Modeling of temperature distribution in a reinforced concrete supertall structure based on structural health monitoring data

Ni, Yiqing; Zhang, P.; Ye, X. W.; Lin, K. C.; Liao, Wei

doi:10.12989/cac.2011.8.3.293

Cited by 11 publications

(11 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…7. Determine whether damage occurred in all the bridges belonging to the same class based on equation (33).…”

Section: Calculation Flow Of the Proposed Algorithmmentioning

confidence: 99%

“…A bridge is viewed as a structural system with the environmental temperature load as its input and the strain data at different measured points as its output. Usually, the environmental temperature load ξ ( t ) follows different probability distribution depending on different cases such as the normal distribution, 30–35 the Gumbel distribution, 36 the combination of several random distribution 37 or the non-Gaussian distribution 32,38 . For the medium- and small-span bridges, the structural form is simple and the degree of statically indeterminate structure is relatively low.…”

Section: Damage Detection Of Bridges Monitored Within One Cluster Basmentioning

confidence: 99%

See 1 more Smart Citation

Damage detection of bridges monitored within one cluster based on the residual between the cumulative distribution functions of strain monitoring data

Zhang

Liu

2020

Structural Health Monitoring

View full text Add to dashboard Cite

With the structural health monitoring technique, several medium- and small-span bridges possessing the same or similar structural characteristics located in a local road network are simultaneously monitored within one cluster. Nevertheless, insufficient research has been performed on detecting the damage of all the bridges monitored within one cluster under time-varying environmental temperatures. To address this issue, a method is proposed to assess the damage in all the bridges within one cluster utilizing the residual between the cumulative distribution functions of strain monitoring data. First, an algorithm for reconstructing the strain monitoring dataset is introduced, effectively improving the computational efficiency at removing as much measured noise from a large amount of monitoring data as possible. Second, a cluster analysis algorithm considering the similarity among the strain monitoring data at different measured points is proposed to classify the strain monitoring data for all the bridges monitored within one cluster. Different classes of strain monitoring data are established by identifying similar probability distribution patterns. Third, for each class, a damage detection index is proposed based on the residual between the cumulative distribution functions of strain monitoring data. All the bridges monitored within one cluster are acted upon by similar environmental temperatures during the same monitoring period; hence, the effects of the environmental temperature on the proposed index are mitigated indirectly during the same monitoring period. Thus, the proposed index is implemented to effectively detect the damage in all the bridges belonging to each class. Finally, the effectiveness of the proposed method is demonstrated through a numerical simulation and with strain monitoring data obtained from actual bridges.

show abstract

“…7. Determine whether damage occurred in all the bridges belonging to the same class based on equation (33).…”

Section: Calculation Flow Of the Proposed Algorithmmentioning

confidence: 99%

Section: Damage Detection Of Bridges Monitored Within One Cluster Basmentioning

confidence: 99%

Damage detection of bridges monitored within one cluster based on the residual between the cumulative distribution functions of strain monitoring data

Zhang

Liu

2020

Structural Health Monitoring

View full text Add to dashboard Cite

show abstract

“…Integration of field monitoring and numerical thermal analysis facilitates a thorough and quantitative understanding of the temperature behaviours of a supertall structure. Densely distributed temperature and strain sensors have been installed on the Canton Tower (600 m tall), 109 Ping An Finance Centre (600 m) 18 and Shanghai Tower (632 m) 31 . These sensors provide a good opportunity for monitoring structural temperature during the construction and service stages.…”

Section: Temperature Effect Monitoringmentioning

confidence: 99%

Review on field monitoring of high‐rise structures

Xia

Weng

2020

Struct Control Health Monit

View full text Add to dashboard Cite

Structural health monitoring (SHM) has been developed and applied to bridge structures since the early 1980s. Numerous approaches and techniques have been proposed and applied to supertall structures during the recent decade. This paper reviews the SHM techniques and applications in supertall structures. The vibration analysis techniques, seismic effect monitoring, wind effect monitoring, comfort assessment, temperature effect monitoring, and construction monitoring of high-rise structures are described and summarised. The latest developments in codification and standardisation in SHM are also introduced.

show abstract

“…It was until recently that a few studies combined finite element (FE) model analysis and field monitoring data to quantify the temperature effects on supertall structures. Ni et al (2011) developed monitoring-based temperature distribution models of Guangzhou Tower in order to serve as a reliable input for numerical simulation of the temperature-induced deformations. Su et al (2017a) inputted the structural temperature of the 600-m-tall Canton Tower into an FE model of the global structure and calculated the temperature-induced deformation.…”

Section: Introductionmentioning

confidence: 99%

Temperature-induced displacement of supertall structures: A case study

Hou

Xia

et al. 2018

Advances in Structural Engineering

View full text Add to dashboard Cite

For a supertall structure, the temperature effect is an important factor that must be considered during the design, construction, and performance assessment. In this article, temperature-induced displacement of the 600-m-high Canton Tower is studied by the data-driven approach based on the sufficient real measurement data obtained from the structural health monitoring system. A multiple linear regression model is employed to establish the quantitative relation between the displacement and temperature data at different facades and sections of the structure in different seasons. Results show that an ordinary linear regression model is able to fit the monitoring data well. However, the model fails to interpret the physical meaning of the model coefficients. A regularized linear regression model is then employed and validated to describe the temperature-induced displacement of the structure. The physical relationship between the temperature and displacement is provided. Finally, the model is also used to separate the temperature- and wind-induced displacement of the supertall structure.

show abstract

Modeling of temperature distribution in a reinforced concrete supertall structure based on structural health monitoring data

Cited by 11 publications

References 10 publications

Damage detection of bridges monitored within one cluster based on the residual between the cumulative distribution functions of strain monitoring data

Damage detection of bridges monitored within one cluster based on the residual between the cumulative distribution functions of strain monitoring data

Review on field monitoring of high‐rise structures

Temperature-induced displacement of supertall structures: A case study

Contact Info

Product

Resources

About