This book presents selected papers from the 18th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2019). It focuses on deep learning networks and their application in domains such as healthcare, security and threat detection, fault diagnosis and accident analysis, and robotic control in industrial environments, and highlights novel ways of using deep neural networks to solve real-world problems. Also offering insights into deep learning architectures and algorithms, it is an essential reference guide for academic researchers, professionals, software engineers in industry, and innovative product developers.

The modern structural health monitoring (SHM) paradigm of transforming in situ, real-time data acquisition into actionable decisions regarding structural performance, health state, maintenance, or life cycle assessment has been accelerated by the rapid growth of “big data” availability and advanced data science. Such data availability coupled with a wide variety of machine learning and data analytics techniques have led to rapid advancement of how SHM is executed, enabling increased transformation from research to practice. This book intends to present a representative collection of such data science advancements used for SHM applications, providing an important contribution for civil engineers, researchers, and practitioners around the world.

In recent decades, the growing number of civil and aerospace structures has accelerated the development of damage detection and health monitoring approaches. Many are based upon non-destructive and non-invasive sensing and analysis of structural characteristics, and most use structural response information to identify the existence, location, and time of damage. Model based techniques such as parametric and non-parametric system identification seek to identify changes in the parameters of a dynamic structural model. Restoring forces in real structures can exhibit highly non-linear characteristics, thus accurate non-linear system identification is critical. Parametric system identification approaches are commonly used, but these require a priori assumptions about restoring force characteristics. Non-parametric approaches do not require such information, but they typically lack direct associations between the model and the structural dynamics, providing limited utility for accurate health monitoring and damage detection. This dissertation presents a novel 'Intelligent Parameter Varying' (IPV) health monitoring and damage detection technique that accurately detects the existence, location, and time of damage occurrence without any assumptions about the constitutive nature of structural non-linearities. This technique combines the advantages of parametric techniques with the non-parametric capabilities of artificial neural networks by incorporating artificial neural networks into a traditional parametric model. This hybrid approach benefits from the effectiveness of traditional modeling approaches and from the adaptation and learning capabilities of artificial neural networks. The generality of this IPV approach makes it suitable to a wide range of dynamic systems, including those with non-linear and time-varying characteristics. This IPV technique is demonstrated using a lumped-mass structural model with an embedded array of artificial neural networks. These networks i.

This book is organized around the various sensing techniques used to achieve structural health monitoring. Its main focus is on sensors, signal and data reduction methods and inverse techniques, which enable the identification of the physical parameters, affected by the presence of the damage, on which a diagnostic is established. Structural Health Monitoring is not oriented by the type of applications or linked to special classes of problems, but rather presents broader families of techniques: vibration and modal analysis; optical fibre sensing; acousto-ultrasonics, using piezoelectric transducers; and electric and electromagnetic techniques. Each chapter has been written by specialists in the subject area who possess a broad range of practical experience. The book will be accessible to students and those new to the field, but the exhaustive overview of present research and development, as well as the numerous references provided, also make it required reading for experienced researchers and engineers.

Many methods have been developed and studied to detect damage through the change of dynamic response of a structure. Due to its capability to recognize pattern and to correlate non-linear and non-unique problem, Artificial Neural Networks (ANN) have received increasing attention for use in detecting damage in structures based on vibration modal parameters. Most successful works reported in the application of ANN for damage detection are limited to numerical examples and small controlled experimental examples only. This is because of the two main constraints for its practical application in detecting damage in real structures. They are: 1) the inevitable existence of uncertainties in vibration measurement data and finite element modeling of the structure, which may lead to erroneous prediction of structural conditions; and 2) enormous computational effort required to reliably train an ANN model when it involves structures with many degrees of freedom. Therefore, most applications of ANN in damage detection are limited to structure systems with a small number of degrees of freedom and quite significant damage levels. In this thesis, a probabilistic ANN model is proposed to include into consideration the uncertainties in finite element model and measured data. Rossenblueth's point estimate method is used to reduce the calculations in training and testing the probabilistic ANN model. The accuracy of the probabilistic model is verified by Monte Carlo simulations. Using the probabilistic ANN model, the statistics of the stiffness parameters can be predicted which are used to calculate the probability of damage existence (PDE) in each structural member. The reliability and efficiency of this method is demonstrated using both numerical and experimental examples. In addition, a parametric study is carried out to investigate the sensitivity of the proposed method to different damage levels and to different uncertainty levels. As an ANN model requires enormous computational effort in training the ANN model when the number of degrees of freedom is relatively large, a substructuring approach employing multi-stage ANN is proposed to tackle the problem. Through this method, a structure is divided to several substructures and each substructure is assessed separately with independently trained ANN model for the substructure. Once the damaged substructures are identified, second-stage ANN models are trained for these substructures to identify the damage locations and severities of the structural ii element in the substructures. Both the numerical and experimental examples are used to demonstrate the probabilistic multi-stage ANN methods. It is found that this substructuring ANN approach greatly reduces the computational effort while increasing the damage detectability because fine element mesh can be used. It is also found that the probabilistic model gives better damage identification than the deterministic approach. A sensitivity analysis is also conducted to investigate the effect of substructure size, support condition and different uncertainty levels on the damage detectability of the proposed method. The results demonstrated that the detectibility level of the proposed method is independent of the structure type, but dependent on the boundary condition, substructure size and uncertainty level.

Non destructive testing aimed at monitoring, structural identification and di- nostics is of strategic importance in many branches of civil and mechanical - gineering. This type of tests is widely practiced and directly affects topical issues regarding the design of new buildings and the repair and monitoring of existing ones. The load bearing capacity of a structure can now be evaluated using well established mechanical modelling methods aided by computing facilities of great capability. However, to ensure reliable results, models must be calibrated with - curate information on the characteristics of materials and structural components. To this end, non destructive techniques are a useful tool from several points of view. Particularly, by measuring structural response, they provide guidance on the validation of structural descriptions or of the mathematical models of material behaviour. Diagnostic engineering is a crucial area for the application of non destructive testing methods. Repeated tests over time can indicate the emergence of p- sible damage occurring during the structure's lifetime and provide quantitative estimates of the level of residual safety.