Mohammad Nahangi

State of research in automatic as-built modelling

Building Information Models (BIMs) are becoming the official standard in the construction industry for encoding, reusing, and exchanging information about structural assets. Automatically generating such representations for existing assets stirs up the interest of various industrial, academic, and governmental parties, as it is expected to have a high economic impact. The purpose of this paper is to provide a general overview of the as-built modelling process, with focus on the geometric modelling side. Relevant works from the Computer Vision, Geometry Processing, and Civil Engineering communities are presented and compared in terms of their potential to lead to automatic as-built modelling.

Automated 3D compliance checking in pipe spool fabrication

In the industrial sector, inspection processes are critical because of the complexity of the components involved.An automated approach for early detection of deviations in pipe spools is developed based on scan-to-BIM registration.A set of experiments is performed to validate the proposed approach for pipe spools.Incurred deviations and defects are identified and characterized in a timely manner. In pipe spool assemblies used in construction, pre-fabrication errors inevitably occur due to the complexity of the tasks involved in the pipe spool fabrication process, the inaccuracy of the tools employed for performing these tasks, human error, and inadequate inspection and monitoring during the process. Permanent deflections may also occur during shipment and transportation. After delivery at construction sites, defective spools must be detected and further consideration given to the erection of the spools to tolerance levels specified; otherwise, the repair and realignment associated with rework can cause schedule delays and consequent substantial costs increases. This paper presents an automated approach for monitoring and assessing fabricated pipe spools using automated scan-to-BIM registration. Defects are detected through a neighborhood-based Iterative Closest Point (ICP) approach for the registration process. While this technique can be broadly employed, this paper focuses on industrial construction facilities with particular emphasis on pipe spool assemblies. Experiments show that the proposed approach can be employed for the automatic and continual monitoring of such assemblies throughout fabrication, assembly and erection to enable timely detection and characterization of deviations. The main contribution of the work presented in this paper is an automated 3D inspection framework and algorithms for construction assemblies in general and pipe spools in particular.

An Integrated Quality Management System for Piping Fabrication Using 3D Laser Scanning and Photogrammetry

Addressing deficiencies and defects that occur during construction projects is costly and time consuming. The manual quality assurance programs currently used in the industry have certain limitations, including human error and lack of consistency. Hence, there is a need for integrated electronic models that employ new technologies and methods. This research introduces an integrated construction quality control system that has the potential to improve quality management processes in the construction field for piping construction, which is a complex process often requiring rework. The proposed integrated system relies on data collected from construction sites using photogrammetry and laser scanning, which is then used to compare actual work performed to that designed. The proposed system aims to improve the promptness and accuracy of quality assurance processes, in particular dimension measurements, by avoiding human error and integrating defect detection and quality management. The use of this system has highlighted some of the limitations during data fusion and acquisition process, which are highlighted in this paper. These aspects must be considered to increase the reliability of the acquired information.

Automated Model-Based Finding of 3D Objects in Cluttered Construction Point Cloud Models

Finding construction components in cluttered point clouds is a critical pre-processing task that requires intensive and manual operations. Accurate isolation of an object from point clouds is a key for further processing steps such as positive identification, scan-to-building information modeling (BIM), and robotic manipulation. Manual isolaton is tedious, time consuming, and disconnected from the automated tasks involved in the process. This article adapts and examines a method for finding objects within 3D point clouds robustly, quickly, and automatically. A local feature on a pair of points is employed for representing 3D shapes. The method has three steps: (1) offline model library generation, (2) online searching and matching, and (3) match refinement and isolation. Experimental tests are carried out for finding industrial (curvilinear) and structural (rectilinear) elements. The method is verified under various circumstances in order to measure its performance toward addressing the major challenges involved in 3D object finding. Results show that the method is sufficiently quick and robust to be integrated with automated process control frameworks.

Automated Registration of 3D Point Clouds with 3D CAD Models for Remote Assessment of Staged Fabrication

Modularization and pre-assembly are parts of a trend toward staged fabrication that has been developing in the construction industry in many parts of the world over the past few decades. Successful delivery and transportation of materials in staged fabrication processes has always been a key challenge. While substantial advances in modularization and pre-fabrication have been achieved recently, there is still a significant rate of damages and defects occurring during transportation and shipment. In addition, there are inaccuracies in staged-fabricated assemblies due to manually intensive quality control during the fabrication process. Thus there is a significant need to continuously monitor the fabrication processes to avoid significant rework costs and delays. This paper presents an automated approach to register laser scanned data, which represents as-built status, with 3D CAD models for prefabricated steel assemblies. Moreover, automated registration enhances three-dimensional tolerance analysis for automated quality control of prefabricated assemblies. An Iterative Closest Point (ICP)-based model is used for automated registration in the presented paper. An experimental study is conducted to validate the proposed model for monitoring the fabrication and installation processes. Experimental results show that the presented approach can be used to detect defected parts or fabrication inaccuracies precisely and quickly.

