Resource Type

Journal Article 417

Conference Videos 10

Conference Topics 1

Year

2023 30

2022 34

2021 43

2020 29

2019 38

2018 26

2017 32

2016 17

2015 21

2014 20

2013 16

2012 12

2011 12

2010 24

2009 15

2008 8

2007 12

2006 4

2005 5

2004 6

open ︾

Keywords

modeling 30

numerical modeling 12

Modeling 9

parametric study 7

optimization 6

building information modeling 5

finite element method 5

Additive manufacturing 4

Deep learning 4

finite element analysis 4

numerical simulation 4

parametric analysis 4

simulation 4

Machine learning 3

modeling and simulation 3

project management 3

ANOVA 2

Algae 2

Artificial intelligence 2

open ︾

Search scope:

排序： Display mode:

Building information modeling based on intelligent parametric technology

ZENG Xudong, TAN Jie

Frontiers of Structural and Civil Engineering 2007, Volume 1, Issue 3,   Pages 367-370 doi: 10.1007/s11709-007-0049-4

Abstract: (BIM) based on intelligent parametric modeling technology.Building information modeling is a new technology in the field of computer aided architectural designtwo-dimensional CAD technology, and demonstrates the advantages and characteristics of intelligent parametricmodeling technology.Building information modeling, which is based on intelligent parametric modeling technology, will certainly
HTML PDF Collect

An efficient prediction framework for multi-parametric yield analysis under parameter variations Article

Xin LI,Jin SUN,Fu XIAO

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 12,   Pages 1344-1359 doi: 10.1631/FITEE.1601225

Abstract: Previous algorithms on parametric yield prediction are limited to predicting a single-parametric yieldor per-forming balanced optimization for several single-parametric yields.In this paper we suggest an efficient multi-parametric yield prediction framework, in which multipleFinally, a copula-based parametric yield prediction procedure has been developed to solve the multi-parametriclimits, or a multi-parametric yield surface under all ranges of performance limits.

Keywords： Yield prediction     Parameter variations     Multi-parametric yield     Performance modeling     Sparse representation    

HTML PDF Collect

Modeling of bentonite/sepiolite plastic concrete compressive strength using artificial neural network

Ali Reza GHANIZADEH, Hakime ABBASLOU, Amir Tavana AMLASHI, Pourya ALIDOUST

Frontiers of Structural and Civil Engineering 2019, Volume 13, Issue 1,   Pages 215-239 doi: 10.1007/s11709-018-0489-z

Abstract: different variables on the plastic concrete compressive strength values was evaluated by conducting parametric

Keywords： bentonite/sepiolite plastic concrete     compressive strength     artificial neural network     support vector machine     parametric    

HTML PDF Collect

Parametric equations for notch stress concentration factors of rib–deck welds under bending loading

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 3,   Pages 595-608 doi: 10.1007/s11709-021-0720-1

Abstract: Then, we performed a parametric analysis to investigate the effects of multiple geometric parameters

Keywords： notch stress concentration factor     rib–deck weld     parametric analysis     regression analysis     parametric    

HTML PDF Collect

Seismic fragility curves for structures using non-parametric representations

Chu MAI, Katerina KONAKLI, Bruno SUDRET

Frontiers of Structural and Civil Engineering 2017, Volume 11, Issue 2,   Pages 169-186 doi: 10.1007/s11709-017-0385-y

Abstract: The classical approach relies on assuming a lognormal shape of the fragility curves; it is thus parametricIn this paper, we introduce two non-parametric approaches to establish the fragility curves without employingThe curves obtained with the non-parametric approaches are compared with respective curves based on the

Keywords： earthquake engineering     fragility curves     lognormal assumption     non-parametric approach     kernel density estimation    

HTML PDF Collect

Parametric oscillation of cables and aerodynamic effect

Yong XIA, Jing ZHANG, Youlin XU, Yozo FUJINO,

Frontiers of Structural and Civil Engineering 2010, Volume 4, Issue 3,   Pages 321-325 doi: 10.1007/s11709-010-0028-z

Abstract: This paper addresses the aerodynamic effect on the nonlinear oscillation, particularly parametric vibration

Keywords： parametric vibration     cables     cable-stayed bridge     nonlinear vibration    

HTML PDF Collect

Parametric study on seismic performance of self-centering reinforced concrete column with bottom-placed

Frontiers of Structural and Civil Engineering   Pages 1145-1162 doi: 10.1007/s11709-023-0945-2

Abstract: Extensive parametric studies pertaining to SRRC columns have been conducted to investigate the critical

Keywords： seismic resilience     self-centering     rubber layer     flag-shaped hysteresis loop     parametric study    

HTML PDF Collect

Nonlinear Model-Based Process Operation under Uncertainty Using Exact Parametric Programming

Vassilis M. Charitopoulos,Lazaros G. Papageorgiou,Vivek Dua

Engineering 2017, Volume 3, Issue 2,   Pages 202-213 doi: 10.1016/J.ENG.2017.02.008

