Model updating of suspended-dome using artificial neural networks

Guo, Jing; Zhao, Xiaoxu; Guo, Junhua; Yuan, Xiying; Dong, Shilin; Zhang, Xiong

doi:10.1177/1369433217693629

Cited by 12 publications

(7 citation statements)

References 28 publications

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“…Collapse of buildings during earthquakes causes serious casualties and economic losses. Much research has been carried out on recent years carried out by Chinese and foreign experts on the mechanical properties and stability of the string dome structure, such as seismic response and anti-seismic analysis [5][6][7][8], buckling and dynamic response under friction [9], model optimization design based on artificial neural networks [10], elastoplastic dynamic response [11], structural optimization algorithm and design [12][13][14], long-span discontinuous mechanical properties [15], initial geometric defect analysis [16], cable tension estimation [17], static and the dynamic analysis by finite element method [18,19], and thrust line analysis of masonry domes [20]. Gong, S.Y.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Response Analysis of a Multiple Square Loops-String Dome under Seismic Excitation

et al. 2021

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The interaction between multiple loops and string cables complicates the dynamic response of triple square loops-string dome structures under seismic excitation. The internal connection between the multiple square loops-string cables and the grid beams was studies to provide a favorable reference for an anti-seismic structure. With a finite element model of the Fuzhou Strait Olympic Sports Center Gymnasium, established by SAP2000 software, the structural dynamic characteristic parameters were obtained first, and then this study adopted a time-history analysis method to study the internal force response of the cables and the roof grid beams of the multiple square loops-string dome (MSLSD) under three types of seismic array excitation. The influence of two factors, namely the seismic pulse and the near and far seismic fields, on the dynamic response of this structure was analyzed by three groups of different types of seismic excitation (PNF, NNF, PFF). As shown from the results, the first three-order vibration modes were torsional deformations caused by cables, the last five were mainly the overall roof plane vibration and antisymmetric vibration. Under the excitation of the three seismic arrays, the internal force responses of stay cables, square cables in the outer ring and the string cables were largest, while the maximum internal force response of the struts changed with the direction of seismic excitation. The largest internal force response of the roof grid beams occurred in local components such as BX3, BX7 and BY7, and the largest deformation of the beam nodes occurred in JX7, JX12 and JY4. In general, the seismic pulse and the near seismic field weakened the internal force response of the struts and cables but increased the internal force response and deformation of the dome beams, while the near and far seismic fields outweighed the seismic pulse. All the above provides an important reference for structural monitoring and seismic resistance.

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Section: Introductionmentioning

confidence: 99%

Dynamic Response Analysis of a Multiple Square Loops-String Dome under Seismic Excitation

et al. 2021

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“…In terms of structural failure, typical failure modes of reinforced concrete columns such as flexure, flexure-shear, and shear were investigated by Mangalathu et al [50,51] using decision trees (DT), SVM, and ANN. Guo et al [52,53] employed the ANN model for the identification of damage in different structures such as suspended-dome and offshore jacket platforms. Regarding structural uncertainty analysis, various published works by E. Zio should be consulted [54][55][56].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Artificial Intelligence System by Evolutionary Algorithm for Prediction of Axial Capacity of Rectangular Concrete Filled Steel Tubes under Compression

Nguyen

Tran

et al. 2020

Materials

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Concrete filled steel tubes (CFSTs) show advantageous applications in the field of construction, especially for a high axial load capacity. The challenge in using such structure lies in the selection of many parameters constituting CFST, which necessitates defining complex relationships between the components and the corresponding properties. The axial capacity (P u ) of CFST is among the most important mechanical properties. In this study, the possibility of using a feedforward neural network (FNN) to predict P u was investigated. Furthermore, an evolutionary optimization algorithm, namely invasive weed optimization (IWO), was used for tuning and optimizing the FNN weights and biases to construct a hybrid FNN-IWO model and improve its prediction performance. The results showed that the FNN-IWO algorithm is an excellent predictor of P u , with a value of R 2 of up to 0.979. The advantage of FNN-IWO was also pointed out with the gains in accuracy of 47.9%, 49.2%, and 6.5% for root mean square error (RMSE), mean absolute error (MAE), and R 2 , respectively, compared with simulation using the single FNN. Finally, the performance in predicting the P u in the function of structural parameters such as depth/width ratio, thickness of steel tube, yield stress of steel, concrete compressive strength, and slenderness ratio was investigated and discussed.Materials 2020, 13, 1205 2 of 25 larger energy absorption capacity [7], convenient construction [11], economy of materials [12][13][14], and excellent seismic and refractory performance [15]. In particular, this type of structure can reduce the environmental burden by removing formwork [16], reusing steel pipes, and using high quality concrete with recycled aggregate [17]. The characteristics of CFST are that the steel material is located far from the central axis so the rigidity of the column is very large, and thus it also contributes to increasing the moment of inertia of the structure [5,18]. The ideal form of concrete core works against the compressive load and hinders the local buckling state of the steel pipe. Therefore, the CFST structures are often used in locations subject to large compressive loads [9,15,19]. The CFST columns are mainly divided into square columns, round columns, and rectangular columns, based on different cross-sectional forms [15]. In particular, the square and rectangular CFST columns have the advantage of easy connection and reliable work with other structural members such as beams, walls, and panels [20]. Compared with square CFST columns, rectangular columns have irregular bending stiffness along different axes, so this type of column is suitable for the mechanical behavior of members including arch ribs, pillars, abutments, and piers, and other structural members under load actions vary greatly from vertical to horizontal [6]. Because the scope of application of rectangular CFST columns is quite wide and this column is mainly subjected to compression, the main purpose of the paper is to analyze and evaluate the ultimate bearing capacity of...

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“…Mangalathu et al [8,9] considered various machine learning techniques to identify the failure modes of beam-column joints and to estimate the shear strength. Guo et al [10] applied artificial neural networks (ANNs) to examine the model updating of a suspended dome considering both experimental and numerical models. In another work, Guo et al [11] proposed using partial modal results in the damage identification of offshore jackets and found that the prediction errors are sensitive to damage locations.…”

Section: Introductionmentioning

confidence: 99%

Damage identification of a jacket support structure for offshore wind turbines

Jiang

Bjornholm

Guo

et al. 2020

2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA)

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Offshore jacket structures are regarded as a suitable type of support structure for offshore wind turbines in immediate water depths. Because of the welded tubular members used and environmental conditions, offshore jackets are often subjected to fatigue damages during their service life. Underwater sensors can provide measurements of the structural vibration signals and provide an efficient way to detect damages at early stages. In this work, simplified forms of the damages are assumed, random damages are imposed on the jacket structure, and damaged indicators are established from combination of modal shapes. Then, a response surface is constructed mapping the damage indicators and damages. Given that the efficiency of the damage identification depends on the locations of the damages and the location and number of sensor locations, a sensitivity study is performed to vary sensor location, sensor quantity, and damage severity. It is found that the effect of damage identification is better when sensor locations are closer to damage locations, and this effect is more sensitive to sensor placements when damages occur in the upper structures. Additionally, the identification effect is more sensitive to damage severity than to occurrence of multiple damages.

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Model updating of suspended-dome using artificial neural networks

Cited by 12 publications

References 28 publications

Dynamic Response Analysis of a Multiple Square Loops-String Dome under Seismic Excitation

Dynamic Response Analysis of a Multiple Square Loops-String Dome under Seismic Excitation

Optimization of Artificial Intelligence System by Evolutionary Algorithm for Prediction of Axial Capacity of Rectangular Concrete Filled Steel Tubes under Compression

Damage identification of a jacket support structure for offshore wind turbines

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