TY - CONF A1 - Hu, Wei-Hua A1 - Caetano, E. A1 - Cunha, A. A1 - Rücker, Werner ED - Carrera, E. ED - Miglioretti, F. ED - Petrolo, M. T1 - Comparison of different statistical approaches for removal of environmental/operational effects on modal properties and damage detection N2 - The implementation of a continuous dynamic monitoring System in Pedro e Ines footbridge at Coimbra, Portugal, operating since June 2007, enabled to detect different environmental/operational effects on the modal properties, based on appropriate processing of monitoring data collected by a set of accelerometers and thermal sensors. In order to remove or mitigate such environmental/operational effects with the purpose of damage detection, two different Statistical methods have been adopted, One of them consists in the multiple linear regression (MLR) by performing correlation analysis between measured modal properties and environmental/operational variables. Another is based on the identification of the linear subspace within the modal properties by applying principal component regression (PCR) without using measured values of environmental and operational variables. This paper presents a comparison ofthe performance of these two alternative approaches on the basis of continuous monitoring data acquired front the instrumented Pedro e Ines footbridge. T2 - SMART2013 - 6th ECCOMAS conference on smart structures and materials CY - Turin, Italy DA - 24.06.2013 KW - Structural health monitoring KW - Environmental/operational effects KW - Principal component regression KW - Multiple linear regression KW - Damage detection PY - 2013 SP - 1 EP - 13 AN - OPUS4-32972 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Hu, W.‐H. A1 - Álvaro Cunha, Á. A1 - Caetano, E. A1 - Rohrmann, R. G. A1 - Said, Samir A1 - Teng, J. T1 - Comparison of different statistical approaches for removing environmental/operational effects for massive data continuously collected from footbridges N2 - The implementation of continuous dynamic monitoring systems in two bridges, in Portugal, is enabled to detect the occurrence of very significant environmental and operational effects on the modal properties of these bridges, based on automated processing of massive amounts of monitoring data collected by a set of accelerometers and thermal sensors over several years. In order to remove or mitigate such environmental/operational effects with the purpose of damage detection, two different statistical methods have been adopted. One of them is the multiple linear regression by performing nonlinear correlation analysis between measured modal properties and environmental/operational variables. Another one is principal component regression based on the identification of the linear subspace within the modal properties without using measured values of environmental and operational variables. This paper presents a comparison of the performance of these two alternative approaches on the basis of continuous monitoring data acquired from two instrumented bridges and simulated damage scenarios. It is observed that different methods show similar capacity in removing environmental effects, and the multiple linear regression method is slightly more sensitive to structural damage. KW - Multiple linear KW - Damage detection KW - Environmental/operational effect KW - Modal property PY - 2017 U6 - https://doi.org/10.1002/stc.1955 SN - 1545-2255 VL - 24 IS - 8 SP - e1955-1 EP - e1955-19 PB - John Wiley & Sons, Ltd AN - OPUS4-41147 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -