Researching a Fuzzy- and Performance-Based Optimization Method for the Life-Cycle Cost of SRHPC Frame Structures

Zheng, Jie; Dong, Lei; Zheng, Shansuo; Zeng, Lei; Xiang, Ze-hui; He, Wei

doi:10.3390/app7030269

Cited by 5 publications

(3 citation statements)

References 17 publications

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“…Each of these conventional optimization techniques has advantages and disadvantages. The problem's complexity, the kind of design variables and constraints, and the available computer resources all influence the solution that is chosen [30,31]. The exponential increase in design parameters with an increase in the complexity of RC framed structures makes it computationally tough and impractical to use heuristic optimization techniques every time for new problems [27].…”

Section: *Author For Correspondencementioning

confidence: 99%

Transformative role of machine learning in design optimization of reinforced concrete frames

2024

IJATEE

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Design optimization in reinforced concrete (RC) frames is crucial for achieving more efficient, cost-effective, and safe constructions. This involves determining optimal section sizes and reinforcing schemes to enhance structural performance, reduce construction expenses, and conserve natural resources. An optimized design contributes to streamlined construction procedures, shorter construction times, and improved overall sustainability. This paper critically evaluates the current literature on machine learning (ML) applications in the design optimization of RC structures, focusing on the period from 1997 to 2023. The study employs a comprehensive methodology, including bibliometric analysis, to analyze trends, research collaborations, and publication patterns. The results highlight the increasing interest in ML for RC frame optimization. Key applications of ML in design optimization include material characterization, design exploration, optimization algorithms, sensitivity analysis, predictive modeling, structural health monitoring, design code compliance, uncertainty quantification, data-driven design decisions, and design collaboration. The study identifies significant ML algorithms used in optimization, such as the trial-and-error method, linear programming (LP), and nonlinear programming (NLP). Overall, the paper provides insights into the evolving landscape of ML applications in RC frame design, emphasizing the potential for interdisciplinary collaboration and future research directions.

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Section: *Author For Correspondencementioning

confidence: 99%

Transformative role of machine learning in design optimization of reinforced concrete frames

2024

IJATEE

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“…Due to their high bearing capacity and good seismic performance such as energy dissipation and ductility, steel reinforcement concrete (SRC) members, especially frame columns, are widely used in high-rise and super high-rise hybrid structures [1][2][3][4]. To ensure that the seismic performance of the structure meets the design requirements, the engineering designer must apply a strict elastoplastic calculation analysis of the structures.…”

Section: Introductionmentioning

confidence: 99%

Hysteretic Behavior of Steel Reinforced Concrete Columns Based on Damage Analysis

et al. 2019

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With the aim to model the seismic behavior of steel reinforced concrete (SRC) frame columns, in this research, hysteresis and skeleton curves were obtained based on the damage test results of SRC frame columns under low cyclic repeat loading and the hysteretic behavior of the frame columns was further analyzed. Then, the skeleton curve and hysteresis loops were further simplified. The simplified skeleton curve model was obtained through the corresponding feature points obtained by mechanical and regression analysis. The nonlinear combination seismic damage index, which was developed by the test results and can well reflect the effect of the loading path and the number of loading cycle of SRC frame columns, was used to establish the cyclic degradation index. The strength and stiffness degradation rule of the SRC frame columns was analyzed further by considering the effect of the accumulated damage caused by an earthquake. Finally, the hysteresis model of the SRC frame columns was established, and the specific hysteresis rules were given. The validity of the developed hysteresis model was verified by e comparison between the calculated results and the test results. The results showed that the model could describe the hysteresis characteristics of the SRC frame columns under cyclic loading and provide guidance for the elastoplastic time-history analysis of these structures.

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“…Popular terms such as Internet of Things and smart structures were coined as a result of the intersection between advances in other engineering disciplines with civil engineering to produce the new field of structural health monitoring (SHM). The field of SHM is now at a vital crossroads, where researchers are challenged to develop technologies for the monitoring and retrofit of older buildings and at the same time to push the boundaries of SHM through the creative use of cutting-edge technologies and data processing algorithms.This issue is a snapshot of the newest research in SHM for civil structures, and it includes a range of topics such as data processing algorithms to detect damage, modeling, and simulation [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]; sensor development and experiments [16][17][18][19][20][21][22][23][24][25][26]; materials studies [27,28]; state-of-the-art reviews [29,30]; and case studies [31]. SHM is highly multi-disciplinary, and advances in other areas of study can likely be recruited for the progress of SHM.…”

mentioning

confidence: 99%

Structural Health Monitoring (SHM) of Civil Structures

2017

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As newer and more reliable ways of construction were developed, civilization began to spread out further and retain functional infrastructure for longer periods of time. Key building materials such as concrete ushered in a new era of civil engineering that enabled the rapid and low-cost construction of infrastructure that now serves as the backbone of modern society. Many of such buildings constructed in the 19th to the mid-20th century are still in operation today as a testament to the robustness of the civil structures enabled by the key building materials and methods. However, robustness has its limits, and the extended service life has inevitably led to the dangerous accumulation of damage in the infrastructure. Aging infrastructure is a problem met around the globe, and in the US, the National Academy of Engineering proclaimed the restoration and improvement of urban infrastructure to be one of the Grand Challenges of the 21st century.For the answer to the challenge of aging infrastructure, we look towards the development of advanced sensor and actuator technologies that hold the promise of heralding the next stage of evolution in civil infrastructure. Popular terms such as Internet of Things and smart structures were coined as a result of the intersection between advances in other engineering disciplines with civil engineering to produce the new field of structural health monitoring (SHM). The field of SHM is now at a vital crossroads, where researchers are challenged to develop technologies for the monitoring and retrofit of older buildings and at the same time to push the boundaries of SHM through the creative use of cutting-edge technologies and data processing algorithms.This issue is a snapshot of the newest research in SHM for civil structures, and it includes a range of topics such as data processing algorithms to detect damage, modeling, and simulation [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]; sensor development and experiments [16][17][18][19][20][21][22][23][24][25][26]; materials studies [27,28]; state-of-the-art reviews [29,30]; and case studies [31]. SHM is highly multi-disciplinary, and advances in other areas of study can likely be recruited for the progress of SHM. The future of this field is very bright, and will be a driver for the coming futuristic, intelligent infrastructure.

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Researching a Fuzzy- and Performance-Based Optimization Method for the Life-Cycle Cost of SRHPC Frame Structures

Cited by 5 publications

References 17 publications

Transformative role of machine learning in design optimization of reinforced concrete frames

Transformative role of machine learning in design optimization of reinforced concrete frames

Hysteretic Behavior of Steel Reinforced Concrete Columns Based on Damage Analysis

Structural Health Monitoring (SHM) of Civil Structures

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