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Automated long-term dynamic monitoring using hierarchical clustering and adaptive modal tracking: validation and applications

DOI:10.1007/s13349-018-0306-3
Authors:
Giacomo Zonno at Politecnico di Milano
Giacomo Zonno
Rafael Aguilar at Pontifical Catholic University of Peru
Rafael Aguilar
Rubén Boroschek at University of Chile
Rubén Boroschek
Paulo B. Lourenco at University of Minho
Paulo B. Lourenco
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Abstract and Figures

Historical buildings demand constant surveying because anthropogenic (e.g., use, pollution or traffic vibration) and natural or environmental hazards (e.g., environmental changes or earthquakes) can endanger their existence and safety. Particularly, in the Andean region of South America, earthen historical constructions require special attention and investigation due to the high seismic hazard of the area next to the Pacific coast. Structural Health Monitoring (SHM) can provide useful, real-time information on the condition of these buildings. In SHM, the implementation of automatic tools for feature extraction of modal parameters is a crucial step. This paper proposes a methodology for the automatic identification of the structural modal parameters. An innovative and multi-stage approach for the automatic dynamic monitoring is presented. This approach uses the Data-Driven Stochastic Subspace Identification method complemented by hierarchical clustering for automatic detection of the modal parameters, as well as an adaptive modal tracking procedure for providing a clear visualization of long-term monitoring results. The proposed methodology is first validated in data acquired in an emblematic sixteenth century historical building: the monastery of Jeronimos in Portugal. After proving its efficiency, the algorithm is used to process almost 5000 events containing data acquired in the church of Andahuaylillas, a sixteenth century adobe building located in Cusco, Peru. The results in these cases demonstrate that accurate estimation of predominant modal parameters is possible in those complex structures even if relatively few sensors are installed.
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ORIGINAL PAPER
Automated long-term dynamic monitoring using hierarchical
clustering and adaptive modal tracking: validation and applications
Giacomo Zonno
1
•Rafael Aguilar
1
•Rube
´n Boroschek
2
•Paulo B. Lourenço
3
Received: 12 July 2018 / Accepted: 5 September 2018 / Published online: 19 September 2018
ÓSpringer-Verlag GmbH Germany, part of Springer Nature 2018
Abstract
Historical buildings demand constant surveying because anthropogenic (e.g., use, pollution or traffic vibration) and natural
or environmental hazards (e.g., environmental changes or earthquakes) can endanger their existence and safety. Particu-
larly, in the Andean region of South America, earthen historical constructions require special attention and investigation
due to the high seismic hazard of the area next to the Pacific coast. Structural Health Monitoring (SHM) can provide useful,
real-time information on the condition of these buildings. In SHM, the implementation of automatic tools for feature
extraction of modal parameters is a crucial step. This paper proposes a methodology for the automatic identification of the
structural modal parameters. An innovative and multi-stage approach for the automatic dynamic monitoring is presented.
This approach uses the Data-Driven Stochastic Subspace Identification method complemented by hierarchical clustering
for automatic detection of the modal parameters, as well as an adaptive modal tracking procedure for providing a clear
visualization of long-term monitoring results. The proposed methodology is first validated in data acquired in an
emblematic sixteenth century historical building: the monastery of Jeronimos in Portugal. After proving its efficiency, the
algorithm is used to process almost 5000 events containing data acquired in the church of Andahuaylillas, a sixteenth
century adobe building located in Cusco, Peru. The results in these cases demonstrate that accurate estimation of pre-
dominant modal parameters is possible in those complex structures even if relatively few sensors are installed.
Keywords Historical buildings Andean adobe structures Long-term monitoring Automatic identification
Adaptive modal tracking
1 Introduction
Structural Health Monitoring (SHM) is an area that has
increasingly become of interest to improve the knowledge
of existing structural systems and their seismic perfor-
mance [1–3]. In the case of cultural heritage buildings, an
increment in the use of SHM has been triggered due to the
high complexity of this type of constructions and the dif-
ficulty to quantify long-term variables such as aging of
materials and effects of environmental conditions. There
are several examples of applications of SHM within the
context of conservation of historical constructions such as
studies in churches [4–6], towers [7,8], buildings and
bridges [9–11]. In Latin America and, particularly in Peru,
there is a significant presence of historical earthen con-
structions [12], which evidence high vulnerability due to
issues in the material itself such as its low tensile strength
and brittle behavior [13,14]. These constructions require
special attention and investigation with modern tools which
are capable of overcoming local needs and on-site negative
circumstances (i.e., absence of electricity or internet con-
nection, unfavorable climatic conditions, limited techno-
logical resources, etc.).
Within the available monitoring tools, vibration-based
SHM is considered a suitable and efficient approach since
&Rafael Aguilar
raguilar@pucp.pe
1
Department of Engineering, Pontificia Universidad Cato
´lica
del Peru
´-PUCP, Av. Universitaria 1801, San Miguel,
Lima 32, Peru
2
Department of Civil Engineering, University of Chile, Av.
