Automated data analysis for static structural health monitoring of masonry heritage structures

Makoond, Nirvan; Pelà, Luca; Molins, Climent; Roca, Pere; Alarcón, Daniel

doi:10.1002/stc.2581

Cited by 29 publications

(36 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Before identifying the parameters of any model to represent the dependence of a structural response on environmental parameters, it is important to identify which environmental parameters are more suitable to be used as predictors in the model. This can be achieved by computing the Pearson correlation coefficient (R) between measured environmental and structural parameters [4]. This coefficient can vary between -1 and +1 with absolute values closer to unity indicating a stronger linear correlation.…”

Section: Preliminary Evaluation Of Correlationmentioning

confidence: 99%

“…However, it can be argued that using data from the entire monitoring period would allow the model to better capture the changing nature of the relationship in some cases. Therefore, as recommended in [4], both operations listed below were carried out for each response variable and the results from both methods were compared:…”

Section: Filtering Environmental Effects Through Linear Modelsmentioning

confidence: 99%

“…Multiple-input single output (MISO) ARX models incorporating both interior and exterior temperatures as predictors were then implemented to allow the model to consider effects caused by thermal gradients. The complete procedure incorporating ARX models that was used for the analysis of data from the static SHM system is described in [4] and will not be reiterated in this article. Besides the measurements of the response and of selected predictors, the only other required input to the procedure is the range of model orders to be tested.…”

Section: Filtering Environmental Effects Using Arx Modelsmentioning

confidence: 99%

“…In order to address this issue, a fully automated data analysis procedure incorporating ARX models has recently been proposed [4]. The entire process includes a method to select optimal model orders from a pre-defined range as well as a procedure to classify each monitored response according to their estimated evolutionary state.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Data Analysis Using ARX Models Applied to Static Structural Health Monitoring of the Monastery of Sant Cugat

Makoond¹,

Pelà²,

Borrell³

et al. 2021

12th International Conference on Structural Analysis of Historical Constructions

View full text Add to dashboard Cite

The church of the monastery of Sant Cugat close to Barcelona is a medieval construction characterized by a complex structural behaviour stemming from the interaction among various structural elements built over different periods. Despite having survived for several centuries, such structures are often affected by slow irreversible deterioration mechanisms that can jeopardise their stability in the future. In order to identify such mechanisms at an early stage, and to better understand the cause of visible pathologies, a static structural health monitoring (SHM) system was installed in the church since 2017. Although this monitoring strategy, aimed at the continuous measurement of key slow-varying parameters, has been used successfully in the past to facilitate the diagnosis of this structural typology, the interpretation of data collected by such systems remains a challenging task. One of the main reasons for this is the fact that many monitored damage and deformation features are sensitive to changes caused by environmental conditions. To address this issue, this paper presents the application of a fully automated data analysis procedure to the records collected from the SHM system installed in the church of the monastery of Sant Cugat. The procedure consists of two parts. The first relies on the identification of models that comprehend an Auto-Regressive output and an eXogenous input (ARX) to represent the dynamics of each monitored response using suitable environmental parameters as predictors. The identified models are then used to estimate filtered evolution rates. The second part of the procedure involves classifying each monitored response into pre-defined evolution states based on outcomes from the first part. The main results from the application to the case of the church of the monastery of Sant Cugat are presented and the implications for the diagnosis of the structure are discussed.

show abstract

Section: Preliminary Evaluation Of Correlationmentioning

confidence: 99%

Section: Filtering Environmental Effects Through Linear Modelsmentioning

confidence: 99%

Section: Filtering Environmental Effects Using Arx Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Data Analysis Using ARX Models Applied to Static Structural Health Monitoring of the Monastery of Sant Cugat

Makoond¹,

Pelà²,

Borrell³

et al. 2021

12th International Conference on Structural Analysis of Historical Constructions

View full text Add to dashboard Cite

show abstract

“…8 Furthermore, the application of such models has been facilitated by the recent proposal of a fully automated procedure for identifying irreversible components linked to active deterioration mechanisms from acquired time histories of monitored structural responses in masonry heritage structures. 9 In addition to simplifying the implementation of ARX models, the method proposed in Ref. 9 also comprehends an automatic classification procedure to facilitate the interpretation of analysis results.…”

Section: Introductionmentioning

confidence: 99%

Static structural health monitoring and automated data analysis procedures applied to the diagnosis of a complex medieval masonry monastery

Makoond

Pelà

Molins

et al. 2020

Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2020

Self Cite

View full text Add to dashboard Cite

Static structural health monitoring (SHM), aimed at the continuous measurement of slow-varying parameters over a long period, has been proved to be a powerful tool to support the diagnosis of masonry heritage structures. In such applications, the initial interpretation task involves the identification of evolutionary conditions from recorded data. However, this can be difficult since monitored features are influenced by environmental changes. In addition, many masonry heritage structures are characterised by a complex structural behaviour stemming from the interaction among different elements, making the task of interpreting SHM data for diagnosis very challenging. One such structure is the church of the monastery of Sant Cugat close to Barcelona, built mostly between the 12th and 15th centuries. Certain key structural parameters of the church have been monitored since 2017 with the aim of understanding the cause of visible pathologies and identifying any active deterioration mechanisms that could pose a threat to the structural integrity of the church in the future. This paper presents the application of an automated data analysis methodology to this problem. The method uses dynamic regression models to filter out components related to reversible seasonal fluctuations from measurements and automatically classifies monitored parameters into evolutionary states based on predicted evolution rates and dispersion metrics from the filtering procedure. A tool is presented which allows analysis results to be updated as new data is received. Finally, results from the proposed methodology are used for the diagnosis of the structure and their usefulness in a broader decision-making framework is discussed.

show abstract

A new sensorized ceramic plug for the remote monitoring of moisture in historic masonry walls: First results from laboratory and onsite testing

Franzoni

Bassi

2022

Structural Contr & Hlth

View full text Add to dashboard Cite

The presence of moisture in historic buildings, especially from rising damp, is extremely widespread and severe, causing materials' deterioration, internal discomfort and bad thermal insulation of external walls. Although this phenomenon is widely studied in the literature, the available solutions are frequently only partially effective, also due to the lack of reliable and compatible techniques to monitor the amount of moisture inside porous building materials, especially in heritage buildings where multiple restrictions exist. In this paper, a new sensorized ceramic plug was developed, to be inserted in historic masonry walls for the remote monitoring of moisture. The plug includes a moisture sensor that is currently used for soil irrigation purpose in agriculture and a ceramic envelope of tailored properties. The plug was developed in laboratory following a step-by-step testing program, which took into account both the specific features of the sensor (requiring a strong research effort to be transferred to building materials) and those of historic walls. After a first set of laboratory tests, the sensorized ceramic plug was validated in small-scale laboratory walls and in a real historic masonry in the monumental Certosa cemetery in Bologna, Italy. The results are extremely encouraging, as they show how the new plug can provide valuable information about the rising damp evolution, and in fact, the data were correlated with the changes in the concurrent environmental parameters in the area of the cemetery. The results also suggest some possible measures to improve the sensorized ceramic plug in the near future.

show abstract

Automated data analysis for static structural health monitoring of masonry heritage structures

Cited by 29 publications

References 54 publications

Data Analysis Using ARX Models Applied to Static Structural Health Monitoring of the Monastery of Sant Cugat

Data Analysis Using ARX Models Applied to Static Structural Health Monitoring of the Monastery of Sant Cugat

Static structural health monitoring and automated data analysis procedures applied to the diagnosis of a complex medieval masonry monastery

A new sensorized ceramic plug for the remote monitoring of moisture in historic masonry walls: First results from laboratory and onsite testing

Contact Info

Product

Resources

About