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Abstract:Benchmarking is a systematic process of measuring and comparing an organization's performance against that of other similar organizations in key business activities. The lessons learned from other companies can be used to establish improvement targets and to promote changes in the organization. The benchmarking process can create a fertile ground for ideas, but only in a receptive environment; companies that share good practices and compare their performance against others benefit most. Recently, industry groups in several different countries have initiated benchmarking programs focused mainly on construction performance measures. This paper describes the scope of these initiatives and discusses the lessons learned and improvement opportunities that were identified in their design and implementation. This investigation is focused on four initiatives, carried out in Brazil, Chile, the United Kingdom, and the United States. This paper concludes by recommending some further directions on this research topic.

Visual recording devices such as video cameras, CCTVs, or webcams have been broadly used to facilitate work progress or safety monitoring on construction sites. Without human intervention, however, both real-time reasoning about captured scenes and interpretation of recorded images are challenging tasks. This article presents an exploratory method for automated object identification using standard video cameras on construction sites. The proposed method supports real-time detection and classification of mobile heavy equipment and workers. The background subtraction algorithm extracts motion pixels from an image sequence, the pixels are then grouped into regions to represent moving objects, and finally the regions are identified as a certain object using classifiers. For evaluating the method, the formulated computer-aided process was implemented on actual construction sites, and promising results were obtained. This article is expected to contribute to future applications of automated monitoring systems of work zone safety or productivity.

Automated tracking of materials on construction projects has the potential to both improve project performance and enable effortless derivation of project performance indicators. This paper presents an approach by which materials tagged with radio frequency identification ͑RFID͒ tags can be automatically identified and tracked on construction sites, without adding to regular site operations. Essentially, this approach leverages automatic reading of tagged materials by field supervisors or materials handling equipment that are equipped with a RFID reader and a global positioning system receiver. To assess the technical feasibility of this approach, a mathematical model has been formulated such that the job site is represented as a grid and the location of materials within the grid is determined by combining proximity reads from a discrete range. Field experiments were conducted using an off-the-shelf RFID technology, and several metrics were developed to quantify the field performance and compare it with the theoretical positional accuracy derived from the discrete formulation.

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