• International conference on construction engineering and project management
• /
• 2022.06a
• /
• pp.792-799
• /
• 2022

Building Information Modeling (BIM) technology is a key component of modern construction engineering and project management workflows. As-is BIM models that represent the spatial reality of a project site can offer crucial information to stakeholders for construction progress monitoring, error checking, and building maintenance purposes. Geometric methods for automatically converting raw scan data into BIM models (Scan-to-BIM) often fail to make use of higher-level semantic information in the data. Whereas, semantic segmentation methods only output labels at the point level without creating object level models that is necessary for BIM. To address these issues, this research proposes a hybrid semantic-geometric approach for clutter-resistant floorplan generation from laser-scanned building point clouds. The input point clouds are first pre-processed by normalizing the coordinate system and removing outliers. Then, a semantic segmentation network based on PointNet++ is used to label each point as ceiling, floor, wall, door, stair, and clutter. The clutter points are removed whereas the wall, door, and stair points are used for 2D floorplan generation. A region-growing segmentation algorithm paired with geometric reasoning rules is applied to group the points together into individual building elements. Finally, a 2-fold Random Sample Consensus (RANSAC) algorithm is applied to parameterize the building elements into 2D lines which are used to create the output floorplan. The proposed method is evaluated using the metrics of precision, recall, Intersection-over-Union (IOU), Betti error, and warping error.

• Journal of the Korea Institute of Building Construction
• /
• v.15 no.3
• /
• pp.317-327
• /
• 2015

Many construction projects have extensively adopted building information modeling (BIM), and various institutions and standards have been developed domestically in Korea. However, the current process that is used to calculate building space area has a significant shortcoming in that there are two different laws to apply the method of measurement considering space boundaries for building element guidelines. For example, space area can be calculated by a polygon, which is modeling using a BIM-based computer aided design program, such that the space polygon is always exported as an inner-edge type. In this paper, we developed an automated algorithm to convert coordinates of space representation using industry foundation classes based BIM data. The proposed algorithm will enable engineers responsible for space management to use a BIM-based model directly in the space programming process without having to do additional work. The proposed process can help ensure that space area is more accurately and reliably.

• International conference on construction engineering and project management
• /
• 2020.12a
• /
• pp.361-370
• /
• 2020

Automatic building material recognition has been a popular research interest over the past decade because it is useful for construction management and facility management. Currently, the extensively used methods for automatic material recognition are mainly based on 2D images. A terrestrial laser scanner (TLS) with a built-in camera can generate a set of coloured laser scan data that contains not only the visual features of building materials but also other attributes such as material reflectance and surface roughness. With more characteristics provided, laser scan data have the potential to improve the accuracy of building material recognition. Therefore, this research aims to develop a TLS-based building material recognition method by combining machine learning techniques. The developed method uses material reflectance, HSV colour values, and surface roughness as the features for material recognition. A database containing the laser scan data of common building materials was created and used for model training and validation with machine learning techniques. Different machine learning algorithms were compared, and the best algorithm showed an average recognition accuracy of 96.5%, which demonstrated the feasibility of the developed method.

• International conference on construction engineering and project management
• /
• 2020.12a
• /
• pp.443-452
• /
• 2020

Worker's awkward postures and unreasonable physical load can be corrected by monitoring construction activities, thereby increasing the safety and productivity of construction workers and projects. However, manual identification is time-consuming and contains high human variance. In this regard, an automated activity recognition system based on inertial measurement unit can help in rapidly and precisely collecting motion data. With the acceleration data, the machine learning algorithm will be used to train classifiers for automatically categorizing activities. However, input acceleration data are extracted either from designed experiments or simple construction work in previous studies. Thus, collected data series are discontinuous and activity categories are insufficient for real construction circumstances. This study aims to collect acceleration data during long-term continuous work in a construction project and validate the feasibility of activity recognition algorithm with the continuous motion data. The data collection covers two different workers performing formwork at the same site. An accelerator, as well as portable camera, is attached to the worker during the entire working session for simultaneously recording motion data and working activity. The supervised machine learning-based models are trained to classify activity in hierarchical levels, which reaches a 96.9% testing accuracy of recognizing rest and work and 85.6% testing accuracy of identifying stationary, traveling, and rebar installation actions.

• Smart Structures and Systems
• /
• v.29 no.2
• /
• pp.287-299
• /
• 2022

Rather than using smartphones as seismometers with designated locations and orientations, this study proposes to employ crowds' smartphones in buildings to perform fast safety assessment of buildings. The principal advantage of using crowds' smartphones is the potential to monitor the safety of millions of buildings without hardware costs, installation labor, and long-term maintenance. This study's goal is to measure the maximum interstory drift ratios during earthquake excitation using crowds' smartphones. Beacons inside the building are required to provide the location and relevant building information for the smartphones via Bluetooth. Wi-Fi Direct is employed between nearby smartphones to conduct peer-to-peer time synchronization and exchange the acceleration data measured. An algorithm to align the orientation between nearby smartphones is proposed, and the performance of the orientation alignment, interstory drift measurement, and damage level estimation are studied numerically. Finally, the proposed approach's performance is verified using large-scale shaking table tests of a scaled steel building. The results presented in this study illustrate the potential to use crowds' smartphones with the proposed approach to record building motions during earthquakes and use those data to estimate buildings' safety based on the interstory drift ratios measured.

