Development of Structural Damage Detection Method Working with Contaminated Vibration Data via Autoencoder and Gradient Boosting

Vibration-based structural damage detection is one of the most promising venues for building smart and automated structural health monitoring applications; however, its applicability is impeded by a large amount of collected vibration data, and the performance could be undermined by degraded data. Therefore, this study develops a robust framework, dubbed AutoBoost-SDD, that can effectively handle contaminated vibration data and provide reliable monitoring results within reasonable computational resources. The proposed method consists of three key components. Firstly, multi-domain feature extraction techniques are utilized to convert high-dimensional raw data into informative feature vectors. Secondly, the auto-encoder deep learning architecture is leveraged to refine feature vectors of contaminated data. Finally, a tree-based boosting machine learning algorithm, namely LightGBM, is employed to assess the structures’ operational states using learned output from the second step. The viability and performance of the proposed framework are illustrated via three case studies involving numerical data of a 5-degree of freedom system, a 2D frame structure, and experimental data of a large-scale 18-story frame structure from the literature. The results show that the AutoBoost-SDD framework is able to provide reasonable detection results despite the presence of various contaminations, including noisy, missing, and anomalous data

Experimental and Probabilistic Investigations of the Effect of Fly Ash Dosage on Concrete Compressive Strength and Stress-strain Relationship

The effect of fly ash (FA) dosage on concrete’s compressive strength and stress-strain relationship is investigated in two steps in this article. First, an experimental program was conducted on concrete mixtures designed with 0% (control batch of 30 MPa mean cylinder compressive strength), 10, 20, 30, and 40% of ordinary Portland cement (OPC) mass replaced by FA, which is taken from a new source in an Asia country. The test results showed that compared to other investigated dosages, concrete using 20% FA/OPC mass-replacement gained the most improvement in the 28-day compressive strength and tensile split strength, as well as the compressive strength development. Second, a probabilistic investigation was conducted using Dropout Neural Network, Bayesian Neural Network, and Gaussian Process models. These artificial intelligence-based models were compared to other models reviewed from the literature, showing relatively good results in terms of the statistical metric R2, which are 0.92, 0.9, and 0.88, respectively. The three models were tested and validated with a dataset of 1032 experimental results on FAC collected from the literature. When testing with the experimental results obtained in the first step, a good correlation between the predicted values and the experimental results was observed within the confidence interval of (5%, 95%), showing the reliability of the proposed models. Thus, the stress-strain relationship of fly ash concrete can also be investigated in a probabilistic manner. It is proved in this study that among the proposed models, Dropout Neural Network has the best balance between performance and time complexity

Direct numerical simulation of the dynamics of sliding rough surfaces

The noise generated by the friction of two rough surfaces under weak contact pressure is usually called roughness noise. The underlying vibration which produces the noise stems from numerous instantaneous shocks (in the microsecond range) between surface micro-asperities. The numerical simulation of this problem using classical mechanics requires a fine discretization in both space and time. This is why the finite element method takes much CPU time. In this study, we propose an alternative numerical approach which is based on a truncated modal decomposition of the vibration, a central difference integration scheme and two algorithms for contact: The penalty algorithm and the Lagrange multiplier algorithm. Not only does it reproduce the empirical laws of vibration level versus roughness and sliding speed found experimentally but it also provides the statistical properties of local events which are not accessible by experiment. The CPU time reduction is typically a factor of 10.Comment: 16 pages, 16 figures, accepted versio

A Covariance Matrix Adaptation Evolution Strategy for Direct Policy Search in Reproducing Kernel Hilbert Space

The covariance matrix adaptation evolution strategy (CMA-ES) is an efficient derivative-free optimization algorithm. It optimizes a black-box objective function over a well defined parameter space. In some problems, such parameter spaces are defined using function approximation in which feature functions are manually defined. Therefore, the performance of those techniques strongly depends on the quality of chosen features. Hence, enabling CMA-ES to optimize on a more complex and general function class of the objective has long been desired. Specifically, we consider modeling the input space for black-box optimization in reproducing kernel Hilbert spaces (RKHS). This modeling leads to a functional optimization problem whose domain is a function space that enables us to optimize in a very rich function class. In addition, we propose CMA-ES-RKHS, a generalized CMA-ES framework, that performs black-box functional optimization in the RKHS. A search distribution, represented as a Gaussian process, is adapted by updating both its mean function and covariance operator. Adaptive representation of the function and covariance operator is achieved with sparsification techniques. We evaluate CMA-ES-RKHS on a simple functional optimization problem and bench-mark reinforcement learning (RL) domains. For an application in RL, we model policies for MDPs in RKHS and transform a cumulative return objective as a functional of RKHS policies, which can be optimized via CMA-ES-RKHS. This formulation results in a black-box functional policy search framework

Determination of Reinforced Fly Ash Concrete Columns’ Resistance Using Nonlinear Models of Materials

