Performance of two model-free data interpretation methods for continuous monitoring of structures under environmental variations

Interpreting measurement data from continuous monitoring of civil structures for structural health monitoring (SHM) is a challenging task. This task is even more difficult when measurement data are influenced by environmental variations, such as temperature, wind and humidity. This paper investigates for the first time the performance of two model-free data interpretation methods: Moving Principal Component Analysis (MPCA) and Robust Regression Analysis (RRA) for monitoring civil structures that are influenced by temperature. The performance of the two methods is evaluated through two criteria: (1) damage detectability and (2) time to detection with respect to two factors: sensor-damage location and traffic loading intensity. Furthermore, the performance is studied in situations with and without filtering seasonal temperature variations through the use of a moving average filter. The study demonstrates that MPCA has higher damage detectability than RRA. RRA, on the other hand, detects damages faster than MPCA. Filtering seasonal temperature variations may reduce the time to detection of MPCA while the benefits are modest for RRA. MPCA and RRA should be considered as complementary methods for continuous monitoring of civil structures.

Les circulations d'eau dans les massifs cristallins alpins et leurs relations avec les ouvrages souterrains

The present study has been achieved within the framework of the scientific program AQUALP, supported by the EVIAN Foundation. Its theme is the study of the hydrogeology of the Alpine crystalline. The AQUALP programme consists of two co-coordinated parts : while the "source" part, lead at the Savoy University, studies natural circulation towards springs, the "work" part, which concerns this study, has focused on the interaction between civil engineering works and underground waters. The approach adopted is that of a field study during which water inflows encountered in various Alpine underground works and replaced in their geological contexts were followed up as from the discharge, the physico-chemical and the isotopic chemistry point of view. A tracer test and numerical simulations of underground flows were also undertaken. Main results The summary of the hydrogeological data in the numerous existing underground works allowed to calculate, on the basis of simple analytical solutions, the distribution of the hydraulically conductivity of the massifs crossed. These results show that the deeper one goes the more the hydraulical conductivity decreases. They bring us to specify the thickness of the decompressed zone, i.e. about 500 m. This zone strongly influences the underground flow in these massifs. Presence of mica inhibits the hydraulical conductivity in conferring a more plastic comportment to the rocks during past tectonic phases. The temperature of the water inflows encountered is a complex function of the thickness of the overlying rock and the quantity of waters circulating in the massif. Therefore it constitutes a precious indicator, during the excavating of underground works, of the presence of possible more permeable sections in formations to come. A numerical simulation of such a case has been undertaken and shows the possibility of using this tool to improve the expectation of water inflow in the work. The temperature study as well as the application of the chalcedony geothermometer to the entire of the inflow studied has shown the existence of ascending flow systems, fed by precipitation. Waters circulating in the Alpine massifs have a chemical composition strongly influenced by the reservoir rock. Chemical analyses of major and trace elements have allowed to distinguish waters issued from the various petrographies encountered. One must first of all underline the complementarity of major and trace elements analyses. The discriminating power of these two types of analyses does not apply to the same type of rocks. The major structures hydraulically conductive in crystalline Alpine massifs have been defined. One can mainly quote isolated fractures, strongly fractured and tectonised zones, kakirite zones, the decompressed zone, the down-bending zone, contacts between distinctive hydrogeological units and large metasedimentary structures. Hydraulical and physico-chemical characteristics of waters associated to each type of defined structures have been determined. These structures are straightened and induce water circulation close to the vertical, from the surface towards the work. The infiltration zones of waters determined on the basis of the orientation of major structures generally coincides with the altitudes calculated on the basis of isotopic analyses of waters (0-18). Some anomalies have nevertheless been put into evidence. They are mainly relative to circulation in the decompressed zone or in overlying Quatemary beds. Relationship between surface waters (barrier lakes or rivers) and underground works have also been put in evidence on the basis of the interpretation of isotopic results. Numerous water inflows are poor in tritium and attest of the time of transit through to the work in the order of a number of decades, in relation with the weak permeability observed in depth. These waters are often rich in sodium, because of the alteration of silicates contained in rocks. More recent waters have been encountered in shallow depths, in the decompressed zone or in association with a great geological fault. The examples of the interaction between underground works and springs are systematically linked to important water inflows in the works either through a decompressed zone of the massif, or through a particularly transmissive zone connected with the surface. These examples show that in this fissured environment, extremely divided into compartments and anisotropic, the drying up of springs is not linked with the progressive lowering of a water table along side the work but at punctual and localized inflows. A conceptual model of the hydrogeology of the Alpine crystalline is proposed in this study. It is accompanied with an interaction model between a work and the natural middle. It must help the project manager to foresee the water inflows in underground works and to adopt the optimal solution to minimize the impact of the work on water resources.