Abstract:

In the present work, two new, (multi-)parametric programming (mp-P)-inspired algorithms for the solutionlogarithmic terms, with the first one being developed for the deterministic case, and the second for the parametric

Keywords： Parametric programming     Uncertainty     Process synthesis     Mixed-integer nonlinear programming     Symbolic manipulation    

HTML PDF Collect

Parametric study of hexagonal castellated beams in post-tensioned self-centering steel connections

Hassan ABEDI SARVESTANI

Frontiers of Structural and Civil Engineering 2019, Volume 13, Issue 5,   Pages 1020-1035 doi: 10.1007/s11709-019-0534-6

Abstract: Furthermore, properties of steel material has been simulated using bilinear elastoplastic modeling with

Keywords： finite element analysis     hexagonal castellated beam     parametric study     post-tensioned self-centering steel    

HTML PDF Collect

Parametric control of structural responses using an optimal passive tuned mass damper under stationary

Min-Ho CHEY, Jae-Ung KIM

Frontiers of Structural and Civil Engineering 2012, Volume 6, Issue 3,   Pages 267-280 doi: 10.1007/s11709-012-0170-x

Abstract: a passive tuned mass damper (TMD) system as a seismic damping device is outlined, highlighting the parametricoptimal damping ratio, to stationary Gaussian white noise acceleration are investigated by using a parametric

Keywords： tuned mass damper     parametric optimization     passive control     white noise     earthquake excitation    

HTML PDF Collect

difference analysis of shallow sprayed concrete tunnels crossing a reverse fault or a normal fault: A parametric

Masoud RANJBARNIA, Milad ZAHERI, Daniel DIAS

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 4,   Pages 998-1011 doi: 10.1007/s11709-020-0621-8

Abstract: the accuracy of the numerical simulation predictions with the centrifuge physical model results, a parametric

Keywords： urban tunnel     sprayed concrete     reverse fault     normal fault     finite difference analysis    

HTML PDF Collect

Predicting lateral displacement caused by seismic liquefaction and performing parametric sensitivity

Nima PIRHADI, Xiaowei TANG, Qing YANG, Afshin ASADI, Hazem Samih MOHAMED

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 2,   Pages 506-519 doi: 10.1007/s11709-021-0677-0

Abstract: Lateral displacement due to liquefaction ( ) is the most destructive effect of earthquakes in saturated loose or semi-loose sandy soil. Among all earthquake parameters, the standardized cumulative absolute velocity ( ) exhibits the largest correlation with increasing pore water pressure and liquefaction. Furthermore, the complex effect o fine content( ) at different values has been studied and demonstrated. Nevertheless, these two contexts have not been entered into empirical and semi-empirical models to predict This study bridges this gap by adding to the data set and developing two artificial neural network (ANN) models. The first model is based on the entire range of the parameters, whereas the second model is based on the samples with values that are less than the 28% critical value. The results demonstrate the higher accuracy of the second model that is developed even with less data. Additionally, according to the uncertainties in the geotechnical and earthquake parameters, sensitivity analysis was performed via Monte Carlo simulation (MCS) using the second developed ANN model that exhibited higher accuracy. The results demonstrated the significant influence of the uncertainties of earthquake parameters on predicting

Keywords： lateral spreading displacement     cumulative absolute velocity     fine content     artificial neural network     sensitivity analysis     Monte Carlo simulation    

HTML PDF Collect

Effect of variable heat capacities on performance of an irreversible Miller heat engine

Xingmei YE

Frontiers in Energy 2012, Volume 6, Issue 3,   Pages 280-284 doi: 10.1007/s11708-012-0203-0

Abstract: Based on the variable heat capacities of the working fluid, the irreversibility coming from the compression and expansion processes, and the heat leak losses through the cylinder wall, an irreversible cycle model of the Miller heat engine was established, from which expressions for the efficiency and work output of the cycle were derived. The performance characteristic curves of the Miller heat engine were generated through numerical calculation, from which the optimal regions of some main parameters such as the work output, efficiency and pressure ratio were determined. Moreover, the influence of the compression and expansion efficiencies, the variable heat capacities and the heat leak losses on the performance of the cycle was discussed in detail, and consequently, some significant results were obtained.

