Keywords

Behavior decisions
Fire Emergency Management System (FEMS)
Building information modeling (BIM)

Researcher

BIM-based building fire emergency management: Combining building users' behavior decisions

Abstract

The application of advanced technology and building user’s different behavior decisions has an important impact on the performance of fire emergency management. Currently, although many studies have focused on developing advanced technology and improving fire emergency management effectiveness, advanced technology often fails because of the inapplicability or non-use of the technology. Therefore, it is necessary to combine building users’ behavior decisions with advanced technology and propose fire response modes that are adapted for such users. Based on the Building Information Modeling (BIM) platform, this paper builds a fire emergency management system (FEMS) that considers the behavior decisions of building users (behaviors such as escape, wait for rescue, and fire extinguishing). We develop a database management module and four functional modules that include fire intelligence-monitoring, fire-warning, fire-response, and fire-treatment. The system realizes the dynamic monitoring of building fires, the judgment of fire category, the locations of users, and the ability to view fire-points in a three-dimensional (3D) model. In addition, this system can also plan optimal action routes according to different building user’s decisions, while the application (app) provides visual route guidance. Through a fire scene simulation experiment, we show that the moving distance of the Test Group equipped with FEMS is 30% shorter than the Control Group, and the total time spent is reduced by 48%. Also, the performance of each type of decision-makers’ behavior in the Test Group was better than that of the Control Group.

 

لینک مقاله در انتشارات:

https://doi.org/10.1016/j.autcon.2019.102975

Automatic re-planning of lifting paths for robotized tower cranes in dynamic BIM environments

Abstract

Computer-Aided Lift Planning (CALP) systems provide smart and optimal solutions for automatic crane lifting, supported by intelligent decision-making and planning algorithms along with computer graphics and simulations. Re-planning collision-free optimal lifting paths in near real-time is an essential feature for a robotized crane operating in a construction environment that is changing with time. The primary focus of the present research work is to develop a re-planning module for the CALP system designed at Nanyang Technological University. The CALP system employs GPU-based parallelization approach for discrete and continuous collision detection as well as for path planning. Building Information Modeling (BIM) is utilized in the system, and a Single-level Depth Map (SDM) representation is implemented to reduce the huge data set of BIM models for usage in discrete and continuous collision detection. The proposed re-planning module constitutes of a Decision Support System (DSS) and a Path Re-planner (PRP). A novel re-planning decision making algorithm using multi-level Oriented Bounding Boxes (OBBs) is formulated for the DSS. A path re-planning strategy via updating the start configuration for the local path is devised for the PRP. Two case studies are carried out with real-world models of a building and a specific tower crane to validate the effective performance of the re-planning module. The results show excellent decision accuracy and near real-time re-planning with high optimality.

لینک مقاله در انتشارات:

https://doi.org/10.1016/j.autcon.2019.102998

Keywords

Crane lifting path re-planning
Dynamic obstacles
Building Information Modeling (BIM)
Single-level depth map
Robotized tower cranes
Multi-level oriented bounding boxes

Researcher

Keywords

Building Information Modeling
BIM
Real-time visualization
Real-time rendering

Researcher

Real-time visualization of building information models (BIM)

Abstract

This paper highlights and addresses the complexity and challenges involved in visualizing large and detailed Building Information Models (BIM) in real-time. The contribution of the paper is twofold: (a) an in-depth analysis of four commonly used BIM viewers in terms of real-time rendering performance and (b) the development and validation of a prototype BIM viewer specifically designed to allow real-time visualization of large and complex building models. Regarding existing BIM viewers our results show that they all share limitations in their ability to handle large BIMs taken from real-world projects interactively. However, for the same test models our prototype BIM viewer is able to provide smooth real-time performance without sacrificing visual accuracy. By taking advantage of an efficient visibility determination algorithm, our prototype viewer restricts rendering efforts to visible objects only, with a significant performance increase compared to existing BIM viewers as a result.

