Nowadays, many buildings are equipped by Building Automation Systems, which utilize numerous sensors acquiring large amounts of data. Such data is useful to assess the performance of existing building systems and hence to support a variety of decisions made during facility operations and maintenance. However, sensor data alone typically does not support analyses of conditions of a facility since there is a need to understand the context under which data is collected. Therefore, data about sensors, and data collected by sensors and the corresponding spatial contextual information from a building information model are required to be analyzed in an integrated way. Although existing standards provide specifications to represent some aspects of sensor metadata (e.g. SensorML) and building information (e.g. IFC), an approach, which integrates information about sensors and building elements, is still needed. In relation to this, the authors first discuss requirements for a model integrating both sensor metadata and building information and then present possible approaches for developing such a model. The discussions include a synthesis and analyses of existing standards for supporting representation of sensor metadata and building information models, and overview of a prototype implemented to generate integrated models of sensors and building information.
A novel method was developed for the rapid determination of multi-indicators in corni fructus by means of near infrared (NIR) spectroscopy. Particle swarm optimization (PSO) based least squares support vector machine was investigated to increase the levels of quality control. The calibration models of moisture, extractum, morroniside and loganin were established using the PSO-LS-SVM algorithm. The performance of PSO-LS-SVM models was compared with partial least squares regression (PLSR) and back propagation artificial neural network (BP-ANN). The calibration and validation results of PSO-LS-SVM were superior to both PLS and BP-ANN. For PSO-LS-SVM models, the correlation coefficients (r) of calibrations were all above 0.942. The optimal prediction results were also achieved by PSO-LS-SVM models with the RMSEP (root mean square error of prediction) and RSEP (relative standard errors of prediction) less than 1.176 and 15.5% respectively. The results suggest that PSO-LS-SVM algorithm has a good model performance and high prediction accuracy. NIR has a potential value for rapid determination of multi-indicators in Corni Fructus.
Availability and accuracy of building information is critical for a variety of tasks during the lifecycle of facilities. Currently Building information modelling (BIM) is mostly used for design and construction. When the as-designed BIM is not updated with the construction changes, it can contain inaccurate information. A way to collect the as-is conditions is to capture how a facility is changing over time. Vision-based data capture technologies, namely laser scanners and cameras, are being widely used. However, occluded components and the challenges associated with reverse engineering of complex construction objects can result in incomplete as-built data. This paper presents a case study, in which a laser scanner and a camera were used to capture the construction history and develop a more complete as-built BIM. A progressive approach is followed to mitigate challenges associated with cluttered construction data. Components/features occluded in any captured scans were reconsidered throughout a continuous planning and data capturing process. Other sources such as as-designed documents are supplemented to as-built data for extraction of information items required for the as-built BIM. The discussions include more ample description of the background research and the addressed problem, followed by detailed description of this study's approach. Lessons learned, findings and recommendations for future research are summarized.
More than 20% of the energy consumed by heating, ventilation and air-conditioning (HVAC) systems is wasted due to undetected faults in these systems. In the past three decades, researchers have developed hundreds of computer algorithms to automatically and continuously analyze their energy performance. However, due to the complex information required by these algorithms, it is very difficult for facilities operators to deploy them in real-world buildings.
Operation and maintenance of commercial facilities rely on complex building information to locate assets and troubleshoot equipment failures. Although building information models (BIM) provide an integrated information repository for searching for facility information, currently more than 90% of existing buildings in the U.S. were still built with 2D drawings. Manually reconstructing BIM, especially the mechanical systems, has been proven to be very labor-intensive. Hence, previous research studies have investigated automated 3D reconstruction approaches using point clouds data obtained by laser scanner techniques or 2D photos. But due to the needs for a line of sight, these approaches are not applicable for constructed buildings. Researchers have also developed approaches that convert 2D drawings to spatial building models, but the previous studies are limited to generate architectural components such as the wall, doors, and windows. Thus, this paper investigated challenges and approaches to automatically generate models for mechanical systems in buildings using 2D drawings. We analyzed the contents and characteristics of the mechanical components that are represented in drawings and developed a set of classification and algorithms that support the automated recognition of the spatial information and metadata of the mechanical systems. A software framework was proposed to utilize the developed computational approach to recognize 2D drawings for BIM reconstruction. The results of the prototype demonstrated that more than 80% of ducts can be recognized from various drawings in DXF format.
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