Structural health monitoring and bridge condition assessment

Thesis (Ph.D.) University of Alaska Fairbanks, 2016This research is mainly in the field of structural identification and model calibration, optimal sensor placement, and structural health monitoring application for large-scale structures. The ultimate goal of this study is to identify the structure behavior and evaluate the health condition by using structural health monitoring system. To achieve this goal, this research firstly established two fiber optic structural health monitoring systems for a two-span truss bridge and a five-span steel girder bridge. Secondly, this research examined the empirical mode decomposition (EMD) method’s application by using the portable accelerometer system for a long steel girder bridge, and identified the accelerometer number requirements for comprehensively record bridge modal frequencies and damping. Thirdly, it developed a multi-direction model updating method which can update the bridge model by using static and dynamic measurement. Finally, this research studied the optimal static strain sensor placement and established a new method for model parameter identification and damage detection.Chapter 1: Introduction -- Chapter 2: Structural Health Monitoring of the Klehini River Bridge -- Chapter 3: Ambient Loading and Modal Parameters for the Chulitna River Bridge -- Chapter 4: Multi-direction Bridge Model Updating using Static and Dynamic Measurement -- Chapter 5: Optimal Static Strain Sensor Placement for Bridge Model Parameter Identification by using Numerical Optimization Method -- Chapter 6: Conclusions and Future Work

How Different Behavior of Bosses Affect the Organization Performance

The behaviors of bosses in the organization determine the good and bad performances of employees. The purpose of this research is to identify behaviors of different bosses in various situations. Actually people or employees must be guided by bosses, who have a clear sense of direction. A group discussion approach has been adopted by the researchers in which department of management sciences of The Islamia University Bahawalpur has been taken for the discussion on boss behaviors. A qualitative approach has been adopted for content analysis of performance evaluation of employees in this university. Research has been taken from teaching staff of university and results are analyzed for the basis of group discussion. Results have shown that by compare and contrast method to evaluate the responses of teacher about their boss. Research have also shown that factors like displaying moral courage ,abusive supervision ,keep commitments and opportunity learn and grow are the potential hindrances in performances of teacher in this department .Proposals and implications for this department have also been discussed in order to improve the performance. Keywords: Human resource management, organization performance, teacher’s evaluation, work environment, bosses behavior, Department of management sciences Islamia University Bahawalpur (DMS, IUB

Evaluation of underwater vehicle\u27s self-localization based on visual odometry or sensor odometry

This research focuses on two self-localization methods for observation of the sea floor and sampling. One is a method of estimate the self-localization using Kalman filter from the acceleration data calculated from equation of motion and the velocity data considering the effect of underwater vehicle\u27s oscillation. The other is visual odometry using a stereo camera. The AUV was deployed in a sea area 40m depth to evaluate the performance of self-localization estimated by two methods. Self-localization estimation using Kalman filter was less accurate than visual odometry, but it was confirmed that the variance of the estimated velocity was smaller than before estimation. In the visual odometry using stereo camera, it was confirmed that the estimation error depends on the travel direction of AUV and the moving direction of a stereo camera.2019 IEEE 14th International Conference on Industrial and Information Systems (ICIIS 2019), 18- 20, December, 2019, University of Peradeniya, Sri Lank

A Method for Orientation of Cellulose Nano Fibers for Addition of Biological Nanoparticles and Tissue Integration

