• Industrial Engineering and Management Systems
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• v.7 no.1
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• pp.9-22
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• 2008

As an extension of a previous work by the authors (Song and Esogbue, 2006), a new algorithm for automated modeling of nonstationary seasonal time series is presented in this paper. Issues relative to the methodology for building automatically seasonal time series models and periodic time series models are addressed. This is achieved by inspecting the trend, estimating the seasonality, determining the orders of the model, and estimating the parameters. As in our previous work, the major instruments used in the model identification process are correlograms of the modeling errors while the least square method is used for parameter estimation. We provide numerical illustrations of the performance of the new algorithms with respect to building both seasonal time series and periodic time series models. Additionally, we consider forecasting and exercise the models on some sample time series problems found in the literature as well as real life problems drawn from the retail industry. In each instance, the models are built automatically avoiding the necessity of any human intervention.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.279-286
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• 2013

This paper introduces a new method for identification of building energy performance problems. The presented method is based on automated analysis and visualization of deviations between actual and expected energy performance of the building using EPAR (Energy Performance Augmented Reality) models. For generating EPAR models, during building inspections, energy auditors collect a large number of digital and thermal imagery using a consumer-level single thermal camera that has a built-in digital lens. Based on a pipeline of image-based 3D reconstruction algorithms built on GPU and multi-core CPU architecture, 3D geometrical and thermal point cloud models of the building under inspection are automatically generated and integrated. Then, the resulting actual 3D spatio-thermal model and the expected energy performance model simulated using computational fluid dynamics (CFD) analysis are superimposed within an augmented reality environment. Based on the resulting EPAR models which jointly visualize the actual and expected energy performance of the building under inspection, two new algorithms are introduced for quick and reliable identification of potential performance problems: 1) 3D thermal mesh modeling using k-d trees and nearest neighbor searching to automate calculation of temperature deviations; and 2) automated visualization of performance deviations using a metaphor based on traffic light colors. The proposed EPAR v2.0 modeling method is validated on several interior locations of a residential building and an instructional facility. Our empirical observations show that the automated energy performance analysis using EPAR models enables performance deviations to be rapidly and accurately identified. The visualization of performance deviations in 3D enables auditors to easily identify potential building performance problems. Rather than manually analyzing thermal imagery, auditors can focus on other important tasks such as evaluating possible remedial alternatives.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.17-22
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• 2011

A considerable number of construction disasters occur on pathways. Safety management is usually performed on construction sites to prevent accidents in activity areas. This means that the safety management level of hazards on pathways is relatively minimized. Many researchers have noted that hazard identification is fundamental to safety management. Thus, algorithms for helping safety managers to identify hazardous areas are developed using automated data collection technology. These algorithms primarily search for potential hazardous areas by comparing workers' location logs based on a real-time location system and optimal routes based on BIM. Potential hazardous areas are filtered by identified hazardous areas and activity areas. After that, safety managers are provided with information about potential hazardous areas and can establish proper safety countermeasures. This can help to improve safety on construction sites.

• Computers and Concrete
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• v.28 no.6
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• pp.605-619
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• 2021

Building Information Modeling (BIM) models are a powerful tool for preserving and using architectural history. Manually creating information models for such a significant number and variety of architectural monuments as Dong drum towers is challenging. The building logic based on "actual measurement construction" was investigated using the metamodel idea, and a metamodel-based automated modeling approach for the wood framework of Dong drum towers was presented utilizing programmable algorithms. Metamodels of fundamental frame kinds were also constructed. Case studies were used to verify the automated modeling's correctness, completeness, and efficiency using metamodel. The results suggest that, compared to manual modeling, automated modeling using metamodel may enhance the model's integrity and correctness by 5-10% while also reducing time efficiency by 10-20%. Metamodel and construction logic offer a novel way to investigate data-driven autonomous information-based modeling.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.11a
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• pp.155-156
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• 2022

In the process of developing an industrial robot, various simulations should be conducted to evaluate the driving, movement, and performance of the robot. Space and time constraints exist to manufacture existing robots and implement various simulations, and efficiency is reduced due to high costs. To solve this problem, many simulations can be conducted by implementing the same movement and working environment as the real environment in virtual reality using digital twin technology. This study proposes a process for establishing a digital twin model of automated layout robots. Using the digital twin model, it is expected that it will not only evaluate the hardware performance of the robot in the future, but also verify the robot's algorithms such as motion planning and work process, identify and solve potential problems in advance, and prevent problems caused by software.

