Bridge damage identification using deep learning-based Convolutional Neural Networks (CNNs)

In this paper, a novel method is proposed based on a windowed-one-dimensional convolutional neural network for multiclass damage detection using acceleration responses. The data is pre-processed and augmented by extracting samples of windows of the original acceleration time series. 1D CNN is developed to classify the signals in multiple classes. The damage is detected if the predicted classification is one of the indicated damage levels. The damage is quantified using the predicted class probabilities. Various signals from the accelerometers are provided as input to the 1D CNN model, and the resulting class probabilities are used to identify the location of the damage. The proposed method is validated using Z24 bridge benchmark data for multiclass classification for two damage scenarios. The results show that the proposed 1D CNN method performs with superior accuracy for severe damage cases and works well with different types of damage types

Towards Multiclass Damage Detection and Localization using Limited Vibration Measurements

Traditional vibration-based damage detection methods provide structural health information based on their measured data (i.e., acceleration and displacement response). Over the last few decades, various model-based and time-frequency methods have shown great promises for damage identification and localization. However, the existing methods are unable to perform satisfactorily in many situations, including the presence of limited sensor measurements and training data, detection of minor and progressive damage, and identification of multiclass damage, creating constraints to make them free of user-intervention and implemented using the modern sensors. The main objective of this thesis is to develop algorithms capable of damage identification and localization using limited measurements that can address the limitation of the traditional methods while providing a minimal to no user-intervention damage identification process. The proposed research in this thesis involves casting damage detection problems as non-parametric and autonomous with the least user intervention. Progressive damage identification is presented using novel time-frequency methods, such as synchrosqueezing transform and multivariate empirical mode decomposition, showing improved sensitivity of identifying minor damage over traditional methods. A basis-free method, such as multivariate empirical mode decomposition, is employed for damage localization using limited sensors. The acquired vibration measurement is decomposed into its mono components, and a damage localization index based on modal energy is proposed to overcome the need for a large number of sensors. The limited measurement aspect of damage localization is explored by selecting fewer sensors, and it is shown that with limited measurements, the proposed method is as effective as a total number of measurements equals the number of degrees of freedom of the model. To create an autonomous damage identification framework, Artificial Intelligence-based methods are explored the first time for multiclass damage classification and localization. Due to the lack of availability of a large amount of data, the acquired vibration data is augmented using windowing of the data per damage class. A novel window-based one-dimensional convolutional neural network is explored to classify sequential time-series of vibration measurements with only one hidden layer. The robustness of the proposed method is further evaluated by a suite of parametric and sensitivity analysis. Improvement of this method is further accomplished by implementing a windowed Long Short-term Memory network capable of learning long-term dependencies of the sequential data. Finally, the proposed methods are validated using a suite of experimental and full-scale studies, including a high-rate dynamics experimental testbed, a stadia prototype experimental setup, the MIT green building, and the Z24 bridge

A Systematic Review of Convolutional Neural Network-Based Structural Condition Assessment Techniques

With recent advances in non-contact sensing technology such as cameras, unmanned aerial and ground vehicles, the structural health monitoring (SHM) community has witnessed a prominent growth in deep learning-based condition assessment techniques of structural systems. These deep learning methods rely primarily on convolutional neural networks (CNNs). The CNN networks are trained using a large number of datasets for various types of damage and anomaly detection and post-disaster reconnaissance. The trained networks are then utilized to analyze newer data to detect the type and severity of the damage, enhancing the capabilities of non-contact sensors in developing autonomous SHM systems. In recent years, a broad range of CNN architectures has been developed by researchers to accommodate the extent of lighting and weather conditions, the quality of images, the amount of background and foreground noise, and multiclass damage in the structures. This paper presents a detailed literature review of existing CNN-based techniques in the context of infrastructure monitoring and maintenance. The review is categorized into multiple classes depending on the specific application and development of CNNs applied to data obtained from a wide range of structures. The challenges and limitations of the existing literature are discussed in detail at the end, followed by a brief conclusion on potential future research directions of CNN in structural condition assessment

Niche models inform the effects of climate change on the endangered Nilgiri Tahr (Nilgiritragus hylocrius) populations in the southern Western Ghats,India

Large mammals are declining globally due to habitat loss and fragmentation. Climate change is one of the factors known to alter the range of several mammalian species. An early understanding of the effect of climate change on species distributions can provide critical information for conservation planning. Nilgiritragus hylocrius (Nilgiri Tahr) is an endangered ungulate that is restricted to the montane grasslands of the Western Ghats, India. Currently, the Nilgiri Tahr is restricted to a fraction of its former range and is also prone to risks due to several ongoing anthropogenic pressures. However, the impact of global climate change on this emblematic species has seldom been estimated. The goal of our study was to use ecological niche models to quantify the effect of climate change on the habitats of Nilgiri Tahr. Using the maximum entropy (MaxEnt) algorithm, we modelled the potential distribution of Nilgiri Tahr in its native range. The models were developed under the current climatic conditions and then projected onto two future climate change scenarios (RCP4.5 and RCP8.5) and for three different time frames in the future (years 2030, 2050 and 2080). We identified that most of the climatically suitable habitats of Nilgiri Tahr would become unsuitable when global warming intensifies. Our models predicted a complete loss of suitable habitats in many existing protected areas in the Western Ghats. We use insights provided by our modeling results to propose conservation management plans to increase the likelihood of persistence of Nilgiri Tahr in the Western Ghats

Insights on the astringency of non alcoholic beverages: fruit, vegetable & planation based perspective

Sensory parameters of food and beverages secured much importance with mounting changes in diet preferences. The taste and flavour of these food groups are largely affected by astringency which in turn influence the sensory experience. Derived from Latin, astringency deals with the sensation of extreme dryness, roughness or puckering involving the secretions of salivary glands. Accountable astringent clusters majorly revolving around the presence of tannins in food trailed by the incidence of salts of multivalent cations like Al, Zn, Cr etc, and dehydrating agents like mineral acids, alcohol etc. To augment the sensory feeling and to broaden the marketing possibilities related to beverages, it is important to accomplish techniques to reduce or control the development of these sensations. De-astringency practices performed in foods can be broadly catalogued into thermal and non-thermal treatments. While the former majorly included hot water, steam and microwave treatment, the latter concentrated mainly on innovative techniques like high hydrostatic pressure, pulsed electric field, thermo-sonication, ultrasonication etc. The effectiveness of these procedures is largely dependent on the mechanisms associated with the development of astringency feelings in foods. Understanding the mechanism underlying astringency sensation is still in a nascent stage and needs more exploration to state the explicit reason behind the process. This review chiefly covers the explanation of astringency, the mechanism involved and the different de-astringency techniques as per the prevailing astringency models