Keywords： Miller cycle     variable heat capacity     irreversibility     parametric optimization    

HTML PDF Collect

Compressive strength prediction and optimization design of sustainable concrete based on squirrel search algorithm-extreme gradient boosting technique

Frontiers of Structural and Civil Engineering   Pages 1310-1325 doi: 10.1007/s11709-023-0997-3

Abstract: Concrete is the most commonly used construction material. However, its production leads to high carbon dioxide (CO2) emissions and energy consumption. Therefore, developing waste-substitutable concrete components is necessary. Improving the sustainability and greenness of concrete is the focus of this research. In this regard, 899 data points were collected from existing studies where cement, slag, fly ash, superplasticizer, coarse aggregate, and fine aggregate were considered potential influential factors. The complex relationship between influential factors and concrete compressive strength makes the prediction and estimation of compressive strength difficult. Instead of the traditional compressive strength test, this study combines five novel metaheuristic algorithms with extreme gradient boosting (XGB) to predict the compressive strength of green concrete based on fly ash and blast furnace slag. The intelligent prediction models were assessed using the root mean square error (RMSE), coefficient of determination (R2), mean absolute error (MAE), and variance accounted for (VAF). The results indicated that the squirrel search algorithm-extreme gradient boosting (SSA-XGB) yielded the best overall prediction performance with R2 values of 0.9930 and 0.9576, VAF values of 99.30 and 95.79, MAE values of 0.52 and 2.50, RMSE of 1.34 and 3.31 for the training and testing sets, respectively. The remaining five prediction methods yield promising results. Therefore, the developed hybrid XGB model can be introduced as an accurate and fast technique for the performance prediction of green concrete. Finally, the developed SSA-XGB considered the effects of all the input factors on the compressive strength. The ability of the model to predict the performance of concrete with unknown proportions can play a significant role in accelerating the development and application of sustainable concrete and furthering a sustainable economy.

Keywords： sustainable concrete     fly ash     slay     extreme gradient boosting technique     squirrel search algorithm     parametric    

HTML PDF Collect

A two-stage parametric subspace model for efficient contrast-preserving decolorization Article

Hong-yang LU, Qie-gen LIU, Yu-hao WANG, Xiao-hua DENG

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 11,   Pages 1874-1882 doi: 10.1631/FITEE.1600017

Abstract: The RGB2GRAY conversion model is the most popular and classical tool for image decolorization. A recent study showed that adapting the three weighting parameters in this first-order linear model with a discrete searching solver has a great potential in its conversion ability. In this paper, we present a two-step strategy to efficiently extend the parameter searching solver to a two-order multivariance polynomial model, as a sum of three subspaces. We show that the first subspace in the two-order model is the most important and the second one can be seen as a refinement. In the first stage of our model, the gradient correlation similarity (Gcs) measure is used on the first subspace to obtain an immediate grayed image. Then, Gcs is applied again to select the optimal result from the immediate grayed image plus the second subspace-induced candidate images. Experimental results show the advantages of the proposed approach in terms of quantitative evaluation, qualitative evaluation, and algorithm complexity.

Keywords： Color-to-gray conversion     Subspace modeling     Two-order polynomial model     Gradient correlation similarity    

HTML PDF Collect

Title Author Date Type Operation

Building information modeling based on intelligent parametric technology

ZENG Xudong, TAN Jie

Journal Article

An efficient prediction framework for multi-parametric yield analysis under parameter variations

Xin LI,Jin SUN,Fu XIAO

Journal Article

Modeling of bentonite/sepiolite plastic concrete compressive strength using artificial neural network

Ali Reza GHANIZADEH, Hakime ABBASLOU, Amir Tavana AMLASHI, Pourya ALIDOUST

Journal Article

Parametric equations for notch stress concentration factors of rib–deck welds under bending loading

Journal Article

Seismic fragility curves for structures using non-parametric representations

Chu MAI, Katerina KONAKLI, Bruno SUDRET

Journal Article

Parametric oscillation of cables and aerodynamic effect

Yong XIA, Jing ZHANG, Youlin XU, Yozo FUJINO,

Journal Article

Parametric study on seismic performance of self-centering reinforced concrete column with bottom-placed

Journal Article

Nonlinear Model-Based Process Operation under Uncertainty Using Exact Parametric Programming

Vassilis M. Charitopoulos,Lazaros G. Papageorgiou,Vivek Dua

Journal Article

Parametric study of hexagonal castellated beams in post-tensioned self-centering steel connections

Hassan ABEDI SARVESTANI

Journal Article

Parametric control of structural responses using an optimal passive tuned mass damper under stationary

Min-Ho CHEY, Jae-Ung KIM

Journal Article

difference analysis of shallow sprayed concrete tunnels crossing a reverse fault or a normal fault: A parametric

Masoud RANJBARNIA, Milad ZAHERI, Daniel DIAS

Journal Article

Predicting lateral displacement caused by seismic liquefaction and performing parametric sensitivity

Nima PIRHADI, Xiaowei TANG, Qing YANG, Afshin ASADI, Hazem Samih MOHAMED

Journal Article

Effect of variable heat capacities on performance of an irreversible Miller heat engine

Xingmei YE

Journal Article

Compressive strength prediction and optimization design of sustainable concrete based on squirrel search algorithm-extreme gradient boosting technique

Journal Article

A two-stage parametric subspace model for efficient contrast-preserving decolorization

Hong-yang LU, Qie-gen LIU, Yu-hao WANG, Xiao-hua DENG

Journal Article