لینک مقاله در انتشارات:

https://doi.org/10.1016/j.autcon.2015.03.018

Integrated BIM, game engine and VR technologies for healthcare design: A case study in cancer hospital

Abstract

The results of healthcare design should meet the requirements of design teams as well as healthcare stakeholders. However, misunderstandings that occur between the design teams and healthcare stakeholders when using 2D illustrations leads to the need for re-design and rework during the design phase. To overcome this problem, this study develops a Database-supported VR/BIM-based Communication and Simulation (DVBCS) system integrated with BIM, game engine and VR technologies for healthcare design special in the Semi-immersed VR environment. The DVBCS system is applied in a case study of a design project of a cancer center in Taiwan to verify the system and demonstrate its effectiveness in practice. The results demonstrate that a DVBCS system is an effective visual communication and simulation platform for healthcare design. The advantage of the DVBCS system lies not only in improving the communication efficiency between the design teams and healthcare stakeholders, but also in facilitating visual interactions and easing the decision-making process while communicating in the 3D VR/BIM environment. The effective use of the proposed DVBCS system will assist design teams and stakeholders significantly in systematically handling healthcare design work in future healthcare design.

لینک مقاله در انتشارات:

https://doi.org/10.1016/j.aei.2018.03.005

Keywords

Building information modeling
BIM
Healthcare design
Virtual reality
Game engine
Design phase
Communication
Semi-immersed VR

Researcher

Keywords

Building information modeling (BIM)
Facility management
IoT
Operation and maintenance (O&M)
Utility tunnels

Researcher

A BIM-based framework for operation and maintenance of utility tunnels

Abstract

Underground utility tunnels are crucial for urban areas with high density in both population and number of buildings since they can tremendously improve the utilization of land resources. However, the operation & maintenance (O&M) of such underground tunnels is tedious and challenging, due to the fact that there is a lack of effective information technologies to facilitate O&M of utility tunnels. Although Building information modelling (BIM) technology is asserted to provide a game-changing solution to address the challenges encountered in the AEC industry, the use of BIM for operating and managing utility facilities in an efficient manner is rarely explored in the existing literature. To address this gap, a novel framework is proposed in this research to promote the sustainable O&M of utility tunnels with the support of BIM. Specifically, this framework encompasses three modules, namely, BIM model, O&M database, and monitoring system. A detailed description of each component of the proposed framework, as well as a preliminary user interface design, is presented in this paper. An implementation example is presented to provide the validation of the proposed framework. The preliminary results indicate the feasibility of the proposed framework in facilitating the information integration and communication of utility tunnels. This research contributes to the main body of knowledge by proposing a generic BIM framework for sustainable O&M of utility tunnels and formalizing data requirements as well as management workflow intended for utility tunnel O&M.

لینک مقاله در انتشارات:

https://doi.org/10.1016/j.tust.2019.103252

A BIM-based framework for quantitative assessment of steel structure deconstructability

Abstract

Recently, attention is being focused on Design for Deconstruction (DfD) as one of the most effective structure end-of-life design scenarios. Although several research efforts have dealt with the DfD and reversible buildings design theories for the past few years, the lack of technological support for developing tools to improve DfD to be BIM compliant has been clearly noticed. This research presents a Deconstructability Assessment Scoring (DAS) methodology for quantitative assessment of Steel Structures deconstructability. The proposed methodology takes into consideration a number of steel parameters and their deconstructability. Seven innovative solutions contributing to steel connections deconstructability are considered in the proposed research. Furthermore, steel elements reusable fire-proofing systems are introduced to provide the required fire resistance and deconstructability. The detailed analyses for the selected parameters are adopted and implemented to form the Steel Structure Deconstructability Assessment Scoring (SS-DAS). The proposed methodology is implemented in Autodesk Revit using Dynamo. The proposed innovative solutions are modeled as families to automate the scoring process.

