The application of Augmented Reality (AR) in the Architecture Engineering and Construction (AEC) industry

Augmented Reality (AR) as a concept has been in use for many years and prevalence of new mobile technologies, such as smartphones and handheld devices, have facilitated the concept of AR becoming fully realized. Various fields are exploiting the increasing feasibilities the concept of AR can offer; one of these being the Architecture, Engineering and Construction (AEC) industry. This paper introduces a research project that investigates benefits and limitations of AR for use in AEC industry. It starts with a brief background to the research before presenting a critical literature review, which forms the basis for the development and design of an AR experiment and a questionnaire for participants in the study. Results are provided with an in-depth discussion on their possible significance, before a conclusion is presented. The results suggest that although the participants believed that AR can offer a wide range of benefits to different tasks and at different stages of a project, it seems more beneficial to some specific tasks or at some specific stages than the others. Using the specific findings of this study future research in this field is proposed in different areas

The role of linked building data (LBD) in aligning augmented reality (AR) with sustainable construction

Over the years, the construction industry has been evolving to embrace the delicate balance between buildings and a sustainable environment by optimizing resource use to create greener and more energy efficient constructions. Sustainable building design and optimization is a highly iterative and complicated process. This is mainly attributed to the complex interaction between the different heterogenous but heuristic construction processes, building systems and workflows involved in achieving this goal. Augmented Reality (AR) has rapidly emerged as a revolutionary technology that could play a key role towards improving coordination of sustainable design processes. AR makes possible the real-time visualization of a three-dimensional (3D) building prototype with linked design information in a real-world environment based on a two-dimensional drawing. From past research, it is evident that this technology relies heavily on a common data environment (CDE) that syncs all construction processes with their related building information in one central model. However, due to the fragmented nature of the construction industry, different domain experts generate and exchange vast amounts of heterogenous information using different software tools outside a CDE. This paper therefore investigates the performance gap that exists within Malaysia’s construction industry towards using linked building data (LBD) with AR to improve the lifecycle sustainability of buildings. The results of this study clearly delineate how current construction practices in Malaysia do not favor the use of AR however, stakeholder perception is positive towards adoption of workflows that link heterogenous building data to streamline AR with sustainable building design and construction

Capabilities and Challenges Using Machine Learning in Tunnelling

Digitalization changes the design and operational processes in tunnelling. The way of gathering geological data in the field of tunnelling, the methods of rock mass classification as well as the application of tunnel design analyses, tunnel construction processes and tunnel maintenance will be influenced by this digital transformation. The ongoing digitalization in tunnelling through applications like building information modelling and artificial intelligence, addressing a variety of difficult tasks, is moving forward. Increasing overall amounts of data (big data), combined with the ease to access strong computing powers, are leading to a sharp increase in the successful application of data analytics and techniques of artificial intelligence. Artificial Intelligence now arrives also in the fields of geotechnical engineering, tunnelling and engineering geology. The chapter focuses on the potential for machine learning methods – a branch of Artificial Intelligence - in tunnelling. Examples will show that training artificial neural networks in a supervised manner works and yields valuable information. Unsupervised machine learning approaches will be also discussed, where the final classification is not imposed upon the data, but learned from it. Finally, reinforcement learning seems to be trendsetting but not being in use for specific tunnel applications yet

Analysis of disruptions caused by construction field rework on productivity in residential projects

Operational performance in residential construction production systems is assessed based on measures such as average house-completion time, number of houses under construction, lead time, and customer service. These systems, however, are prone to nonuniformity and interruptions caused by a wide range of variables such as inclement weather conditions, accidents at worksites, fluctuations in demand for houses, and rework. The availability and capacity of resources therefore are not the sole measures for evaluating construction production systems capacity, especially when rework is involved. The writers’ aim is to investigate the effects of rework timeframe and frequency/length on tangible performance measures. Different call-back timeframes for rework and their impact on house-completion times are modeled and analyzed. Volume home-building was chosen as the industry sector studied in the research reported in this paper because it is a data-rich environment. The writers designed several experiments to model on time, late, and early call-back timeframes in the presence of rework with different length and frequency. Both mathematical modeling and discrete-event simulation were then used to compare and contrast outputs. The measurements showed that the average completion time is shorter in systems interrupted by frequent but short rework. In other words, a smaller downstream buffer between processes is required to avoid work starvation than those systems affected by infrequent but long interruptions. Early call-backs for rework can significantly increase the number of house completions over the long run. This indicates that there is an opportunity for the mass house-building sector to improve work practice and project delivery by effectively managing rework and its related variables. The research reported in this paper builds on the current body-of-knowledge by applying even-flow production theory to the analysis of rework in the residential construction sector, with the intention of ensuring minimal disruption to construction production process and improving productivity

Optimization of process integration and multi-skilled resource utilization in off-site construction

Traditional approaches in construction project management assign each process to a trade contractor with an individual specialisation, and trades with the greatest work content (bottlenecks) have a significant influence on the progress rate of projects. A system with integrated processes, however, is able to function dynamically in response to variability in product demand and labour resources. This investigation aims to compare and contrast cross-training strategies that are applicable to off-site construction in order to create multi-skilled resources. To this end, the optimal number of additional skills was formulated as a constrained optimization problem. Then, production data from two prefabricated production facilities in Melbourne and Brisbane, Australia were used to construct a total of 1080 simulation experiments. Tangible performance metrics of systems were used to compare process integration strategies and use of multi-skilled resources. Findings show that choosing optimal process integration architecture depends on the level of capacity imbalance and processing time variability. This investigation optimises the decision making on process integration in off-site construction networks

