This paper compares the performance of various feature extraction methods applied to structural sensor measurements
acquired in-situ, from a decommissioned bridge under realistic damage scenarios. Three feature extraction methods are
applied to sensor data to generate feature vectors for normal and damaged structure data patterns. The investigated
feature extraction methods include identification of both time domain methods as well as frequency domain methods.
The evaluation of the feature extraction methods is performed by examining distance values among different patterns,
distance values among feature vectors in the same pattern, and pattern recognition success rate. The test data used in the
comparison study are from the System Identification to Monitor Civil Engineering Structures (SIMCES) Z24 Bridge
damage detection tests, a rigorous instrumentation campaign that recorded the dynamic performance of a concrete box-girder
bridge under progressively increasing damage scenarios. A number of progressive damage test case data sets,
including undamaged cases and pier settlement cases (different depths), are used to test the separation of feature vectors
among different patterns and the pattern recognition success rate for different feature extraction methods is reported.
This paper presents an immune network-based emergent pattern recognition method. The artificial immune network
provides more flexible learning tools than neural networks and clustering technologies. With a neural network, a
network structure has to be defined first. The immune network allows their components to change and learn patterns by
changing the strength of connections between individual components. The presented computational model achieves
emergent pattern recognition by dynamically constructing a network of feature vectors to represent the internal image of
input data patterns. The immune network-based emergent pattern recognition approach has tested using a benchmark
civil structure. The test result shows the feasibility of using the presented method for the emergent structural damage
pattern recognition.
This paper presents an artificial immune pattern recognition (AIPR) approach for the damage detection and classification
in structures. An AIPR-based Structure Damage Classifier (AIPR-SDC) has been developed by mimicking immune
recognition and learning mechanisms. The structure damage patterns are represented by feature vectors that are extracted
from the structure's dynamic response measurements. The training process is designed based on the clonal selection
principle in the immune system. The selective and adaptive features of the clonal selection algorithm allow the classifier
to generate recognition feature vectors that are able to match the training data. In addition, the immune learning
algorithm can learn and remember various data patterns by generating a set of memory cells that contains representative
feature vectors for each class (pattern). The performance of the presented structure damage classifier has been validated
using a benchmark structure proposed by the IASC-ASCE (International Association for Structural Control - American
Society of Civil Engineers) Structural Health Monitoring Task Group. The validation results show a better classification
success rate comparing to some of other classification algorithms.
This paper presents a biologically inspired sensor network framework for autonomous structural health monitoring
(SHM). The presented sensor network framework transforms desirable characteristics and effective defense mechanisms
of the natural immune system to wireless sensor networks for SHM. The autonomous structural health monitoring is
achieved through an integrated sensor network framework consisting of high computational power sensors, a mobileagent-
based sensor network middleware, and artificial immune pattern recognition (AIPR) methodology for structure
damage detection and classification. An AIPR-based structure damage classifier (AIPR-SDC) has been developed,
which incorporates several novel characteristics of the natural immune system. The performance of the AIPR-SDC has
been validated using a benchmark structure proposed by the IASC-ASCE (International Association for Structural
Control - American Society of Civil Engineers) SHM Task Group. The validation results show a better classification
success rate comparing to some of other classification algorithms. The further study of unsupervised structure damage
classification is also conducted by integrating data clustering techniques and the AIPR method.
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