لینک مقاله در انتشارات:

https://doi.org/10.1016/j.autcon.2019.103064

Keywords

Building Information Modeling
Steel structure
Design for Deconstruction (DfD)
Deconstructability Assessment Score (DAS)
Dynamo
Revit

Researcher

Keywords

Path planning
Indoors
Mobility
BIM
IFC

Researcher

A BIM-based method to plan indoor paths

Abstract

Humans more and more live in urban areas and spend the majority of their time indoors, while buildings are becoming increasingly interconnected. Path planning in complex indoor environments is consequently becoming a societal challenge. This paper proposes a system, called BiMov, dedicated to automatically determining plan paths in complex buildings based on a digital mock-up (a BIM in IFC format). The originality of the research comes from the separation of the BIM model topological analysis from the semantic and geometric consideration of the customer of the path planning service. The process consists in exploiting the semantic, geometric, and topological features of the constituents of a BIM to generate navigation graphs that take the profile of MOoP (Mobile Object or Person) and the operational accessibility status of spaces and transitions (such as doors or stairs) into account. The ultimate aim is to determine the optimal path according to given criteria. The system is based on four data models: (1) a building model derived from the original BIM that represents and structures the building features relevant to indoor mobility; (2) a MOoP model representing its bulk size, ability to move horizontally and vertically, and various authorisations; (3) a calendar model representing the conjectural accessibility status of spaces and transitions; (4) a navigation graph model proposing three levels of detail. The Macro level represents a simple graph of connectivity between neighbouring interior spaces and is intended to help architects verify their design in terms of accessibility. The External level is used to connect accessible spaces via their common horizontal or vertical transitions. This level is intended for MOoPs that do not require a detailed description of the path. The Internal level integrates meshing of each space: a 2D mesh for planar mobility or a 3D mesh for drones. This level is intended for MOoP such as heavy object handlers or autonomous mobile vehicles that need to validate a reliable path within spaces containing furniture, hazardous machinery, or bulky equipment. We developed a prototype software application that illustrates our approach for different path planning scenarios in a BIM model generated outside this project.

لینک مقاله در انتشارات:

https://doi.org/10.1016/j.autcon.2020.103120

Automated modification of compound elements for accurate BIM-based quantity takeoff

Abstract

BIM-based quantity takeoff is faster, more accurate, and more reliable than traditional quantity takeoff. However, the quality of BIM models is a major issue. Quantity takeoff is inaccurate for compound elements such as walls and floors modeled as single model elements with defined material layers because the size and composition of each layer cannot be freely adjusted. Furthermore, overlapping regions of compound elements with other elements result in excess material quantities. Manual inspection and modification of each compound element are time-consuming, cost-intensive, and error-prone. This study proposes a method to automatically modify compound elements in BIM models by separating each layer into an individual model element and eliminating overlapping regions. Accurate material quantities for compound elements can be obtained from the modified BIM models. A prototype system was developed and the proposed method was validated by two case studies. Compared to manual modification, the proposed method successfully modified compound elements in BIM models in much less time, with accurate material quantities. The modified BIM models are also beneficial in the construction phase, which requires greater detail regarding BIM elements.

لینک مقاله در انتشارات:

https://doi.org/10.1016/j.autcon.2020.103142

Keywords

Building Information Modeling (BIM)
Quantity takeoff
BIM-based quantity takeoff
Quantification
Compound element

Researcher

Keywords

BIM
Log data mining
Long short-term memory neural network
Design command prediction

Researcher

BIM log mining: Learning and predicting design commands

Abstract

This paper develops a framework to learn and predict design commands based upon building information modeling (BIM) event log data stored in Autodesk Revit journal files, which has the potential to improve the modeling efficiency. BIM design logs, which automatically keep detailed records on the modeling process, are the basis of data acquisition and data mining. Long Short-Term Memory Neural Network (LSTM NN), as a probabilistic deep learning model for learning sequential data with varying lengths from logs, is established to provide designers with predictions about the possible design command class in the next step. To demonstrate the feasibility of this method, a case study runs at large design logs over 4 GB from an international design firm for command class prediction. To begin with, useful data retrieved from logs is cleaned and saved in a 320 MB Comma Separated Values (CSV) file with totally 352,056 lines of commands over 289 projects. Subsequently, various design commands are categorized into 14 classes according to their effects and given numerical labels, which are then fed into LSTM NN for training and testing. As a result, the overall accuracy of this particular case study can reach 70.5% in the test set, which outperforms some classical machine learning methods, like k nearest neighbor, random forest and support vector machine. This research contributes to applying a probabilistic LSTM NN with optimal parameters to learn features from designers’ subjective behaviors effectively and predict the next possible design command class intelligently towards automation of the design process. Moreover, the three most possible command classes will be offered as the recommendations under the assumption that the correct class tends to appear owning the top three highest probabilities, which can possibly enhance the reliability of predictions.

