Collaborative Design Processes: A Class on Concurrent Collaboration in Multidisciplinary Design

The rise of concurrent engineering in construction demands early team formation and constant communication throughout the project life cycle, but educational models in architecture, engineering and construction have been slow to adjust to this shift in project organization. Most students in these fields spend the majority of their college years working on individual projects that do not build teamwork or communication skills. Collaborative Design Processes (CDP) is a capstone design course where students from the University of Illinois at Urbana-Champaign and the University of Florida learn methods of collaborative design enhanced by the use of information technology. Students work in multidisciplinary teams to collaborate from remote locations via the Internet on the design of a facility. An innovation of this course compared to previous efforts is that students also develop process designs for the integration of technology into the work of multidisciplinary design teams. The course thus combines both active and reflective learning about collaborative design and methods. The course is designed to provide students the experience, tools, and methods needed to improve design processes and better integrate the use of technology into AEC industry work practices. This paper describes the goals, outcomes and significance of this new, interdisciplinary course for distributed AEC education. Differences from existing efforts and lessons learned to promote collaborative practices are discussed. Principal conclusions are that the course presents effective pedagogy to promote collaborative design methods, but faces challenges in both technology and in traditional intra-disciplinary training of students

A Novel Building Temperature Simulation Approach Driven by Expanding Semantic Segmentation Training Datasets with Synthetic Aerial Thermal Images

Multi-sensor imagery data has been used by researchers for the image semantic segmentation of buildings and outdoor scenes. Due to multi-sensor data hunger, researchers have implemented many simulation approaches to create synthetic datasets, and they have also synthesized thermal images because such thermal information can potentially improve segmentation accuracy. However, current approaches are mostly based on the laws of physics and are limited to geometric models’ level of detail (LOD), which describes the overall planning or modeling state. Another issue in current physics-based approaches is that thermal images cannot be aligned to RGB images because the configurations of a virtual camera used for rendering thermal images are difficult to synchronize with the configurations of a real camera used for capturing RGB images, which is important for segmentation. In this study, we propose an image translation approach to directly convert RGB images to simulated thermal images for expanding segmentation datasets. We aim to investigate the benefits of using an image translation approach for generating synthetic aerial thermal images and compare those approaches with physics-based approaches. Our datasets for generating thermal images are from a city center and a university campus in Karlsruhe, Germany. We found that using the generating model established by the city center to generate thermal images for campus datasets performed better than using the latter to generate thermal images for the former. We also found that using a generating model established by one building style to generate thermal images for datasets with the same building styles performed well. Therefore, we suggest using training datasets with richer and more diverse building architectural information, more complex envelope structures, and similar building styles to testing datasets for an image translation approach

Semantic Modeling of Outdoor Scenes for the Creation of Virtual Environments and Simulations

Efforts from both academia and industry have adopted photogrammetric techniques to generate visually compelling 3D models for the creation of virtual environments and simulations. However, such generated meshes do not contain semantic information for distinguishing between objects. To allow both user- and system-level interaction with the meshes, and enhance the visual acuity of the scene, classifying the generated point clouds and associated meshes is a necessary step. This paper presents a point cloud/mesh classification and segmentation framework. The proposed framework provides a novel way of extracting object information – i.e., individual tree locations and related features while considering the data quality issues presented in a photogrammetric-generated point cloud. A case study has been conducted using data that were collected at the University of Southern California to evaluate the proposed framework

An Approach to Semantically Segmenting Building Components and Outdoor Scenes Based on Multichannel Aerial Imagery Datasets

As-is building modeling plays an important role in energy audits and retrofits. However, in order to understand the source(s) of energy loss, researchers must know the semantic information of the buildings and outdoor scenes. Thermal information can potentially be used to distinguish objects that have similar surface colors but are composed of different materials. To utilize both the red–green–blue (RGB) color model and thermal information for the semantic segmentation of buildings and outdoor scenes, we deployed and adapted various pioneering deep convolutional neural network (DCNN) tools that combine RGB information with thermal information to improve the semantic and instance segmentation processes. When both types of information are available, the resulting DCNN models allow us to achieve better segmentation performance. By deploying three case studies, we experimented with our proposed DCNN framework, deploying datasets of building components and outdoor scenes, and testing the models to determine whether the segmentation performance had improved or not. In our observation, the fusion of RGB and thermal information can help the segmentation task in specific cases, but it might also make the neural networks hard to train or deteriorate their prediction performance in some cases. Additionally, different algorithms perform differently in semantic and instance segmentation