Blanco Encalada 2002, Santiago, Regio
´n Metropolitana,
Chile
3
Department of Civil Engineering, University of Minho,
ISISE, Campus de Azure
´m, 4800-058 Guimara
˜es, Portugal
123
Journal of Civil Structural Health Monitoring (2018) 8:791–808
https://doi.org/10.1007/s13349-018-0306-3(0123456789().,-volV)(0123456789().,-volV)
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
... Structural health monitoring systems have also significantly increased interest in monitoring cultural heritage structures [21][22][23][24][25][26]. For instance, the studies done in [21] show the application of a WSN for monitoring the structural health of a historic masonry tower. ...
... This study also projects their ideas toward the importance of usage and problems with big data in this field. In addition, the study of [24] also shows an interesting application of an SHM on two emblematic historical constructions: a masonry church in Portugal and an adobe church in Perú. This study proposes a methodology for the automatic identification of the structural modal parameters, the process used contains four stages: data acquisition, system identification using the SSI-data method, a cleaning stage of the signal with criteria, and finally, an automatic detection using hierarchical clustering. ...
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View
... In the case of the Andahuaylillas church, it presents a structure supported on large adobe masonry walls, with variable thickness ranging from 1.1 to 2.0 m and an average height of 10 m for the area of the main nave and 12 m for the presbytery area (Zonno et al. 2018). The longitudinal walls of the nave of the church are connected to each other by the roof system, which is composed by an arrangement of "A-shape" trusses of timber elements (Zonno et al. 2019c). ...
... The system was configured to obtain modal properties of the structure every 60 min. The complete description of the data processing can be found in Zonno et al. (2018). ...
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In Structural Health Monitoring understanding the effect of the environment on the modal properties of structures is essential. In this study, we evaluate the influence of changes in ambient conditions on the natural frequencies of three real large-scale structures: A 9-story reinforced concrete (RC) building and two sixteenth century churches of adobe masonry. For these structures, both environmental parameters (ambient temperature and relative humidity) and modal parameters were continuously monitored for a combined period of 8-years (RC building) and 2-years (for each of the adobe churches) in order to assess the effect of daily and seasonal variations of environmental parameters on the modal response. The results of the seasonal comparison indicate a negative correlation between ambient temperature and natural frequencies for the concrete structure, while for adobe structures a positive correlation between humidity and natural frequencies was observed. For daily variations, an out-of-phase and lag response of natural frequencies to variations in the environment was observed. A change on the daily lag was observed depending on the time of the year. Both seasonal and daily comparisons show the existence of a strong seasonality in frequency variations, where the form of response of this to environmental effects is different depending on the season. The magnitude of the daily variability is smaller in comparison to the seasonal variations. Finally, it was determined that the variation in natural frequencies depends on several factors such as the predominant material of the structure (namely reinforced concrete and adobe), the type of environmental exposure (temperature or humidity), and the characteristics of the structure (dimension of main elements).
View
... The implemented monitoring systems are summarized in Fig. 2, and consist: (i) local acquisition and storage of the raw data (dynamic and environmental data), (ii) transmission of the raw data by a 4g data plan to the central monitoring station, (iii) reception and storage of data, (iv) processing of raw data, and (v) publishing the results in a web platform using a cloud engine. For the processing of the dynamic raw data (stage iv of the monitoring system), an automatic processing tool was developed and tested in [14]. In particular, the developed tool is able to identify automatically the frequencies, mode shapes and damping values of the structure through four main steps: (a) digital signal pre-processing of the dynamic data; (b) application of the SSI-Data method to obtain the stabilization diagram; (c) filtering of the stabilization diagram with the application of hard and soft validation criteria; (c) automatic detection of the modal parameters using hierarchical clustering approach and automatic thresholds; and (d) the Bell tower application of an adaptive modal tracking for a final cleaning of the dynamic results (see more details in [14]). ...
... For the processing of the dynamic raw data (stage iv of the monitoring system), an automatic processing tool was developed and tested in [14]. In particular, the developed tool is able to identify automatically the frequencies, mode shapes and damping values of the structure through four main steps: (a) digital signal pre-processing of the dynamic data; (b) application of the SSI-Data method to obtain the stabilization diagram; (c) filtering of the stabilization diagram with the application of hard and soft validation criteria; (c) automatic detection of the modal parameters using hierarchical clustering approach and automatic thresholds; and (d) the Bell tower application of an adaptive modal tracking for a final cleaning of the dynamic results (see more details in [14]). Within this context, the implemented dynamic monitoring system consists of a Kinemetrics Obsidian 8x [15], a data acquisition unit with a capacity of 8-channels and 24 bits of resolution ( Fig. 3f) and four uniaxial force balance accelerometers Episensor ES-U2 [16] with a bandwidth range from DC to 200 Hz, a dynamic range of 155 dB+, a sensitivity of 10 V/g, and an operating temperature range from −20 °C to 70 °C (Fig. 3c). ...