لینک مقاله در انتشارات:

https://doi.org/10.1016/j.autcon.2020.103107

BIM-based decision support system for automated manufacturability check of wood frame assemblies

Abstract

As offsite construction is increasing in popularity, an increasing number of construction products are fabricated in a controlled factory environment. Due to the complexity of construction products and the rising amount of automation used in the industry, productivity has reached a peak because the process planning of manufacturing activities is still done manually, for example, building information models (BIM) do not provide manufacturing information for construction products. Knowing whether a machine can manufacture a construction product defined by the BIM model is a critical prerequisite for new products. This paper proposes a BIM-based framework for automating the evaluation of machine capabilities for the manufacturing of construction-oriented products. By identifying intersections of the building elements of the product, feasible manufacturing operations are determined, and manufacturing locations are calculated. These locations are then compared to the manufacturing capabilities of the machine. The proposed approach is validated using two wood frame assemblies. The results show that the system accurately determines whether a user-selected machine can manufacture a construction product pre-designed using BIM software.

لینک مقاله در انتشارات:

https://doi.org/10.1016/j.autcon.2019.103065

Keywords

Building information modeling
Ontologies
Intelligent manufacturing
Wood framing
Construction automation
Computer numerical control
Mass customization

Researcher

Keywords

Building information modelling
Health and safety
Occupational risk assessment

Researcher

BIM-integrated management of occupational hazards in building construction and maintenance

Abstract

Health and safety in the construction sector are very important issues owing to the high accident rate in the industry. Recent studies have shown that implementing the building information modelling (BIM) methodology can improve the working conditions at construction sites and during building maintenance. Therefore, the European Union is promoting the development of projects through BIM. The government of Spain has established a roadmap to enforce the development of projects with BIM, and the integration of occupational health and safety in projects developed with BIM in Spain must comply with the current regulations. This study proposes a methodology—consistent with the requirements stipulated by the Spanish health and safety regulations—for its integration in the design phase of building projects developed using BIM.

لینک مقاله در انتشارات:

https://doi.org/10.1016/j.autcon.2020.103115

BIM-based decision support system for automated manufacturability check of wood frame assemblies

Abstract

With rapid advances in building information modeling (BIM), a huge amount of BIM components has been built to increase design efficiency. Meanwhile, finding the appropriate BIM component in the huge library has become a challenge. Besides the methods of case-based reasoning (CBR) or multi-attribute decision model (MADM), the probabilistic matrix factorization (PMF) method of a recommendation system can be an efficient alternative. However, the user behavior patterns (i.e., the rating matrices) are changing with time to influence the recommendation precision. Therefore, this study aims to enhance the dynamic recommendation ability for BIM components by proposing a hybrid probabilistic matrix factorization method (PMF-GMn). The latent user preference matrix and the latent BIM component feature matrix can be generated by the PMF method from the rating matrix. Then, the predicted latent matrices can be obtained by the optimized grey model. Finally, the predicted latent matrices are further combined into the predicted rating matrix to recommend the appropriate BIM components. An illustrative example of the prefabricated building design is used to demonstrate the feasibility. This experiment is implemented by inviting twenty users to use the proposed SharePBIM platform for five months. The statistical results indicated that PMF-GMn can provide better performance than PMF in both two criteria of RMSE and Recall@ k

“>k

.