• Smart Structures and Systems
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• v.13 no.6
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• pp.901-925
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• 2014

The reliability of a Bridge management System depends on the quality of visual inspection and the reliable estimation of bridge condition rating. However, the current practices of visual inspection have been identified with several limitations, such as: they are time-consuming, provide incomplete information, and their reliance on inspectors' experience. To overcome such limitations, this paper presents an approach of automating the prediction of condition rating for bridges based on digital image analysis. The proposed methodology encompasses image acquisition, development of 3D visualization model, image processing, and condition rating model. Under this method, scaling defect in concrete bridge components is considered as a candidate defect and the guidelines in the Ontario Structure Inspection Manual (OSIM) have been adopted for developing and testing the proposed method. The automated algorithms for scaling depth prediction and mapping of condition ratings are based on training of back propagation neural networks. The result of developed models showed better prediction capability of condition rating over the existing methods such as, Naïve Bayes Classifiers and Bagged Decision Tree.

• Korean Journal of Construction Engineering and Management
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• v.11 no.6
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• pp.14-23
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• 2010

A considerable number of construction disasters occurs on pathway. A safety management in construction sites is usually performed to prevent accidents in activity areas. This means that safety management level of hazards on pathway is relatively minified. Many researchers have introduced that a hazard identification is fundamental of safety management. Thus, algorithms for helping safety managers' hazardous area identification is developed using automated data collection technology. These algorithms primarily search potential hazardous area by comparing workers' location logs based on real-time locating system and optimal routes based on BIM. And potential hazardous areas is filtered by identified hazardous areas and activity areas. After that, safety managers are provided with information about potential hazardous areas and can establish proper safety countermeasures. This can help improving safety in construction sites.

• Smart Structures and Systems
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• v.30 no.5
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• pp.501-511
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• 2022

Three-dimensional (3D) models have become crucial for improving civil infrastructure analysis, and they can be used for various purposes such as damage detection, risk estimation, resolving potential safety issues, alarm detection, and structural health monitoring. 3D point cloud data is used not only to make visual models but also to analyze the states of structures and to monitor them using semantic data. This study proposes automating the generation of high-quality 3D point cloud data and removing noise using deep learning algorithms. In this study, large-format aerial images of civilian infrastructure, such as cut slopes and dams, which were captured by drones, were used to develop a workflow for automatically generating a 3D point cloud model. Through image cropping, downscaling/upscaling, semantic segmentation, generation of segmentation masks, and implementation of region extraction algorithms, the generation of the point cloud was automated. Compared with the method wherein the point cloud model is generated from raw images, our method could effectively improve the quality of the model, remove noise, and reduce the processing time. The results showed that the size of the 3D point cloud model created using the proposed method was significantly reduced; the number of points was reduced by 20-50%, and distant points were recognized as noise. This method can be applied to the automatic generation of high-quality 3D point cloud models of civil infrastructures using aerial imagery.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.21-26
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• 2006

This paper focuses on modeling and predicting differential GPS corrections transmitted by marine radio-beacon systems using artificial neural networks. Various neural network structures with various training algorithms were examined, including Linear, Radial Biases, and Feedforward. Matlab Neural Network toolbox is used for this purpose. Data sets used in building the model are the transmitted pseudorange corrections and broadcast navigation message. Model design is passed through several stages, namely data collection, preprocessing, model building, and finally model validation. It is found that feedforward neural network with automated regularization is the most suitable for our data. In training the neural network, different approaches are used to take advantage of the pseudorange corrections history while taking into account the required time for prediction and storage limitations. Three data structures are considered in training the neural network, namely all round, compound, and average. Of the various data structures examined, it is found that the average data structure is the most suitable. It is shown that the developed model is capable of predicting the differential correction with an accuracy level comparable to that of beacon-transmitted real-time DGPS correction.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.63-74
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• 2020

The assembly of modular construction requires a series of thoroughly-considered decisions for crane lifting including the crane model selection, crane location planning, and lift path planning. Traditionally, this decision-making process is empirical and time-consuming, requiring significant human inputs. Recently, research efforts have been dedicated to improving lift planning practices by leveraging cutting-edge technologies such as automated data acquisition, Building Information Modelling (BIM) and computational algorithms. It has been demonstrated that these technologies have advanced lift planning to some degree. However, the advancements tend to be fragmented and isolated. There are two hurdles prevented a systematic improvement of lift planning practices. First, the lack of formalized lift planning workflow, outlining the procedure and necessary information. Secondly, there is also an absence of a shared information environment, enabling storages, updates and the distribution of information to stakeholders in a timely manner. Thus, this paper aims to overcome the hurdles. The study starts with a literature review in combination with document analysis, enabling the initial workflow and information flow. These were contextualised through a series of interviews with Australian practitioners in the crane-related industry, and systematically analysed and schematically validated through an expert panel. Findings included formalized workflow and corresponding information exchanges in a traditional lift planning practice via a Business Process Model and Notation (BPMN). The traditional practice is thus reviewed to identify opportunities for further enhancements. Finally, a BIM-based lift planning workflow is proposed, which integrates the scattered technologies (e.g. BIM and computational algorithms) with the aim of supporting lift planning automation. The resulting framework is setting out procedures that need to be developed and the potential obstacles towards automated lift planning are identified.