View
... For instance, the clustering-based approach proposed by Reynders et al. [7] for the automated interpretation of the SD consists of the following steps: (i) a pre-cleaning stage by means of a classification of all the identified modes as possible physically or certainly spurious; (ii) hierarchical clustering of the possible physical modes for the automatic detection of vertical lines in the SD; (iii) final classification of the formed clusters. Some applications of the hierarchical clustering method for the automatic interpretation of the SD have been reported by Magalhaes et al. [6], de Almeida Cardoso et al. [16], Zonno et al. [27], and Garcia-Macias and Ubertini [28], among the others. Zini et al. [11] also proposed a statistical approach to define the cut-off threshold in the hierarchical clustering technique. ...
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... Particularly in the last few years, many eforts have been made to accomplish automatic SSI-based monitoring. Zonno et al. [33] proposed a multistage methodology combining the data-driven SSI together with the techniques of hierarchical clustering and adaptive modal tracking for the automatic modal parameter identifcation of historical buildings in Portugal and Peru. Li et al. [34] developed a three-stage automated algorithm by incorporating the second-order blind identifcation with the covariance-driven SSI to conduct continuous monitoring for a concrete arch gravity dam in China. ...
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View
... Most of the automation algorithms involve different types of clustering strategies to recognize modes with similar features. Typical strategies include hierarchical clustering, partitioning method and histogram analysis [15,16]. Among these automation methods, the SSIbased automation that is implemented by automatically interpreting stabilization diagram and diverse clustering strategies is frequently used due to its simple mathematical nature and high identification accuracy. ...
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... The reliability of automated OMA is dependent on how well the spurious poles are eliminated so that the stabilized poles are grouped into physical modes without user intervention. This study used a soft-validation procedure to secure stable poles with repeated modal identifications by increasing the model order (the eigenvalue number in the model) [41,42]. The spurious poles were eliminated by comparing the Euclidian distances of the same poles between the kth and (k + 1)th model orders: ...
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The influence of environmental parameters on the dynamic behaviour of the San Frediano bell tower in Lucca
Article
This paper aims at assessing the influence of environmental parameters on the modal characteristics of age–old masonry constructions. The results of a long–term ambient vibration monitoring of the San Frediano bell tower in Lucca (Italy) are reported. The tower, dating back to the 11th century, has been fitted along its height with four triaxial seismometric stations, which were left active for about one year. Data from the monitoring system have been processed via the Stochastic Subspace Identification Method in order to identify the tower’s modal characteristics and their variations over the year. The dependence of the tower’s frequencies on the ambient temperature was first studied and simulated via simple auto–regressive models. Then, some output–only models based on the principal component analysis (PCA) were applied, under the hypotheses of both linear and nonlinear (Kernel PCA) dependence of the natural frequencies on the unknown environmental parameters. The results indicate PCA to be an effective tool for detecting changes in the dynamic characteristics of masonry constructions.
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From continuous vibration monitoring to FEM-based damage assessment: Application on a stone-masonry tower
Article
The paper presents a damage assessment strategy suitable to historic masonry towers. The methodology is exemplified using the data collected in the continuous dynamic monitoring of the San Vittore bell-tower (Arcisate, Northern Italy). The proposed damage assessment procedure aims not only at detecting the occurrence of structural anomalies, but also at localising the damage in the investigated structure. After a brief description of the tower and past diagnostic survey (including ambient vibration tests and Finite Element modelling), the results of the continuous dynamic monitoring are highlighted and the effect of temperature on automatically identified resonant frequencies is discussed. Subsequently, regression models based on Principal Component Analysis are applied in order to filter out the fluctuations caused by the environmental effects on the identified resonant frequencies. The damage detection and damage localisation issues are then addressed by using novelty analysis tools. The effectiveness of the proposed strategy is demonstrated through the detection and localisation of realistic damage scenarios simulated with the baseline Finite Element model. Specifically, the damage localisation has been tackled by using the “cleaned” modal properties within a continuous Finite Element model updating scheme.
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Fully Automated Operational Modal Analysis using multi-stage clustering
Article
The interest for robust automatic modal parameter extraction techniques has increased significantly over the last years, together with the rising demand for continuous health monitoring of critical infrastructure like bridges, buildings and wind turbine blades. In this study a novel, multi-stage clustering approach for Automated Operational Modal Analysis (AOMA) is introduced. In contrast to existing approaches, the procedure works without any user-provided thresholds, is applicable within large system order ranges, can be used with very small sensor numbers and does not place any limitations on the damping ratio or the complexity of the system under investigation. The approach works with any parametric system identification algorithm that uses the system order n as sole parameter. Here a data-driven Stochastic Subspace Identification (SSI) method is used. Measurements from a wind tunnel investigation with a composite cantilever equipped with Fiber Bragg Grating Sensors (FBGSs) and piezoelectric sensors are used to assess the performance of the algorithm with a highly damped structure and low signal to noise ratio conditions. The proposed method was able to identify all physical system modes in the investigated frequency range from over 1000 individual datasets using FBGSs under challenging signal to noise ratio conditions and under better signal conditions but from only two sensors.
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