Hyojoo Son

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.

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...

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...

Rapid 3D Object Modeling Using 3D Data from Flash LADAR for Automated Construction Equipment Operations

Automated recognition and modeling of 3D objects located in a construction work environment that are difficult to characterize or are constantly changing is critical for autonomous heavy equipment operation. Such automation allows for accurate, efficient, and autonomous operation of heavy equipment in a broad range of construction tasks by providing interactive background information. This paper presents 3D object recognition and modeling system from range data obtained from flash LADAR, with the goal of rapid and effective representation of the construction workspace. The proposed system consists of four steps: data acquisition, pre-processing, object segmentation on range images, and 3D model generation. During the object segmentation process, the split-and-merge algorithm, which separates a set of objects in a range image into individual objects, is applied to range images for the segmentation of objects. The whole process is automatic and is performed in nearly real time with an acceptable level of accuracy. The system was validated in outdoor experiments, and the results show that the proposed 3D object recognition and modeling system achieves a good balance between speed and accuracy, and hence could be used to enhance efficiency and productivity in the autonomous operation of heavy equipment.

Comparison of Single Classifier Models for Predicting Long-term Business Failure of Construction Companies Using Finance-based Definition of the Failure

This study compares the performance of six classifier models (ANN, KNN, C4.5, SVM, LR, and NB) for predicting the business failure of construction companies after three years from 2010 to 2012. Although previous studies have explored numerous business failure prediction models for construction companies, these models have focused on short-term failure, defined as failure occurring within one year, and have defined business failure based on companies’ experiencing serious legal events, including bankruptcy, delisting, and default. However, the construction industry is typically characterized by projects with longer durations, usually exceeding one year. This implies that previous short-term models cannot predict the business failure of construction companies until the end of particular projects. Moreover, this problem is compounded by the fact that legal events can involve lengthy proceedings, which are often initiated much later than the actual moment of the business failure. Therefore, in this study, six classifier models will be used to predict the business failure of the construction companies within three years using a finance-based definition of failure. The results show that all six models’ performances noticeably decrease when they predict more than one year. These results demonstrate that previous short-term prediction models with outstanding performance cannot be practical in predicting the long-term business failure of construction companies.

Detection of Nearby Obstacles with Monocular Vision for Earthmoving Operations

The equipment used in earthmoving operations poses a significant threat to the safety of the equipment operator and construction workers due to the operator’s inherently poor visibility of the surrounding environment. This study proposes a method of automated detection of nearby obstacles with monocular vision, with the goal of protecting the equipment operator and construction workers from potentially dangerous situations, such as collisions between earthmoving equipment and obstacles within a certain proximity. The proposed method consists of three steps: 1) correction of lens distortion prior to further processing, 2) shadow removal, and 3) detection of nearby obstacles with a predefined height level via perspective transformations. The proposed method was tested on video streams acquired from a camera installed on the side of the equipment body while an excavator executed excavating and moving tasks. The experimental results showed that the proposed method can provide the equipment operator with information about nearby obstacles during the excavator’s manipulation and transportation. It is expected that the proposed method can be implemented in rearview monitoring systems and surrounding view monitoring systems for operator assistance and to achieve active safety.