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The purpose of a routine bridge inspection is to assess the physical and functional condition of a bridge according to a regularly scheduled interval. The Federal Highway Administration (FHWA) requires these inspections to be conducted at least every 2 years. Inspectors use simple tools and visual inspection techniques to determine the conditions of both the elements of the bridge structure and the bridge overall. While in the field, the data is collected in the form of images and notes; after the field work is complete, inspectors need to generate a report based on these data to document their findings. The report generation process includes several tasks: (1) evaluating the condition rating of each bridge element according to FHWA Recording and Coding Guide for Structure Inventory and Appraisal of the Nation’s Bridges; and (2) updating and organizing the bridge inspection images for the report. Both of tasks are time-consuming. This study focuses on assisting with the latter task by developing an artificial intelligence (AI)-based method to rapidly organize bridge inspection images and generate a report. In this paper, an image organization schema based on the FHWA Recording and Coding Guide for the Structure Inventory and Appraisal of the Nation’s Bridges and the Manual for Bridge Element Inspection is described, and several convolutional neural network-based classifiers are trained with real inspection images collected in the field. Additionally, exchangeable image file (EXIF) information is automatically extracted to organize inspection images according to their time stamp. Finally, the Automated Bridge Image Reporting Tool (ABIRT) is described as a browser-based system built on the trained classifiers. Inspectors can directly upload images to this tool and rapidly obtain organized images and associated inspection report with the support of a computer which has an internet connection. The authors provide recommendations to inspectors for gathering future images to make the best use of this tool.

This article provides an assessment of the embodied and operational energy during construction and occupation phases of three main construction methods on social dwellings in Ecuador. The analysis focuses on current construction practices in the two largest Ecuadorian cities: Guayaquil and Quito, represent- ing typical coastal and highland climates of the country respectively. A model of a social interest dwelling that follows the guidelines of the Ecuadorian Housing Secretary is defined. Three different construction methods were considered: Isolated Concrete Forms (ICF), concrete Load Bearing Walls (LBW) and traditional beams-columns Confined Masonry (CM) systems. The assessment also includes two roofing alternatives: concrete slabs defined for each construction method (Sa, Sb, Sc) and metallic alloy roofs (Z). The embodied energy assessment focuses on the materials required for each construction technique, and uses a craddle-to-gate life cycle assessment (LCA) approach. The operational energy evaluation uses the cooling energy to reach comfort inside the dwelling by simulations with EnergyPlus. CM dwelling units using zinc-alloy roofs show the lowest embodied energy results (2.0 GJ/m2) while ICF structures using concrete slab have the highest (3.2 GJ/m2). In terms of operational energy, the lowest consumption corresponds to ICF units with slab roof (13.6 GJ/m2) while the largest are LBW units with zinc-alloy roofs (65.2 GJ/m2). Most of the energy consumed during the life cycle corresponds to operational energy with a range between 81.1% and 97.0%. In conclusion, possible energy savings between 49% and 69% through the life cycle are identified by switching from a CM system to ICF system. Hence, these results have potential impact on housing and energy policy in Ecuador.