Seismic Safety Assessment of Mixed Timber-Masonry Historical Building: An Example in Lima, Peru

Barontini, Alberto; Lourenço, Paulo B.

doi:10.1080/13632469.2018.1540368

Cited by 12 publications

(2 citation statements)

References 8 publications

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“…On the other hand, the rapid development of modern cities, the emergence of high-rise buildings, and urban metros have caused varying degrees of impact on these old buildings in the vicinity, all of which are potential safety hazards. Structural safety monitoring technology has a prominent role to play in monitoring and maintaining the safety of buildings [5][6][7][8][9]. erefore, installing structural monitoring systems on historic buildings to predict their safety state is an e ective technical tool.…”

Section: Introductionmentioning

confidence: 99%

Research on a Machine Learning-Based Method for Assessing the Safety State of Historic Buildings

Peng

Zhang

2022

Computational Intelligence and Neuroscience

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Historic and protected buildings are increasingly valued due to their valuable historical and cultural value. The assessment of the safety state of historic buildings has received more attention. Emerging machine learning algorithms, with their excellent computational performance, provide new ideas and new means to solve practical problems in various fields. Therefore, this paper proposes a method for assessing the safety state of historic buildings based on machine learning techniques. Firstly, based on the analysis of the characteristics of historical buildings and common security problems, the application of wireless sensor networks to the security monitoring of historical buildings is proposed in order to improve the automation of monitoring. Then, in order to improve the accuracy of the assessment, a combination of kernel canonical correlation analysis (KCCA) and support vector machine (SVM) is used to establish the security monitoring model. The experimental results show that by choosing a suitable KCCA function, the redundant features of the data can be reduced while the comprehensiveness of the building structure identification features can be retained, thus effectively improving the prediction accuracy of the SVM. The KCCA-SVM model can accurately predict the physical quantities such as relative structural displacement of historical buildings with good reliability.

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Section: Introductionmentioning

confidence: 99%

Research on a Machine Learning-Based Method for Assessing the Safety State of Historic Buildings

Peng

Zhang

2022

Computational Intelligence and Neuroscience

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“…In this test method, the dynamic parameters such as natural frequencies, mode shapes and damping ratios are estimated from output-only responses. There are many studies using the OMA technique to identify the dynamic parameters of masonry constructions [5][6][7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Identification of an Early 20th Century Civil Architectural Building

Bağbancı

2020

Civ Eng J

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Historical structures are important in terms of both original construction techniques and cultural heritage. Therefore, material properties, construction techniques and dynamic behaviors of these structures must be identified in order to preserve them in the future by restoration studies. This study is aimed to serve as an example for similar buildings in the region whose walls were constructed using filled brick with lime mortar and constructed using both timber and reinforced concrete slabs. In this study, the plan layout, construction techniques and the material usage of the building were investigated in detail. The mechanical and dynamic properties such as compressive stress, elastic moduli, shear stress, natural frequencies and mode shapes of the building were determined in-situ by flat-jack, shear and vibration tests. The finite element model of the structure was prepared, and the modal analysis of the structure was performed. The calibration of the model was ensured according to the vibration test results. The results obtained from this study show us that in-situ tests are extremely important for the accuracy of finite element models. It has been determined that the mechanical test data can be used with over 80% success in finite element models.

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Research Developments and Needs on Seismic Performance and Strengthening of Adobe Masonry Constructions

Parisi

Tarque

Varum

2021

Structural Characterization and Seismic Retrofitting of Adobe Constructions

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Seismic Safety Assessment of Mixed Timber-Masonry Historical Building: An Example in Lima, Peru

Cited by 12 publications

References 8 publications

Research on a Machine Learning-Based Method for Assessing the Safety State of Historic Buildings

Research on a Machine Learning-Based Method for Assessing the Safety State of Historic Buildings

Dynamic Identification of an Early 20th Century Civil Architectural Building

Research Developments and Needs on Seismic Performance and Strengthening of Adobe Masonry Constructions

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