This article introduces experimental and analytical studies on the resistance under eccentric loads of reinforced fly ash concrete (RFAC) columns, in which fly ash (FA) is used to partially replace ordinary Portland cement (OPC) with a by-mass ratio of 20%. Based on experimental results of concrete specimens with mean 28-day cylinder strength of 30 MPa, modifications on simplified bi-linear and tri-linear models of stress-strain relationships of OPC concrete specified in the Russian and Vietnamese design standards are proposed. These nonlinear deformation models are incorporated into an analytical approach to establish the resistance of RFAC columns in the form of interaction surface, associated with an assessment method for safety factor based on the principle of inverse distance weighted average (IDWA). Parameters of the proposed analytical approach are determined by test results obtained from eight RFAC column specimens having 150 × 200 (mm) rectangular cross-section, 1600 mm-height, and 4Φ14 longitudinal rebars with yield strength of 362.6 MPa. In the tests, the specimens were loaded with uniaxial eccentricities ranging from 0 to 80 mm until failed. It is shown that with ε’b1 = 0.0022 and kE = 0.91, the corresponding safety factors of bi-linear and tri-linear models validated for the tested specimens are conservative and nearest to unity, proving that the proposed analytical approach is capable of closely predicting the RFAC columns’ resistance

Simulation numérique directe en dynamique du contact rugueux

Le bruit de frottement entre deux surfaces rugueuses est dû à la vibration verticale engendrée par les impacts inter-aspérités de deux solides frottés. Les expérimentations ne donnent pas l?accès aux grandeurs mécaniques locales comme l?intensité de choc, la déformation d?aspérité, la pression locale.?Afin de révéler la relation entre les derniers et les paramètres macroscopiques, on propose une approche numérique 1D basée sur la méthode de décomposition modale, les formulations de Lagrange et pénalité, et certains schémas d'intégration

Dynamique des interfaces multicontact

Le bruit de frottement de deux surfaces rugueuses est dû à la vibration verticale engendrée par les impacts inter-aspérités de deux solides glissants. Il relève de la physique des interfaces multicontact dont les propriétés sont encore largement méconnues. L'objet de cette thèse est de comprendre les mécanismes de transfert d'énergie et de génération des vibrations à l'œuvre à l'interface entre deux surfaces rugueuses en glissement relatif. Ces interfaces présentent des spots de contact qui se renouvellent très rapidement mais dont la physique statistique reste à découvrir. Un outil numérique est spécialement développé pour étudier efficacement ce phénomène aux échelles microscopique et macroscopique. Les simulations sont effectuées à l'aide de centres de calcul haute performance à Lyon. Elles ont mené aux conclusions suivantes. Le niveau de la vibration Lv (dB) est une fonction croissante du logarithme de la rugosité de surface Ra et de la vitesse de glissement V, ce qui est en accord avec les résultats expérimentaux issus de la littérature. De plus, grâce à cet outil numérique, on a pu analyser précisément les chocs entre surfaces définis à partir de l'évolution temporelle de la force de contact. Leur durée est de l'ordre de 0.1 ms, la force maximale de contact peut atteindre 100 fois le poids propre du solide glissant, et le nombre de chocs est de l'ordre de 10000 par seconde pour une surface de l'ordre de 4 cm2. Les chocs sont donc des excitations transitoires brèves mais nombreuses et intenses. Ces chocs se comportent comme les sources d'énergie vibratoire qui sont responsables d'un transfert d'énergie à l'interface. C'est en effet la transformation de l'énergie cinétique du mouvement solide glissant en énergie vibratoire qui est responsable du bruit de frottement.The friction noise between two rough surfaces is caused by the vertical vibration generated by inter-asperity impacts of sliding solids. This phenomenon involves the physics of multicontact interfaces, a field which is largely unknown. The purpose of this thesis is to understand the mechanisms of noise generation and the energy transfer process between two rough surfaces in sliding contact. The contact spots in the interface are rapidly renewed during the movement in a random fashion but their statistical properties remain to be discovered. A numerical tool is developed in order to efficiently study this phenomenon at both macroscopic and microscopie scales. The simulations are carried out thanks to the high performance computing centre in Lyon. This study leads to the following conclusions. The vibration level Lv (dB) is an increasing logarithm function of surface roughness Ra and sliding velocity V. This statement is consistent with experimental results available in the literature. Moreover, we can analyze precisely the asperity shocks which are defined from the time evolution of the contact force. The shock duration is of the order of 0.1 ms, the maximal contact force can reach to 100 times the weight of sliding solid, and the shock rate is of the order of 10000 for a surface of 4 cm2 . The asperity shocks are transient excitations, brief but abundant and intensive. These shocks behave like vibrational energy sources and are responsible of the energy transfer in the interface. This is the transformation process of kinetic energy to vibrational energy which is responsible of friction noises.LYON-Ecole Centrale (690812301) / SudocSudocFranceF