Support vector regression for anomaly detection from measurement histories

Copyright © 2013 Elsevier. NOTICE: this is the author’s version of a work that was accepted for publication in Advanced Engineering Informatics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Advanced Engineering Informatics Vol. 27 (2013), DOI: 10.1016/j.aei.2013.03.002This research focuses on the analysis of measurements from distributed sensing of structures. The premise is that ambient temperature variations, and hence the temperature distribution across the structure, have a strong correlation with structural response and that this relationship could be exploited for anomaly detection. Specifically, this research first investigates whether support vector regression (SVR) models could be trained to capture the relationship between distributed temperature and response measurements and subsequently, if these models could be employed in an approach for anomaly detection. The study develops a methodology to generate SVR models that predict the thermal response of bridges from distributed temperature measurements, and evaluates its performance on measurement histories simulated using numerical models of a bridge girder. The potential use of these SVR models for damage detection is then studied by comparing their strain predictions with measurements collected from simulations of the bridge girder in damaged condition. Results show that SVR models that predict structural response from distributed temperature measurements could form the basis for a reliable anomaly detection methodology

Topics in construction safety and health : struck-by and caught-in hazards : an interdisciplinary annotated bibliography

"These referenced articles provide literature on the dangers to construction workers from job hazards in their occupations including the equipment they use and the type of work environment they are working in" - NIOSHTIC-2NIOSHTIC no. 20068258Production of this document was supported by cooperative agreement OH 009762 from the National Institute for Occupational Safety and Health (NIOSH). The contents are solely the responsibility of the authors and do not necessarily represent the official views of NIOSH.Struck-by-and-Caught-in-Hazards-annotated-bibliography.pdfcooperative agreement OH 009762 from the National Institute for Occupational Safety and Healt

Monitoring Workers on Construction Sites using Data Fusion of Real-Time Worker’s Location, Body Orientation, and Productivity State

Traditionally, on-site construction production monitoring depends primarily on manual processes that are time-consuming and error-prone. State-of-the-art technologies have been utilized lately to improve these processes to support timely decisions pertinent to the productivity and safety of onsite operations. This research introduces a novel construction site monitoring system to track workers' location, body orientation, and productivity state. The developed system uses Bluetooth Low Energy (BLE) based reference transmitting beacons fixed on job sites and a set of receiving beacons mounted on workers’ hardhats, chests, and wrists. The system works via three modules, i.e. (i) RTLS (Real-Time Location System) module; (ii) body orientation detection module; and (iii) productivity state detection module. The RTLS module is developed to continuously track the location of the workers and subsequently extract the actual labor workspaces. The RTLS is explicitly designed for construction by satisfying requirements for widespread on-site adoption, including cost efficiency, deployability, scalability, adjustability to the construction site dynamism, and the expected accuracy. The main features of the developed RTLS are (i) substituting commonly used BLE receivers with BLE receiving beacons; (ii) proposing a modular infrastructure placement strategy; (iii) deploying Trilateration and Min-Max as localization techniques; (iv) post-processing the worker’s estimated locations. As per the body orientation detection module, it identifies workers' body orientation on the job sites, using the impacts of signal blockage by a human body to identify an approximate worker's body orientation. It works based on geometrical relationships and Received Signal Strength Indicator (RSSI) values between the chest-mounted receiving beacon and the reference transmitting beacons. Last but not least, the productivity state detection module determines workers' productivity state (i.e., direct work, support work, delay) and travel state, using the accelerometer sensor embedded in the body-mounted receiving beacons. Consequently, the collected data of the system modules are fused to augment real-time knowledge of workers' status on job sites

EG-ICE 2021 Workshop on Intelligent Computing in Engineering

The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways

EG-ICE 2021 Workshop on Intelligent Computing in Engineering

The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways