Changwan Kim

As-built data acquisition and its use in production monitoring and automated layout of civil infrastructure

Access to reliable 3D as-built data is a critical issue in civil infrastructure.Applications to production monitoring and automated layout are discussed.Research on applications of as-built data in the civil engineering field is reviewed.State-of-the-art and other developments in as-built data analysis are surveyed.Unsolved problems and challenges for future improvements in this field are discussed. The collection and analysis of data on the three-dimensional (3D) as-built status of large-scale civil infrastructure - whether under construction, newly put into service, or in operation - has been receiving increasing attention on the part of researchers and practitioners in the civil engineering field. Such collection and analysis of data is essential for the active monitoring of production during the construction phase of a project and for the automatic 3D layout of built assets during their service lives. This review outlines recent research efforts in this field and technological developments that aim to facilitate the analysis of 3D data acquired from as-built civil infrastructure and applications of such data, not only to the construction process per se but also to facility management - in particular, to production monitoring and automated layout. This review also considers prospects for improvement and addresses challenges that can be expected in future research and development. It is hoped that the suggestions and recommendations made in this review will serve as a basis for future work and as motivation for ongoing research and development.

Fully Automated As-Built 3D Pipeline Extraction Method from Laser-Scanned Data Based on Curvature Computation

There has been a growing demand for the three-dimensional (3D) reconstruction of as-built pipelines. The as-built 3D pipeline reconstruction process consists of the measurement of an industrial plant, identification of pipelines, and generation of 3D models of the pipelines. Although measurement is now efficiently performed using laser-scanning technology, and in spite of significant progress in 3D pipeline model generation, the identification of pipelines from large and complex sets of laser-scanned data continues to pose a challenge. The aim of this study is to propose a method to automatically extract 3D points corresponding to as-built pipelines that occupy large areas of industrial plants from laser-scanned data. The proposed extraction method consists of the following steps: preprocessing, segmentation of the 3D point cloud, feature extraction based on curvature computation, and pipeline classification. An experiment was performed at an operating industrial plant to validate the proposed method. The experimental result revealed that the proposed method can indeed contribute to the automation of as-built 3D pipeline reconstruction.

Ubiquitous Sensor Network for Construction Material Monitoring

Timely acquisition of construction resource information is an essential task for construction engineers and managers. Due to the harsh and dynamic construction environment, it is not easy to acquire construction information in real time. This paper presents a radio frequency identification (RFID) and ZigBee (IEEE 802.15.4)-based system to manage materials on a busy construction site where a data communications system is not in place. RFID tags are attached to and used to identify various kinds of construction materials, and the ZigBee communication technology is used to wirelessly transfer this information. To confirm the viability of our system, the RFID and ZigBee technologies were assessed using an indoor experiment. Following this, a field experiment was then conducted. On a building construction site, a range of construction materials was identified using RFID tags and this information was transferred to an end user with the help of ZigBee multihop networking. The results of the field experiment showed an acceptable reading range and rate for the proposed system. Therefore, the integrated system with RFID and ZigBee modules demonstrated great potential for improving the existing management processes for construction resources on large and complex construction sites.

Comparison of Preproject Planning for Green and Conventional Buildings

AbstractThe importance of green buildings has been frequently highlighted. However, barriers such as greater complexity, lack of understanding of sustainability, and the perception of a greater possibility of cost overrun have hindered the dissemination of green buildings. More planning efforts for green buildings can presumably help mitigate these barriers. This paper investigates preproject planning efforts for green and conventional building projects. Project-level data were collected (124 in total, 71 from conventional building projects and 53 from green building projects), with project data consisting of general information about the project, a Project Definition Rate Index (PDRI) survey, and cost performance. The project data were categorized into four groups based on their project type (green and conventional) and cost performance (actual cost on/under budget and over budget). For the four groups, a two-way analysis of variance test was used to compare the degree of preproject planning efforts meas...

Trends of Fall Accidents in the U.S. Construction Industry

AbstractFall accidents constitute a crucial type of accident in the construction industry. This study investigates fall accidents that occurred in the United States between 1997 and 2012. Using the...

Multiimaging Sensor Data Fusion-Based Enhancement for 3D Workspace Representation for Remote Machine Operation

AbstractIn incompletely characterized environments such as construction sites, remote machine operation is the preferred—and sometimes the only—safe and efficient solution for the operation of construction machines. When it comes to the operation of remote-controlled construction machines, a human—machine interface is needed so that even in the case of an unstructured environment (such as a construction site), the operator can interact with the machine in a safe and efficient manner. The human—machine interface needs to have the capability of realistically representing a three-dimensional (3D) workspace that provides information feedback to the remote operator. Workspace representation methods that are currently in use have certain limitations—they are time consuming and labor intensive and require high-performance computers. A major objective of this study is the development of an efficient means of representing a workspace in 3D that has the capacity to provide interactive visual feedback to the operato...

