Building envelopes and regional conditions can significantly contribute to the cost and energy performance of the buildings. Structured methods that take into account the impacts of both the envelope materials and the regional conditions to find the most feasible envelope materials within a region, however, are still missing. This study responds to this need by proposing a novel method using the capabilities of Building Information Modeling (BIM). The proposed method is used for identifying costand energy-efficient building envelope materials within a region over the life cycle. First, commonly used envelope materials in a region are identified. Then, BIM is employed for simulating the energy performance and evaluating the life cycle cost of the materials. The method was implemented in Tehran, Iran. It was successfully utilized for improving the cost and energy performance of a nine-story residential building case. The achieved results indicated a potential energy performance enhancement of 31%, and the life cycle cost improvement of 28% by replacing conventionally used envelope materials with the available high-performance building materials. The proposed method can benefit various stakeholders in the building construction industry, including municipalities, owners, contractors, and consumers, by enhancing the cost and energy performance of the buildings.
Floods are one of the most prevalent and costliest natural hazards globally. The safe transit of people and goods during a flood event requires fast and reliable access to flood depth information with spatial granularity comparable to the road network. In this research, we propose to use crowdsourced photos of submerged traffic signs for street-level flood depth estimation and mapping. To this end, a deep convolutional neural network (CNN) is utilized to detect traffic signs in user-contributed photos, followed by comparing the lengths of the visible part of detected sign poles before and after the flood event. A tilt correction approach is also designed and implemented to rectify potential inaccuracy in pole length estimation caused by tilted stop signs in floodwaters. The mean absolute error (MAE) achieved for pole length estimation in pre- and post-flood photos is 1.723 and 2.846 in., respectively, leading to an MAE of 4.710 in. for flood depth estimation. The presented approach provides people and first responders with a reliable and geographically scalable solution for estimating and communicating real-time flood depth data at their locations.
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