EPFL1998

Model-free data interpretation for continuous monitoring of complex structures

Daniele Posenato

Structural Health Monitoring (SMH) has the potential to provide reliable, quantitative data on the real condition of structures. Early detection of damage or deterioration can enable preventive repair and reduce maintenance costs. Although obtaining data from monitoring may be easy and fast, the phase of data interpretation is usually complex and expensive in terms of time and resources. An approach to damage detection involves comparisons between predictions from behavioral models and measurements. However, civil engineering structures are difficult to model accurately and this challenge is compounded when structures are built in uncertain environments. Such situations encourage the enhancement of traditional approximate structural assessments through in-service measurements and interpretation of monitoring data. Practitioners encounter difficulties in selecting the data interpretation technique that is most appropriate for continuous monitoring. Only a few studies exist on statistical methods for interpretation of measurements from continuous monitoring. These studies have not included the complexities that arise in practical situations such as measurement noise, missing data and outliers. Moreover, structures are today equipped with hundreds of sensors that measure aspects related to the response and the environment. Therefore algorithms that are able to analyze enormous amounts of heterogeneous data are required for data interpretation. The most innovative contribution of the research work is the definition of new methodologies based on Moving Principal Component Analysis (MPCA) and Robust Regression Analysis (RRA) to detect changes in the behavior of civil engineering structures. The methodology analyzes changes in the patterns of measurement series and is thus able to indicate when and where the damage occurs. The algorithms are designed to learn characteristics of time series generated by sensors during a period called the initialization phase when the structure is assumed to behave normally. The information from the initialization phase subsequently helps in identifying behaviors that can be classified as anomalous. In this manner the proposed methodologies identify anomalous behavior without explicit (and costly) knowledge of structural characteristics such as geometry and behavior models. The methodologies have been tested on data from numerically simulated structures. Studies involved a range of measurement noise levels and a range of damage severities. A comparative study with other statistical methods demonstrates superior performance of the proposed methodologies. The MPCA methodology is extended to accommodate situations of missing data and outliers. Situations of missing complete measurements and missing a single measurement at random are managed with a minimum of extra data pre-processing. A simple and robust cleaning procedure algorithm based on the Inter Quartile Range analysis is presented to remove outliers from measurement series. A clustering algorithm to group measurement series ensures that MPCA is applicable for structures having a significant number of sensors. MPCA is applied to all clusters generated by the clustering algorithm. Finally, MPCA and RRA methodologies have been validated on full scale structures. The results show the ability to identify abrupt permanent changes due to construction stages through time-scale reversal. The results indicate that the proposed methodology can support structural assessments of civil engineering structures.

EPFL2009

Methodologies for model-free data interpretation of civil engineering structures

Daniele Posenato, Ian Smith

Structural health monitoring (SHM) has the potential to provide quantitative and reliable data on the real condition of structures, observe the evolution of their behaviour and detect degradation This paper presents two methodologies for model-free data interpretation to identify and localize anomalous behaviour in civil engineering structures Two statistical methods based on (i) moving principal component analysis and (ii) robust regression analysis are demonstrated to be useful for damage detection during continuous static monitoring of civil structures. The methodologies are tested on numerically simulated elements with sensors for a range of noise in measurements. A comparative study with other statistical analyses demonstrates superior performance of these methods for damage detection. Approaches for accommodating outliers and missing data, which are commonly encountered in structural health monitoring for civil structures, are also proposed. To ensure that the methodologies are scalable for complex structures with many sensors, a clustering algorithm groups sensors that have strong correlations between their measurements Methodologies are then validated on two full-scale structures: The results show the ability of the methodology to identify abrupt permanent changes in behavior. (C) 2010 Elsevier Ltd All rights reserved.

Elsevier2010

Model-free data interpretation for continuous monitoring of complex structures

Daniele Posenato

EPFL2009

Les circulations d'eau dans les massifs cristallins alpins et leurs relations avec les ouvrages souterrains

EPFL1998