• Journal of the Korea institute for structural maintenance and inspection
• /
• v.18 no.3
• /
• pp.101-107
• /
• 2014

Damage location and extent of structure could be detected by the inverse analysis on dynamic response properties such as frequencies and mode shapes. The dynamic response of building structures has many noise and affected by nonstructural members and, above all, the behavior of building structure is more complex than civil structure and this makes the damage detection difficult. In recent researches the damage is detected by the indirect index such as sensitivity or assumed values. However, for the more reasonable damage detection, it needs to use the damage index directly induced from dynamic equation. The purpose of this study is to provide the damage detection method on shear building structures by the damage index directly induced from dynamic equation. The provided damage index could be estimated from measured mode shape of undamaged structure and frequency difference between undamaged and damaged structure. The damage detection method is applied to numerical analysis model such as MATLAB and MIDAS GENw for the verification. The damage index at damaged story represents (-) sign and 15 times than other undamaged sories.

• Proceedings of the KSRS Conference
• /
• 2002.10a
• /
• pp.530-535
• /
• 2002

An approach for quickly updating GIS building data using high resolution remotely sensed data is proposed in this paper. High resolution remotely sensed data could be aerial photographs, satellite images and airborne laser scanning data. Data from different types of sensors are integrated in building extraction. Based on the extracted buildings and the outdated GIS database, the change-detection-template can be automatically created. Then, GIS building data can be fast updated by semiautomatically processing the change-detection-temp late. It is demonstrated that this approach is quick, effective and applicable.

• Proceedings of the SAREK Conference
• /
• 2006.06a
• /
• pp.807-813
• /
• 2006

The objective of this study is to develop a rule-based fault detection algorithm and an experimental verification using an artificial air handling unit. To develop an analytical algorithm which precisely detects a tendency of faulty component, energy equations at each control volume of AHU were applied. An experimental verification was conducted on the HVAC simulator. The rule based FDD algorithm isolated a faulted sensor from HVAC components in summer and winter conditions.

• Proceedings of the Korean Institute of Building Construction Conference
• /
• 2023.05a
• /
• pp.251-252
• /
• 2023

As technology improves with the 4th industrial revolution, smart construction is becoming a key part of safety management in the architecture and civil engineering. By using object detection technology with CCTV data, construction sites can be managed efficiently. In this study, super resolution technology based on deep learning is proposed to improve the accuracy of object detection in construction sites. As the resolution of a train set data and test set data get higher, the accuracy of object detection model gets better. Therefore, according to the scale of construction sites, different object detection models can be considered.

• Proceedings of the KSRS Conference
• /
• 2005.10a
• /
• pp.241-244
• /
• 2005

Many building detection methods mainly rely on line segments extracted from aerial or satellite imagery. Building detection methods based on line segments, however, are difficult to succeed in high resolution satellite imagery such as IKONOS imagery, for most buildings in IKONOS imagery have small size of roofs with low contrast between roof and background. In this paper, we propose an efficient method to extract line segments and group them at the same time. First, edge preserving filtering is applied to the imagery to remove the noise. Second, we segment the imagery by watershed method, which collects the pixels with similar intensities to obtain homogeneous region. The boundaries of homogeneous region are not completely coincident with roof boundaries due to low contrast in the vicinity of the roof boundaries. Finally, to resolve this problem, we set up snake model with segmented region boundaries as initial snake's positions. We used a greedy algorithm to fit a snake to roof boundary. Experimental results show our method can obtain more .correct roof boundary with small size and low contrast from IKONOS imagery. Snake algorithm, building roof detection, watershed segmentation, edge-preserving filtering

• Journal of the Korea Institute of Building Construction
• /
• v.12 no.3
• /
• pp.299-314
• /
• 2012

One of reasons for introducing Building Information Modeling (BIM) is to support clash detection tasks by means of a 3D product model. In the conventional construction project process, clashes have been found during construction phase. However, it can cause cost overrun and time delay. In order to investigate and correct clash detections at design phase, relevant business process and guide for this task should be provided. This study aims to identify hindrances in clash detection tasks at the design phase and analyze its current process using IDEF0 model. Despite the convenience of IDEF0 as a systems analysis tool, professional participants might have difficulties to understand their own tasks according to business process. For this reason, in this research, Business Process Model and Notation (BPMN) is introduced to provide ideal process and required decision making governance. The provide BPMN model will provide insights for a BIM-based collaborative environment to enhance the constructability through the construction project.

• International conference on construction engineering and project management
• /
• 2013.01a
• /
• pp.558-565
• /
• 2013

The process and efficiency of monitoring building and construction violations is a concern of the construction industry. The detection of violations requires appropriate and sufficiently accurate spatial information to manage and support a comprehensive inspection process and monitor compliance. A building inspection workflow must extract appropriate spatial and measurement in-formation from a variety of sources, identify potential violations across a range of compliance criteria and determine the quality of resulting inspection reports. This paper presents a framework for supporting building inspections using spatial information and methods to detect construction violations and compliance. Current inspection processes involve issues around the identification of building violations, access to building regulations and existing spatial information, integration of a range of spatial and non-spatial information, and the quality of decisions within the inspection workflows. A survey of building inspectors was conducted and used together with the issues identified to establish the requirements for a spatial inspection framework. The results demonstrate how such a framework can support improved decision-making and reduced fieldwork effort in detecting and measuring the accuracy of building violations involving building placements, street offsets and footprint areas.

• Journal of IKEEE
• /
• v.12 no.1
• /
• pp.51-58
• /
• 2008

This paper presents a new method for building detection and reconstruction from aerial images. In our approach, we extract the useful building location information from the generated disparity map to obtain the segmentation of interested objects and thus reduce significantly unnecessary line segment extracted in low level feature extraction step. Hypothesis selection is carried out by using undirected graph in which close cycles represent complete rooftops hypotheses, and hypothesis are finally tested to contruct building model. We test the proposed method with synthetic images generated from Avenches dataset of Ascona aerial images. The experiment result shows that the extracted 3D line segments of the buildings can be efficiently used for the task of building detection and reconstruction from aerial images.

• Structural Engineering and Mechanics
• /
• v.63 no.6
• /
• pp.789-797
• /
• 2017

Recently, some integrated structural identification/damage detection and reliability evaluation of structures with uncertainties have been proposed. However, these techniques are applicable for off-line synthesis of structural identification and reliability evaluation. In this paper, based on the recursive formulation of the extended Kalman filter, an on-line integration of structural identification/damage detection and reliability evaluation of stochastic building structures is investigated. Structural limit state is expanded by the Taylor series in terms of uncertain variables to obtain the probability density function (PDF). Both structural component reliability with only one limit state function and system reliability with multi-limit state functions are studied. Then, it is extended to adopt the recent extended Kalman filter with unknown input (EKF-UI) proposed by the authors for on-line integration of structural identification/damage detection and structural reliability evaluation of stochastic building structures subject to unknown excitations. Numerical examples are used to demonstrate the proposed method. The evaluated results of structural component reliability and structural system reliability are compared with those by the Monte Carlo simulation to validate the performances of the proposed method.

• Journal of KIBIM
• /
• v.7 no.1
• /
• pp.18-27
• /
• 2017

Although Industry Foundation Classes (IFC) provide standards for exchanging Building Information Modeling (BIM) data, authoring tools still require manual mapping between BIM entities and IFC classes. This leads to errors and omissions, which results in corrupted data exchanges that are unreliable and thus compromise the validity of IFC. This research explored precedent work by Krijnen and Tamke, who suggested ways to automate the mapping of IFC classes using a machine learning technique, namely anomaly detection. The technique incorporates geometric features of individual components to find outliers among entities in identical IFC classes. This research primarily focused on applying this approach on two architectural BIM models and determining its feasibility as well as limitations. Results indicated that the approach, while effective, misclassified outliers when an IFC class had several dissimilar entities. Another issue was the lack of entities for some specific IFC classes that prohibited the anomaly detection from comparing differences. Future research to improve these issues include the addition of geometric features, using novelty detection and the inclusion of a probabilistic graph model, to improve classification accuracy.