Semantic as-built 3D modeling of structural elements of buildings based on local concavity and convexity

Abstract The aim of this study is to propose a method for generating as-built BIMs from laser-scan data obtained during the construction phase, particularly during ongoing structural works. The proposed method consists of three steps: region-of-interest detection to distinguish the 3D points that are part of the structural elements to be modeled, scene segmentation to partition the 3D points into meaningful parts comprising different types of elements (e.g., floors, columns, walls, girders, beams, and slabs) using local concave and convex properties between structural elements, and volumetric representation. The proposed method was tested in field experiments by acquiring and processing laser-scan data from construction sites. The performance of the proposed method was evaluated by quantitatively measuring how accurately each of the structural elements was recognized as its functional semantics. Overall, 139 elements of the 141 structural elements (99%) in the two construction sites combined were recognized and modeled according to their actual functional semantics. As the experimental results imply, the proposed method can be used for as-built BIMs without any prior information from as-planned models.

Automatic 3D Reconstruction of As-Built Pipeline Based on Curvature Computations from Laser-Scanned Data

Demand has been growing for three-dimensional (3D) reconstruction of asbuilt pipelines that occupy large areas within operating plants. In practice, measurements are efficiently performed using laser-scanning technology; however reconstructing an as-built pipeline from this laser-scanned data remains challenging. The data acquired from the plant facility can be incomplete due to complex occlusion, or it can be affected by noise due to the reflective surfaces of the pipelines and other parts. The aim of this study is to propose a method for generating models of entire pipelines that include straight pipes, elbows, reducers, and tee pipes from laserscanned data. The proposed 3D reconstruction method for as-built pipelines is divided into three main tasks: (1) identifying the types and locations of the pipelines from the laser-scanned data; (2) segmenting the pipelines into each type of pipe form; and (3) reconstructing the pipelines’ geometry and topology and generating models of them. Field experiments were performed at an operating industrial plant in order to validate the proposed method. The results revealed that the proposed method can indeed contribute to the automation of 3D reconstruction of as-built pipelines.

Prediction of government-owned building energy consumption based on an RReliefF and support vector machine model

AbstractAccurate prediction of the energy consumption of government-owned buildings in the design phase is vital for government agencies, as it enables formulation of the early phases of development of such buildings with a view to reducing their environmental impact. The aim of this study was to identify the variables that are associated with energy consumption in government-owned buildings and to propose a predictive model based on those variables. The proposed approach selects relevant variables using the RReliefF variable selection algorithm. The support vector machine (SVM) method is used to develop a model of energy consumption based on the identified variables. The proposed approach was analyzed and validated on data for 175 government-owned buildings derived from the 2003 Commercial Building Energy Consumption Survey (CBECS) database. The experimental results revealed that the proposed model is able to predict the energy consumption of government-owned buildings in the design phase with a reasonab...

Scan-to-BIM - An Overview of the Current State of the Art and a Look Ahead

The use of Building Information Modeling (BIM) technology to support building design and construction practices is becoming the standard in the U.S. and worldwide, and various benefits of implementing BIM, such as reduced RFIs and change orders, have already been proven. However, efficient integration of BIM with facility management (FM) systems, which requires having accurate as-built BIM, has not yet been achieved. Integrated BIM-FM systems offer significant benefits to owners such as better visualization of the spaces to be maintained in a virtual environment. BIMs, specifically MEP BIMs, are reviewed on a regular basis during project coordination meetings between stakeholders, and are updated regularly during the construction phase. However, some coordination issues related to MEP components maybe realized within the BIM, but fixed in the field; and those changes are not typically reflected in the BIM. Three dimensional (3D) point clouds provide accurate and comprehensive as-built information, and are used for creating as-built BIMs. The process of creating as-built BIM from 3D point cloud data is referred to as Scan-to-BIM. Currently available offthe-shelf Scan-to-BIM software packages require significant manual user input making the entire process cumbersome and error prone. Significant amount of research has been done both in academia and in industry to automate Scan-to-BIM process. The goal of this paper is to provide a comprehensive review on current Scan-to-BIM software and their capabilities to retrieve MEP components from 3D point clouds, while identifying challenges and the look ahead for future research.