Novel Technologies for Construction Field Data Collection

A vast growth of advanced information technology systems and tools nowadays is opening new ways to collect accurate as-built data. Since the turn of the millennium, new technology developments enable for the first time to gather accurate as-built information. Accurate as-built data will be of great usage to construction management as well as to designers and engineers. Given that most of the planned data are already digitally available, as-built data remains on paper forms. Information technology developments are opening new ways to digitize construction field data in order to develop intelligent tools for construction management allowing design engineers to update as-planned data. 3D Laser scanning, digital close-range photogrammetry and mobile computing are among the promising data collection technologies, which are auspicious to create new opportunities to develop advanced construction management and engineering tools. Primarily, accurate collected as-built data will be highly beneficial for the process of updating as-planned data

Data Fusion and Modeling for Construction Management Knowledge Discovery

Advances in construction data analysis techniques have provided useful tools to discover explicit knowledge on historical databases supporting project managers’ decision making. However, in many situations, historical data are extracted and preprocessed for knowledge discovery based on time-consuming and problem-specific data preparation solutions, which often results in inefficiencies and inconsistencies. To overcome the problem, we are working on the development of a new data fusion methodology, which is designed to provide timely and consistent access to historical data for efficient and effective management knowledge discovery. The methodology is intended to be a new bridge between historical databases and data analysis techniques, which shields project managers from complex data preparation solutions, and enables them to use discovered knowledge for decision making more conveniently. This paper briefly describes the motivation, the background and the initial results of the ongoing research

A Data-Driven Approach for Granular Simulation of Potential Earthquake Damage to Bridge Networks and Resulting Decreases in Mobility

Quantified investigation of resilience in regional transportation networks has been a growing research focus. Despite this increased attention, state-of-the-art studies fall short of devising and utilizing explicit transportation network models where infrastructure components (roads, bridges, etc.) and travel behaviors of network users are modeled in high fidelity. This study presents a novel model-based approach that couples a semi-automated, image-based structure-specific bridge modeling method with a metropolis-scale travel demand model towards achieving a comprehensive and high-resolution resilience assessment. As a result of its data-driven approach, the proposed method is capable of capturing and incorporating many details that are usually omitted in traditional analyses, promising improved accuracy in estimating the resilience and sustainability metrics of transportation networks. As a small-scale testbed for the proposed approach, this study displays the results of a preliminary investigation of potential seismic losses for the Los Angeles Metropolitan Area due to a hazard-consistent scenario earthquake primarily affecting the Ports of Los Angeles and Long Beach. This analysis makes use of structure-specific fragility functions of 200 bridges in the vicinity of the port facilities, generated from street-level imagery, and provides a detailed picture of the expected disruptions to truck freight mobility resulting from the scenario event

ESCANEAMENTO 3D A LASER, FOTOGRAMETRIA E MODELAGEM DA INFORMAÇÃO DA CONSTRUÇÃO PARA GESTÃO E OPERAÇÃO DE EDIFICAÇÕES HISTÓRICAS

Este artigo apresenta os resultados de integração de tecnologias de levantamento híbridas (escaneamento 3D a laser, fotogrametria) para a captura do estado real de uma edificação histórica do Campus da University of Southern California em Los Angeles visando a sua modelagem BIM. O estudo de caso é um edifício de dois andares, construído em 1964 em linguagem modernista, com estrutura metálica aparente, considerado patrimônio histórico recentemente. A escolha da abordagem híbrida pautou-se em critérios como: precisão dos dados levantados; nível de detalhe requerido para cada elemento do edifício; esforço para aquisição e pós-processamento dos dados; e acessibilidade ao elemento a ser capturado. Levando-se em conta os resultados obtidos, apresentamos a avaliação das ferramentas e estratégias empregadas para aquisição dos dados espaciais do edifício, em função dos seguintes critérios: escala, complexidade e alcance dos equipamentos. O modelo tridimensional em forma de nuvens de pontos gerado pela captura constitui a base para a criação de um modelo de informações de construção semanticamente orientado, ferramenta potencial para produzir um inventário abrangente que considere os requisitos de manutenção peculiares de edificações históricas. Esse artigo pretende contribuir para ampliar a discussão sobre a adoção de BIM na área de patrimônio histórico.This article presents the results of integrating hybrid-surveying technologies(3D laser scanning, photogrammetry) to capture the real state of a historic building on thecampus of the University of Southern California in Los Angeles aiming to create its BIM model.The case study is a two-story building, built in 1964 in modernist language, with apparentsteel structure, considered historical heritage recently. The adoption of hybrid approach wasguided on the following criteria: accuracy of data collected; level of detail required for eachelement of the building; effort to acquire and post-processing of data; and accessibility to theelement to be captured. Taking into account the results obtained, we present the evaluation ofthe tools and strategies used to acquire the building’s spatial data, according to the followingcriteria: scale, complexity and range of equipment. The point clouds three-dimensionalmodel, generated by the capturing tools, forms the basis for creating a semantically orientedbuilding information model. BIM is a potential tool to produce a comprehensive inventory thatconsiders the unique maintenance requirements of historic buildings. This article intends tocontribute to the discussion on the adoption of BIM in the heritage area