Integrated Information Modeling And Visual Simulation Of Engineering Operations Using Dynamic Augmented Reality Scene Graphs

This paper describes research that investigated the applicability of 3D Augmented Reality (AR) in visualizing dynamic engineering operations. The research resulted in the design and development of ARVISCOPE, a general purpose high level AR animation scripting language, and ROVER, a mobile computing hardware framework. When used together, ARVISCOPE and ROVER can create 3D AR animations of arbitrary length and complexity at the operations level of detail. ARVISCOPE takes advantage of advanced Global Positioning System (GPS) and 3D orientation tracking technologies to accurately track a user\u27s spatial context while georeferencing superimposed dynamic 3D graphics in an augmented environment. In achieving the research objectives, major technical challenges such as accurate registration, automated occlusion handling, and dynamic scene construction and manipulation were encountered and successfully addressed. This paper, describes the methodology and technical details of how scene graphs enable the process of constructing and updating an augmented scene representing a dynamic engineering operation. © 2011 The authors

Coupling Mobile Technology, Position Data Mining, and Attitude toward Risk to Improve Construction Site Safety

Construction sites comprise constantly moving heterogeneous resources that interact in close proximity of each other. The sporadic nature of such interactions creates an accident prone physical space surrounding workers. Despite efforts to improve site safety using location-aware proximity sensing techniques, major scientific gaps still remain in reliably forecasting impending hazardous scenarios before they occur. In the research documented in this thesis, spatiotemporal data of workers and site hazards are fused with a quantifiable model of an individual\u27s attitude toward risk to generate proximity-based safety alerts in real time. In particular, two trajectory prediction models, namely polynomial regression (PR) and hidden Markov model (HMM) are investigated and their effectiveness in predicting a worker\u27s position given his or her past movement trajectory is evaluated. Next, HMM prediction is further improved and calibrated by factoring in a worker\u27s risk profile, a measure of his affinity for or aversion to risky behavior near hazards. Finally, a mobile application is designed and tested in a series of field experiments involving trajectories of different shape and complexity to verify the applicability and value of the designed methodology in addressing construction safety-related problems. Results demonstrate that the developed risk-calibrated HMM-based motion trajectory prediction can reliably detect unsafe movements and impending collision events

Theory and Practice of Tunnel Engineering

Tunnel construction is expensive when compared to the construction of other engineering structures. As such, there is always the need to develop more sophisticated and effective methods of construction. There are many long and large tunnels with various purposes in the world, especially for highways, railways, water conveyance, and energy production. Tunnels can be designed effectively by means of two and three-dimensional numerical models. Ground–structure interaction is one of the significant factors acting on economic and safe design. This book presents recent data on tunnel engineering to improve the theory and practice of the construction of underground structures. It provides an overview of tunneling technology and includes chapters that address analytical and numerical methods for rock load estimation and design support systems and advances in measurement systems for underground structures. The book discusses the empirical, analytical, and numerical methods of tunneling practice worldwide

Potenciais aplicações de tecnologias da Construção 4.0 em sistemas construtivos modulares em estrutura de aço

TCC (graduação) - Universidade Federal de Santa Catarina, Centro Tecnológico, Engenharia Civil.A construção civil é um setor de grande importância no cenário da indústria global. Apesar do seu significativo impacto econômico, é fortemente criticado pela baixa produtividade, baixa agilidade, imprecisão de prazos e custos; sendo um setor que apresenta um dos menores investimentos em pesquisa e desenvolvimento, impactando negativamente na sua inovação. Nesse contexto, novas tecnologias relacionadas à Indústria 4.0 estão surgindo e podem proporcionar benefícios às empresas de construção, na chamada Construção 4.0, especialmente quando aplicadas à construção fora do canteiro, como no caso da construção modular. Apesar deste potencial, pouco se sabe da relação entre as práticas relativas à Indústria 4.0 e à construção modular. Neste sentido, este trabalho objetiva-se identificar oportunidades para a incorporação de práticas da Construção 4.0 nas etapas do ciclo da construção modular em estrutura de aço. Para tanto, uma pesquisa qualitativa foi conduzida, baseada na revisão de literatura e em entrevistas a dois profissionais do setor da construção modular. Os resultados sintetizam-se em um Quadro, relacionando potenciais aplicações de Tecnologias da Indústria 4.0 nas etapas da construção modular. Especificamente, estas aplicações indicam opções para melhorar os resultados das etapas da construção modular em termos de automação e acompanhamento de processos, auxiliando na tomada de decisões.Construction industry is a sector of great importance in the global industry scenario. Despite its significant economic impact, it is heavily criticized for low productivity, low agility, imprecision of deadlines and costs; being a sector that has one of the lowest investments in research and development, negatively impacting its innovation. In this context, new technologies related to Industry 4.0 are emerging and can provide benefits to construction companies, in the so-called Construction 4.0, especially when applied to offsite construction, as in the case of modular construction. Despite this potential, little is known about the relationship between practices related to Industry 4.0 and modular construction. In this sense, this work aims to identify opportunities for the incorporation of Construction 4.0 practices in the stages of the construction cycle in modular steel structures. To this end, qualitative research was conducted, based on a literature review and interviews with two professionals in the modular construction sector. The results are summarized in a Table, listing potential applications of Industry 4.0 technologies in the stages of modular construction. Specifically, these applications indicate options to improve the results of the modular construction stages in terms of automation and process monitoring, helping in decision making