Yiqing Ni

Bio: Yiqing Ni is an academic researcher from Hong Kong Polytechnic University. The author has contributed to research in topics: Structural health monitoring & Damper. The author has an hindex of 46, co-authored 384 publications receiving 8025 citations. Previous affiliations of Yiqing Ni include Wenzhou University & Zhejiang University.

Fiber Bragg grating sensors for structural health monitoring of Tsing Ma bridge : Background and experimental observation

Abstract: The rapid expansion of the optical fiber telecommunication industry due to the explosion of the Internet has substantially driven down the cost of optical components, making fiber optic sensors more economically viable. In addition, the rapid development of fiber-optic sensors, particularly the fiber Bragg grating (FBG) sensors offers many advantages and capability that could not be achieved otherwise. In the past few years, fiber Bragg grating sensors have attracted a lot of interest and they are being used in numerous applications. This paper describes the FBG sensors developed for structural health monitoring, and were installed on Hong Kong's landmark Tsing Ma bridge (TMB), which is the world longest (1377 m) suspension bridge that carried both railway and regular road traffic. Forty FBG sensors divided into three arrays were installed on the hanger cable, rocker bearing and truss girders of the TMB. The objectives of the study are to investigate the feasibility of using the developed FBG sensors for structural health monitoring, via monitoring the strain of different parts of the TMB under both the railway and highway loads as well as comparing the FBG sensors' performance with the conventional structural health monitoring system - Wind and Structural Health Monitoring System (WASHMS) that has been operating at TMB since the bridge's commissioning in May 1997. The experimental observations in this project show that the results using FBG sensors were in excellent agreement with those acquired by WASHMS.

Technology innovation in developing the structural health monitoring system for Guangzhou New TV Tower

Abstract: The Guangzhou New TV Tower (GNTVT), currently being constructed in Guangzhou, China, is a supertall structure with a height of 610 m. This tube-in-tube structure comprises a reinforced concrete inner tube and a steel outer tube adopting concrete-filled-tube columns. A sophisticated structural health monitoring (SHM) system consisting of over 600 sensors has been designed and is being implemented by The Hong Kong Polytechnic University to GNTVT for both in-construction and in-service real-time monitoring. This paper outlines the technology innovation in developing and implementing this SHM system, which includes (i) modular design of the SHM system, (ii) integration of the in-construction monitoring system and the in-service monitoring system, (iii) wireless-based data acquisition and Internet-based remote data transmission, (iv) design and implementation of a fiber Bragg grating sensing system,(v) structural health and condition assessment using static and dynamic monitoring data, (vi) verification of the effectiveness of vibration control devices by the SHM system, and (vii) development of an SHM benchmark problem by taking GNTVT as a test bed and using real-world measurement data. Preliminary monitoring data including those obtained during the Wenchuan earthquake and recent typhoons are also presented. Copyright © 2008 John Wiley & Sons, Ltd.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks

Abstract: A number of image processing techniques IPTs have been implemented for detecting civil infrastructure defects to partially replace human-conducted onsite inspections. These IPTs are primarily used to manipulate images to extract defect features, such as cracks in concrete and steel surfaces. However, the extensively varying real-world situations e.g., lighting and shadow changes can lead to challenges to the wide adoption of IPTs. To overcome these challenges, this article proposes a vision-based method using a deep architecture of convolutional neural networks CNNs for detecting concrete cracks without calculating the defect features. As CNNs are capable of learning image features automatically, the proposed method works without the conjugation of IPTs for extracting features. The designed CNN is trained on 40 K images of 256 × 256 pixel resolutions and, consequently, records with about 98% accuracy. The trained CNN is combined with a sliding window technique to scan any image size larger than 256 × 256 pixel resolutions. The robustness and adaptability of the proposed approach are tested on 55 images of 5,888 × 3,584 pixel resolutions taken from a different structure which is not used for training and validation processes under various conditions e.g., strong light spot, shadows, and very thin cracks. Comparative studies are conducted to examine the performance of the proposed CNN using traditional Canny and Sobel edge detection methods. The results show that the proposed method shows quite better performances and can indeed find concrete cracks in realistic situations.

Vibration-based Damage Identification Methods: A Review and Comparative Study:

Abstract: A comprehensive review on modal parameter-based damage identification methods for beam- or plate-type structures is presented, and the damage identification algorithms in terms of signal processing...

A review of structural health monitoring literature 1996-2001

Abstract: Staff members at Los Alamos National Laboratory (LANL) produced a summary of the structural health monitoring literature in 1995. This presentation will summarize the outcome of an updated review covering the years 1996 2001. The updated review follows the LANL statistical pattern recognition paradigm for SHM, which addresses four topics: 1. Operational Evaluation; 2. Data Acquisition and Cleansing; 3. Feature Extraction; and 4. Statistical Modeling for Feature Discrimination. The literature has been reviewed based on how a particular study addresses these four topics. A significant observation from this review is that although there are many more SHM studies being reported, the investigators, in general, have not yet fully embraced the well-developed tools from statistical pattern recognition. As such, the discrimination procedures employed are often lacking the appropriate rigor necessary for this technology to evolve beyond demonstration problems carried out in laboratory setting.