لینک مقاله در انتشارات:

https://doi.org/10.1016/j.aei.2019.101024

Keywords

Building information modeling
Grey model
Prefabricated building
Probabilistic matrix factorization
Recommendation system

Researcher

Keywords

Building information modeling
Data driven approach
Facility management
Internet of Things
Predictive maintenance
Machine learning

Researcher

Data-driven predictive maintenance planning framework for MEP components based on BIM and IoT using machine learning algorithms

Abstract

Facility managers usually conduct reactive maintenance or preventive maintenance strategies in building maintenance management. However, there are some limitations that reactive maintenance cannot prevent failure, and preventive maintenance cannot predict the future condition of MEP components and repair in advance to extend the lifetime of facilities. Therefore, this study aims to apply a predictive maintenance strategy with advanced technologies to overcome these limitations. Building information modeling (BIM) and Internet of Things (IoT) have the potential to improve the efficiency of facility maintenance management (FMM). Despite the significant efforts that have been made to apply BIM and IoT to the architecture, engineering, construction, and facility management (AEC/FM) industry, BIM and IoT integration for FMM is still at an initial stage. In order to provide a better maintenance strategy for building facilities, a data-driven predictive maintenance planning framework based on BIM and IoT technologies for FMM was developed, consisting of an information layer and an application layer. Data collection and data integration among the BIM models, FM system, and IoT network are undertaken in the information layer, while the application layer contains four modules to achieve predictive maintenance, namely: (1) condition monitoring and fault alarming module, (2) condition assessment module, (3) condition prediction module, and (4) maintenance planning module. Machine learning algorithms, ANN and SVM, are used to predict the future condition of MEP components. Furthermore, the developed framework was applied in an illustrative example to validate the feasibility of the approach. The results show that the constantly updated data obtained from the information layer together with the machine learning algorithms in the application layer can efficiently predict the future condition of MEP components for maintenance planning.

    لینک مقاله در انتشارات:

    https://doi.org/10.1016/j.autcon.2020.103087

    BIM-based life cycle environmental performance assessment of single-family houses: Renovation and reconstruction strategies for aging building stock in British Columbia

    Abstract

    The building sector accounts for 40% of the energy use and one-third of the greenhouse gas (GHG) emissions globally. Buildings deteriorate with age, which leads to a decrease in their energy performance. Therefore, it is significant to improve the energy performance of aging building block although it is challenging. The two main decision paths for municipalities are renovation and reconstruction. This study investigated the above options for the older housing stock in a densely populated urban centre in British Columbia, Canada. A scenario-based analysis approach was taken to evaluate the life cycle GHG emissions of six different renovation and reconstruction scenarios. The total life cycle emissions were calculated for each scenario including the embodied and operational emission, and the emissions from building construction and maintenance. A BIM-LCA combined approach was used to assess the embodied GHG emissions with the SimaPro software. HOT2000 software was used to model the operational GHG emissions. The results show that in the reconstruction scenarios, around 40% of the emissions are from the material manufacturing stage. The embodied emissions generated from the reconstruction scenarios are 5–6 times higher than the renovation scenarios. The life cycle GHG emissions of the existing house can be reduced by applying renovations, with the emissions saving gradually increasing with the level of retrofitting. The passive house reconstruction scenario delivers the greatest benefit in terms of life cycle emissions reduction compared to all other scenarios. In terms of the GHG emission intensity per unit area, the newly-built houses in scenarios 5 and 6 also have lower life cycle GHG emission per square meter than the renovated existing houses in scenario 1–4 after 15 and 10 years to breakeven respectively. Based on this, it can be concluded that when considering the older existing building stock, a careful weighing of options must take place before making the decision on replacing them with new construction. However, it is also important to consider the economic and social aspects before making decisions to renovate or replace existing houses. The study outcomes will support city planners and urban development planners to make decisions on BC aging building stock development, especially in high population density neighbourhoods.

    لینک مقاله در انتشارات:

    https://doi.org/10.1016/j.jclepro.2019.119543

    Keywords

    BIM
    Building reconstruction
    Life cycle assessment
    Residential building
    Renovation
    GHG emissions

    Researcher

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