Material-Based Construction Site Image Retrieval

The technological advancements in digital imaging, the widespread popularity of digital cameras, and the increasing demand by owners and contractors for detailed and complete site photograph logs have triggered an ever-increasing growth in the rate of construction image data collection, with thousands of images being stored for each project. However, the sheer volume of images and the difficulties in accurately and manually indexing them have generated a pressing need for methods that can index and retrieve images with minimal or no user intervention. This paper reports recent developments from research efforts in the indexing and retrieval of construction site images in architecture, engineering, construction, and facilities management image database systems. The limitations and benefits of the existing methodologies will be presented, as well as an explanation of the reasons for the development of a novel image retrieval approach that not only can recognize construction materials within the image content in order to index images, but also can be compatible with existing retrieval methods, enabling enhanced results.

Content-Based Search Engines for Construction Image Databases

Images represent a valuable source of information for the construction industry. Due to technological advancements in digital imaging, the increasing use of digital cameras is leading to an ever-increasing volume of images being stored in construction image databases and thus makes it hard for engineers to retrieve useful information from them. Content-Based Search Engines are tools that utilize the rich image content and apply pattern recognition methods in order to retrieve similar images. In this paper, we illustrate several project management tasks and show how Content-Based Search Engines can facilitate automatic retrieval, and indexing of construction images in image databases.

The flow of steel into the construction sector

Half of the world's annual production of steel is used in constructing buildings and infrastructure. Producing this steel causes significant amounts of carbon dioxide to be released into the atmosphere. Climate change experts recommend this amount be halved by 2050; however steel demand is predicted to have doubled by this date. As process efficiency improvements will not reach the required 75% reduction in emissions per unit steel output, new methods must be examined to deliver service using less steel production. To apply such methods successfully to construction, it must first be known where steel is used currently within the industry. This information is not available so a methodology is proposed to estimate it from known data. Results are presented for steel flows by product for ten construction sectors for both the UK and the world in 2006. An estimate for steel use within a 'typical' building is also published for the first time. Industrial buildings and utility infrastructure are identified as the largest end-uses of steel, while superstructure is confirmed as the main use of steel in a building. The results highlight discrepancies in previous steel estimates and life-cycle assessments, and will inform future research on lowering demand for steel, hence reducing carbon emissions. © 2012 Elsevier B.V. All rights reserved.

Multi-Modal Image Retrieval from Construction Databases and Model-Based Systems

In the modern and dynamic construction environment it is important to access information in a fast and efficient manner in order to improve the decision making processes for construction managers. This capability is, in most cases, straightforward with today’s technologies for data types with an inherent structure that resides primarily on established database structures like estimating and scheduling software. However, previous research has demonstrated that a significant percentage of construction data is stored in semi-structured or unstructured data formats (text, images, etc.) and that manually locating and identifying such data is a very hard and time-consuming task. This paper focuses on construction site image data and presents a novel image retrieval model that interfaces with established construction data management structures. This model is designed to retrieve images from related objects in project models or construction databases using location, date, and material information (extracted from the image content with pattern recognition techniques).