• Proceedings of the Korean Institute of Building Construction Conference
• /
• 2015.11a
• /
• pp.64-65
• /
• 2015

As construction projects have increased in size and height recently, lifting accounts for increasingly greater portion and tower cranes are used more frequently. At present, the selection and arrangement of tower crane are depend on the experience of experts. However, since the number of experts is fairly limited and a database for tower cranes regarding lifting capacity, operation properties, rent, etc has not been widely employed, tower cranes are often not effectively selected and arranged which can cause cost overruns and delays in the lifting work. To address such issues, this study attempts to perform a basic study for development of automatic arrangement algorithm of tower crane using drawing recognition. If relevant database is established and the algorithm suggested in this study is refined more systematically, even beginning level engineers will be able to plan tower crane arrangement in a way comparable to experienced experts.

• Journal of KIBIM
• /
• v.10 no.4
• /
• pp.32-39
• /
• 2020

In recent years, the construction industry is getting bigger and more complex, so it is becoming difficult to acquire point cloud data for construction equipments and workers. Point cloud data is measured using a drone and MMS(Mobile Mapping System), and the collected point cloud data is used to create a 3D digital map. In particular, the construction site is located at outdoors and there are many irregular terrains, making it difficult to collect point cloud data. For these reasons, adopting a noise reduction algorithm suitable for the characteristics of the construction industry can affect the improvement of the analysis accuracy of digital maps. This is related to various environments and variables of the construction site. Therefore, this study reviewed and analyzed the existing research and techniques on the noise reduction algorithm. And based on the results of literature review, performance evaluation of major noise reduction algorithms was conducted for digital maps of construction sites. As a result of the performance evaluation in this study, the voxel grid algorithm showed relatively less execution time than the statistical outlier removal algorithm. In addition, analysis results in slope, space, and earth walls of the construction site digital map showed that the voxel grid algorithm was relatively superior to the statistical outlier removal algorithm and that the noise removal performance of voxel grid algorithm was superior and the object preservation ability was also superior. In the future, based on the results reviewed through the performance evaluation of the noise reduction algorithm of this study, we will develop a noise reduction algorithm for 3D point cloud data that reflects the characteristics of the construction site.

• Proceedings of the Korean Institute of Building Construction Conference
• /
• 2019.11a
• /
• pp.60-61
• /
• 2019

Concrete carbonation is one of the factors that reduce the durability of concrete. In modern times, due to industrialization, the carbon dioxide concentration in the atmosphere is increasing, and the impact of carbonation is increasing. So, it is important to understand the carbonation resistance according to the concrete compounding to secure the concrete durability life. In this study, we want to predict the concrete carbonation velocity coefficient, which is an indicator of the carbonation resistance of concrete, through the deep learning algorithm, and to find the activation function suitable for the prediction of carbonation rate coefficient as a process to determine the learning accuracy through the deep learning algorithm. In the scope of this study, using the ReLU function showed better accuracy than using other activation functions.

• Proceedings of the Korean Institute of Building Construction Conference
• /
• 2023.05a
• /
• pp.89-90
• /
• 2023

This dissertation investigates the feasibility of accurately calculating the required rebar length using BIM-based shape codes and the potential benefits of such an approach in terms of cost reduction, waste reduction, and environmental improvement. The study aims to explore the possibility of automatically calculating slab rebar length before construction to reduce rebar cutting waste and cost. The results of this study will provide insights into the potential of using shape codes to reduce rebar cutting waste and cost in building frame construction.

• Proceedings of the Korean Institute of Building Construction Conference
• /
• 2023.11a
• /
• pp.237-238
• /
• 2023

Urbanization and the increase in building scale have amplified the complexity of M.E.P design. Traditional design methods face limitations when considering intricate pathways and variables, leading to an emergent need for research in automated design. Initial algorithmic approaches encountered challenges in addressing complex architectural structures and the diversity of M.E.P types. However, with the launch of OpenAI's ChatGPT-3.5 beta version in 2022, new opportunities in the automated design sector were unlocked. ChatGPT, based on the Large Language Model (LLM), has the capability to deeply comprehend the logical structures and meanings within training data. This study analyzed the potential application and latent value of LLMs in M.E.P design. Ultimately, the implementation of LLM in M.E.P design will make genuine automated design feasible, which is anticipated to drive advancements across designs in the construction sector.