet target reliabilities

Advancing Concrete Strength Prediction using Non-destructive Testing: Development and Verification of a Generalizable Model

Accurate prediction of concrete compressive strength is imperative for investigating the in-situ concrete quality. To avoid destructive testing, developing reliable predictive models for concrete compressive strength using nondestructive tests (NDTs) is an active area of research. However, many of the developed models are dependent on calibration and/or concrete past history (e.g. mixture proportion, curing history, concrete mechanical properties, etc.), which reduces their utility for in-situ predictions. This paper develops predictive models for concrete compressive strength that are independent of concrete past history. To this end, ultrasonic pulse velocity (UPV) and rebound hammer (RH) tests were performed on 84 concrete cylindrical samples. Next, compressive strengths were determined using destructive testing on these cylinders, and predictive models were developed using NDT results. Furthermore, to ensure generalizability to new data, all models were tested on independent data collected from six different research papers. The results support combined usage of UPV and RH in a quadratic polynomial model structure. Therefore, the final model was proposed based on combining models from a threefold cross-validation of the experimental data. This model predicted the independent data with very good accuracy. Finally, a concrete quality classification table using combined RH and UPV is proposed based on a variant of machine learning k-means clustering algorithm

Comparing Methods of Targeting Obesity Interventions in Populations: An Agent-based Simulation

Social networks as well as neighborhood environments have been shown to effect obesity-related behaviors including energy intake and physical activity. Accordingly, harnessing social networks to improve targeting of obesity interventions may be promising to the extent this leads to social multiplier effects and wider diffusion of intervention impact on populations. However, the literature evaluating network-based interventions has been inconsistent. Computational methods like agent-based models (ABM) provide researchers with tools to experiment in a simulated environment. We develop an ABM to compare conventional targeting methods (random selection, based on individual obesity risk, and vulnerable areas) with network-based targeting methods. We adapt a previously published and validated model of network diffusion of obesity-related behavior. We then build social networks among agents using a more realistic approach. We calibrate our model first against national-level data. Our results show that network-based targeting may lead to greater population impact. We also present a new targeting method that outperforms other methods in terms of intervention effectiveness at the population level

A Secure-Coordinated Expansion Planning of Generation and Transmission Using Game Theory and Minimum Singular Value

In this paper a novel method have been proposed for expansion planning of generation and transmission that considered static security of the system such as voltage security margin And loadability limit. In the same study of expansion planning Security constraints of the system are neglected. In this study at the first step minimum singular value technique is used to evaluate voltage security margin and loadabolity limit, in order to select best bus for load incrimination. After it, in order to Supply the load, coordinated expansion planning of generation and transmission is needed, therefor the strategic interaction between transmission company (TransCo) and generation company (GenCo) for Transmission expansion planning (TEP) and generation expansion planning (GEP) in a competitive electricity market is proposed using Game Theory (GT).DOI:http://dx.doi.org/10.11591/ijece.v4i6.668