5,215 research outputs found
Classification of Building Information Model (BIM) Structures with Deep Learning
In this work we study an application of machine learning to the construction
industry and we use classical and modern machine learning methods to categorize
images of building designs into three classes: Apartment building, Industrial
building or Other. No real images are used, but only images extracted from
Building Information Model (BIM) software, as these are used by the
construction industry to store building designs. For this task, we compared
four different methods: the first is based on classical machine learning, where
Histogram of Oriented Gradients (HOG) was used for feature extraction and a
Support Vector Machine (SVM) for classification; the other three methods are
based on deep learning, covering common pre-trained networks as well as ones
designed from scratch. To validate the accuracy of the models, a database of
240 images was used. The accuracy achieved is 57% for the HOG + SVM model, and
above 89% for the neural networks.Comment: This work has been submitted to the IEEE for possible publication.
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Analytical modelling in Dynamo
BIM is applied as modern database for civil
engineering. Its recent development allows to preserve
both structure geometrical and analytical information. The
analytical model described in the paper is derived directly
from BIM model of a structure automatically but in most
cases it requires manual improvements before being sent
to FEM software. Dynamo visual programming language
was used to handle the analytical data. Authors developed
a program which corrects faulty analytical model obtained
from BIM geometry, thus providing better automation for
preparing FEM model. Program logic is explained and test
cases shown
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State-of-the-art on research and applications of machine learning in the building life cycle
Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science
Scan4Façade: Automated As-Is Façade Modeling of Historic High-Rise Buildings Using Drones and AI
This paper presents an automated as-is façade modeling method for existing and historic high-rise buildings, named Scan4Façade. To begin with, a camera drone with a spiral path is employed to capture building exterior images, and photogrammetry is used to conduct three-dimensional (3D) reconstruction and create mesh models for the scanned building façades. High-resolution façade orthoimages are then generated from mesh models and pixelwise segmented by an artificial intelligence (AI) model named U-net. A combined data augmentation strategy, including random flipping, rotation, resizing, perspective transformation, and color adjustment, is proposed for model training with a limited number of labels. As a result, the U-net achieves an average pixel accuracy of 0.9696 and a mean intersection over union of 0.9063 in testing. Then, the developed twoStagesClustering algorithm, with a two-round shape clustering and a two-round coordinates clustering, is used to precisely extract façade elements’ dimensions and coordinates from façade orthoimages and pixelwise label. In testing with the Michigan Central Station (office tower), a historic high-rise building, the developed algorithm achieves an accuracy of 99.77% in window extraction. In addition, the extracted façade geometric information and element types are transformed into AutoCAD command and script files to create CAD drawings without manual interaction. Experimental results also show that the proposed Scan4Façade method can provide clear and accurate information to assist BIM feature creation in Revit. Future research recommendations are also stated in this paper
Using the Knowledge Transfer Partnership model as a method of transferring BIM and Lean process related knowledge between academia and industry: A Case Study Approach
This paper looks at the vehicle of the Knowledge Transfer Partnership (KTP) between
academia and business and how successful it is in reaching its range of objectives and
developing theoretical and practical educational materials for BIM curriculums. The KTP
operates by helping businesses improve their competitiveness and productivity through the
better use of knowledge, technology and skills that reside within the UK knowledge base. At
the same time, it also helps to increase the business relevance of knowledge base research
and teaching for the academic institutions.
For this paper, the KTP project between the University of Salford and John McCall
Architects (JMA) in Liverpool is reviewed. This two year KTP focused on the implementation
of BIM and Lean principles to JMA’s architectural practice in social housing sector. The
KTP project is 70% Government funded and 30% funded by JMA and undertaken under the
Technology Strategy Board programme, enabling innovation in business. The initial aims
and objectives of the KTP are assessed and evaluated against the actual knowledge transfer
and implementation and the final outcomes of the KTP for the University, JMA and the KTP
associate are highlighted
DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems
Deep learning (DL) defines a new data-driven programming paradigm that
constructs the internal system logic of a crafted neuron network through a set
of training data. We have seen wide adoption of DL in many safety-critical
scenarios. However, a plethora of studies have shown that the state-of-the-art
DL systems suffer from various vulnerabilities which can lead to severe
consequences when applied to real-world applications. Currently, the testing
adequacy of a DL system is usually measured by the accuracy of test data.
Considering the limitation of accessible high quality test data, good accuracy
performance on test data can hardly provide confidence to the testing adequacy
and generality of DL systems. Unlike traditional software systems that have
clear and controllable logic and functionality, the lack of interpretability in
a DL system makes system analysis and defect detection difficult, which could
potentially hinder its real-world deployment. In this paper, we propose
DeepGauge, a set of multi-granularity testing criteria for DL systems, which
aims at rendering a multi-faceted portrayal of the testbed. The in-depth
evaluation of our proposed testing criteria is demonstrated on two well-known
datasets, five DL systems, and with four state-of-the-art adversarial attack
techniques against DL. The potential usefulness of DeepGauge sheds light on the
construction of more generic and robust DL systems.Comment: The 33rd IEEE/ACM International Conference on Automated Software
Engineering (ASE 2018
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