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Radio frequency identification (RFID) technology has been widely used in the field of construction during the last two decades. Basically, RFID facilitates the control on a wide variety of processes in different stages of the lifecycle of a building, from its conception to its inhabitance. The main objective of this paper is to present a review of RFID applications in the construction industry, pointing out the existing developments, limitations and gaps. The paper presents the establishment of the RFID technology in four main stages of the lifecycle of a facility: planning and design, construction and commission and operation and maintenance. Concerning this last stage, an RFID application aiming to facilitate the identification of pieces of furniture in scanned inhabited environments is presented. Conclusions and future advances are presented at the end of the paper.

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In this paper we present a method that automatically yields Boundary Representation Models (B-rep) for indoors after processing dense point clouds collected by laser scanners from key locations through an existing facility. Our objective is particularly focused on providing single models which contain the shape, location and relationship of primitive structural elements of inhabited scenarios such as walls, ceilings and floors. We propose a discretization of the space in order to accurately segment the 3D data and generate complete B-rep models of indoors in which faces, edges and vertices are coherently connected. The approach has been tested in real scenarios with data coming from laser scanners yielding promising results. We have deeply evaluated the results by analyzing how reliably these elements can be detected and how accurately they are modeled.

Methods employed for surveying buildings for condition have traditionally been reliant upon visual assessment and manual recording. Survey of traditional masonry also ostensibly conforms to this approach but, due to the sheer volume of masonry units composing walls, it is often prohibitively time consuming, exceptionally complex and ultimately costly. Notable features of such survey work for ashlar stone types require each stone to be labelled and overlaid with information relative to condition. Further hindering these already costly operations, it has been shown that the accuracy of reporting, including labelling the manifestation of defects and defect diagnosis, is subjective, depending upon the expertise and experience of those evaluating the fabric. Moving beyond these preliminary survey and reporting stages, this situation gives rise to variable repair and maintenance strategies that can have significant cost implications and can debase fundamental conservation activities. The development of digital technologies, such as terrestrial laser scanning, and advancements in novel computer vision statistical techniques can help produce accurate representation of buildings that can be subsequently rapidly processed, achieving many tangible survey functions with greater inherent objectivity. In this paper, an innovative strategy for automatic detection and classification of defects in digitised ashlar masonry walling is presented. The classification method is based on the use of supervised machine learning algorithms, assisted by surveyors' strategies and expertise to identify defective individual masonry units, through to broader global patterns for groups of stones. The proposed approach has been tested on the main façade of the Chapel Royal in Stirling Castle (Scotland), demonstrating its potential for ashlar masonry forms of wall construction. It is important to recognise that the findings are not limited to this culturally significant building and will be of high value to almost innumerable ashlar-built structures worldwide. The research ultimately attempts to reduce the degree of subjectivity in classifying defects, on a scale and rapidity hitherto beyond traditional project cost constraints. Importantly, it is recognised that through automation more effective utilisation of resources that would have been traditionally spent on survey can be redeployed to support fabric intervention or routine maintenance operations.

This paper is focused on the automatic construction of 3D basic-semantic models of inhabited interiors using laser scanners with the help of RFID technologies. This is an innovative approach, in whose field scarce publications exist. The general strategy consists of carrying out a selective and sequential segmentation from the cloud of points by means of different algorithms which depend on the information that the RFID tags provide. The identification of basic elements of the scene, such as walls, floor, ceiling, windows, doors, tables, chairs and cabinets, and the positioning of their corresponding models can then be calculated. The fusion of both technologies thus allows a simplified 3D semantic indoor model to be obtained. This method has been tested in real scenes under difficult clutter and occlusion conditions, and has yielded promising results.

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