Integrating response surface methodology and finite element analysis for model updating and damage assessment of multi-arch gallery masonry bridges

Shimpi, Vinay; Sivasubramanian, Madappa V R; Singh, S B

doi:10.1007/s12046-023-02363-1

Cited by 2 publications

(3 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The second part introduces the principle of a BNN and the algorithm for predicting the displacement response of bridge structures, and the third part proposes a method for evaluating the performance of the two driving methods. The main contents of this section are shown in Figure 1, with reference to [20,35,36].…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, an efficient method is needed to estimate the output values of complex problems. Response Surface Methodology (RSM) [36] is a technique in which an approximate model of a complex system is constructed to estimate the output values, significantly reducing computational costs. This method can generate responses based on given inputs and be used to perform model prediction and optimization.…”

Section: Fem Updatementioning

confidence: 99%

See 1 more Smart Citation

Application of Response Surface-Corrected Finite Element Model and Bayesian Neural Networks to Predict the Dynamic Response of Forth Road Bridges under Strong Winds

Liu,

Meng,

et al. 2024

Sensors

View full text Add to dashboard Cite

With the rapid development of big data, the Internet of Things (IoT), and other technological advancements, digital twin (DT) technology is increasingly being applied to the field of bridge structural health monitoring. Achieving the precise implementation of DT relies significantly on a dual-drive approach, combining the influence of both physical model-driven and data-driven methodologies. In this paper, two methods are proposed to predict the displacement and dynamic response of structures under strong winds, namely, a Bayesian Neural Network (BNN) model based on Bayesian inference and a finite element model (FEM) method modified based on genetic algorithms (GAs) and multi-objective optimization (MOO) using response surface methodology (RSM). The characteristics of these approaches in predicting the dynamic response of large-span bridges are explored, and a comparative analysis is conducted to evaluate their differences in computational accuracy, efficiency, model complexity, interpretability, and comprehensiveness. The characteristics of the two methods were evaluated using data collected on the Forth Road Bridge (FRB) as an example under unusual weather conditions with strong wind action. This work proposes a dual-driven approach, integrating machine learning and FEM with GNSS and Earth Observation for Structural Health Monitoring (GeoSHM), to bridge the gap in the limited application of dual-driven methods primarily applied for small- and medium-sized bridges to large-span bridge structures. The research results show that the BNN model achieved higher R2 values for predicting the Y and Z displacements (0.9073 and 0.7969, respectively) compared to the FEM model (0.6167 and 0.6283). The BNN model exhibited significantly faster computation, taking only 20 s, while the FEM model required 5 h. However, the physical model provided higher interpretability and the ability to predict the dynamic response of the entire structure. These findings help to promote the further integration of these two approaches to obtain an accurate and comprehensive dual-driven approach for predicting the structural dynamic response of large-span bridge structures affected by strong wind loading.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Fem Updatementioning

confidence: 99%

Application of Response Surface-Corrected Finite Element Model and Bayesian Neural Networks to Predict the Dynamic Response of Forth Road Bridges under Strong Winds

Liu,

Meng,

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…( 5) and Eq. ( 6), respectively [19,20]. R 2 represents the amount of dispersion explained by the RS model around the mean [21,22].…”

Section: Rsm For Structural Damage Identificationmentioning

confidence: 99%

Model Updating for Damage Identification: Leveraging Response Surface Methodology and Frequency Response Function Curvature

Nur Raihana Sukri,

Nurulakmar Abu Husain,

Syarifah Zyurina Nordin

et al. 2024

ARAM

View full text Add to dashboard Cite

The limitations of the classical finite element (FE) model updating-based damage identification method, described as having convergence problems and a high computing cost, served as a catalyst for this investigation. Unlike traditional FE models, response surface methodology (RSM) employs explicit mathematical functions to describe the input-response relationship, offering a clear and concise representation. However, the current application of RSM is restricted to a few responses and updating parameters, and unresolved issues persist, including symmetrical damage location and false damage detection. In this study, a new RSM approach is introduced, using FRF curvature as the response. The effectiveness of the proposed method is demonstrated through an experimental modal analysis of a free-free aluminium beam, employing four different Design of Experiment (DOE) techniques: a minimum-run resolution V (CCDmrv) design and a half-fractional (CCDhalf) design using Central Composite Design (CCD), Box-Behnken design (BBD), and D-optimal design. The research systematically evaluates and compares the performance of these DOE techniques in identifying damage. Overall, the results highlight the success of the RSM method, particularly CCDhalf, D-optimal and BBD, in effectively identifying damage.

show abstract

Integrating response surface methodology and finite element analysis for model updating and damage assessment of multi-arch gallery masonry bridges

Cited by 2 publications

References 44 publications

Application of Response Surface-Corrected Finite Element Model and Bayesian Neural Networks to Predict the Dynamic Response of Forth Road Bridges under Strong Winds

Application of Response Surface-Corrected Finite Element Model and Bayesian Neural Networks to Predict the Dynamic Response of Forth Road Bridges under Strong Winds

Model Updating for Damage Identification: Leveraging Response Surface Methodology and Frequency Response Function Curvature

Contact Info

Product

Resources

About