The University of Maine is one of the world’s leading producers of cellulose nanofibers (CNF). This material has the power to revitalize the Maine paper industry. This has led to an abundance of labs on campus researching applications for the material. Active research activities at the University of Maine include industrial applications such as filtration, biodegradable packaging, building materials and niche health care applications. Niche health care applications include wicking pads for point of care diagnostics and tunable biomaterials ranging from dressings to implantable bone-like materials. Medical applications require an analysis of the biocompatibility of CNF.CNF is biocompatible in general: however, the degree of biocompatibility, particularly for tissue engineering applications involving long term integration within the body, is an area of active research. CNF is a highly tunable biomaterial; control of the fibril anisotropy dramatically alters bulk biomechanical properties which also modulates biological interaction. To tune fiber anisotropy, a novel strategy to produce single direction-oriented CNF films by application of a unilateral force via a latex substrate was developed. The alignment of CNF films was characterized using both polarized light microscopy and scanning electron microscopy(SEM). CNF is birefringent; therefore, using polarized light microscopy the alignment of CNF can be inferred by quantifying the birefringence optical index (BOI). The BOI ranges from -1 to 1, with high birefringence found at the extrema values. To validate the birefringence orientation sensitivity to CNF film alignment; we performed SEM for direct assessment of fiber alignment using OrientationJ, a FIJI plugin. Hydroxyapatite (HA) was incorporated as an additive to evaluate the application of this method to cellulose nano composites. Both the BOI and the SEM fibril analysis confirmed that CNF fibrils are aligned preferentially along the axis of force transmission. Furthermore, the addition of HA did not diminish the degree of CNF fibril alignment. Overall, we demonstrate a rapid and economic approach to fabricate and assess fibril alignment in CNF films useful for a myriad of tissue engineering applications

Bike Host - Building mobility and community through cycling mentorship

The project goal is to evaluate cycling mentorship as a cost-effective method of encouraging more people to cycle as their primary means of transportation, particularly over the short distances that make up a high proportion of daily trips. To reach this goal, Bike Host will enable 60 new Canadians with no bicycle access to become regular commuting cyclists who are willing to invest in the purchase of their own bicycles. Along the way, Bike Host will provide social support and community resources to help participants establish a network of knowledgeable cyclists who are able to offer advice on topics such as bicycle repair, theft prevention, and road safety. The success of the 2014 Bike Host program will be measured by survey materials that are developed by the Toronto Think and Do Tank, and the participant responses will be analyzed by research assistants at the University of Toronto, organized by myself as Research Manager under the direction of Dr. Beth Savan. Community-based social marketing has been instrumental in detailing the specific processes and tools that leverage social environments to foster sustainable behaviours relating to individual energy-use and waste diversion, but these principles have only begun to be employed to alter the much more complicated behaviours that affect our transportation habits.11,12 The Bike Host research project builds on our previous work promoting behaviour change and bicycle use to new immigrants and secondary school-aged children, and tailors this approach directly to local populations in St. Jamestown and Regent Park. These communities host large cohorts of new Canadians, who have recently changed their lives through immigrating. Evidence is clear that those experiencing life transitions are much more likely to make changes to long-held habits, making these communities ideal targets for travel behaviour change programs.13 Our program creates new social norms for these new Canadians, engaging them through social interaction, community based activities and public statements of commitment to cycling. My research will evaluate this program to identify its success and shortcomings as well as the most important factors in facilitating cycling adoption in this community. The end goal is to use our evaluation data to help refine and modify our behaviour change toolkit by analyzing the results of entry and exit surveys and observing patterns of cycling behaviour in participant trip logs. 1

Air Quality Prediction in Smart Cities Using Machine Learning Technologies Based on Sensor Data: A Review

The influence of machine learning technologies is rapidly increasing and penetrating almost in every field, and air pollution prediction is not being excluded from those fields. This paper covers the revision of the studies related to air pollution prediction using machine learning algorithms based on sensor data in the context of smart cities. Using the most popular databases and executing the corresponding filtration, the most relevant papers were selected. After thorough reviewing those papers, the main features were extracted, which served as a base to link and compare them to each other. As a result, we can conclude that: (1) instead of using simple machine learning techniques, currently, the authors apply advanced and sophisticated techniques, (2) China was the leading country in terms of a case study, (3) Particulate matter with diameter equal to 2.5 micrometers was the main prediction target, (4) in 41% of the publications the authors carried out the prediction for the next day, (5) 66% of the studies used data had an hourly rate, (6) 49% of the papers used open data and since 2016 it had a tendency to increase, and (7) for efficient air quality prediction it is important to consider the external factors such as weather conditions, spatial characteristics, and temporal features