Dimensional Variability Analysis of Construction Assemblies Using Kinematics Chains and Building Information Models

Design optimization frameworks used in manufacturing can be adopted into construction to solve complex and relatively unsolved challenges, as recent construction methods progressively incorporate more manufacturing aspects. For example, the specification and dimensional control for compliance checking of construction components can be solved using tools existing in manufacturing. Even though building information models (BIM) assist with clash detection for identifying potential dimensional problems, dimensional variability remains a complex challenge to address in construction. This paper explores the use of a dimensional variation analysis (DVA), which is originally developed in the manufacturing industry as a design optimization tool. This paper presents a DVA approach which is based on kinematics theory in robotics to define the assembly equation (how various parts of a component are related to each other mathematically). A design-model based DVA is validated using a case study. Results show that the method is capable of determining variability between the designed and fabricated states with a reasonable

An image-based frameworks for automated discrepancy quantification and realignment of industrial assemblies

Image-based frameworks for automated as-built modeling and infrastructure 3D reconstruction are increasingly being used in the construction industry. The increasing use of image-based technologies in the construction processes is due to the ease of application, cheap cost of enhancement, time effectiveness and high level of accuracy and automation. Automating the tasks involved in the inspection, quality control and quality assurance (QA/QC) processes are the potential avenues for utilizing such frameworks. This paper presents an image-based approach for acquiring the built status of fabricated assemblies and describes a framework for realigning the defective segments by borrowing concepts from robotics and forward kinematics. A laboratory set of experiments is then conducted to measure the accuracy and performance of the proposed framework for realignment of industrial facilities, in general, and pipe spools, in particular. Results demonstrate that it can be utilized in real cases providing the required level of accuracy as well.

Automated dimensional compliance assessment with incomplete point cloud

Dimensional compliance assessment of prefabricated assemblies is a critical part of mitigating rework on heavy industrial construction projects. As assemblies become more complex, manual direct contact metrology becomes ineffective at detecting fabrication error and so automated alternatives that offer objective, fast, and continuous data collection must be explored. Nahangi and Haas (2014) developed an automated method for assessing pipe spools through an algorithm that compares as-built laser scans to 3D design files. The tool is capable of automatic and continual monitoring of prefabricated assemblies throughout their lifecycle and enables timely detection and quantification of dimensional noncompliance. In the original publication, the tool was validated using ideal input data. In this paper, the tool is tested for robustness when processing incomplete point cloud input data. Non-ideal input data is a risk associated with unfavorable conditions in the fabrication environment such as random assembly occlusions causing blind spots in sensing setup, budgetary constraints limiting the purchase of sensing equipment/viewpoints, and random hardware or software failures resulting in corrupt data. The tool was found to reliably detect dimensional non-compliance so long as the non-compliance indicator (pipe spool feature distinguishing the non-compliant state from the design state) was not fully occluded. Accuracy of non-compliance quantification was predominantly high, however, loosely proportional to the input point cloud’s coverage of the assembly’s surface area.

Robotic Kinematics Analogy for Realignment of Defective Construction Assemblies

Aligning and plumbing of construction assemblies is a fundamental problem because of reliance on manual solutions for geometric feedback control problem involved with practices such as pipefitting and steel structures erection. Where defective components and segments are not well controlled, the errors propagate in larger components and therefore cause more severe problems. In order to address such a challenging problem and tackle potential solutions, this paper presents a framework for automatic and systematic development of realignment actions required to achieve a target state by borrowing concepts from: (1) 3D imaging that enables the identification of the as-built status and then quantification of incurred discrepancies as a feedback by comparing the captured status with the designed state existing in the building information model (BIM); and (2) an inverse kinematics analogy that results in the calculation of required changes in the degrees of freedom defined where realignment and changes can be applied. Experimental results show that the framework can generate the required actions for achieving a desired state systematically and

Comparison of Methods Used for Detecting Unknown Structural Elements in Three-dimensional Point Clouds

Three-dimensional (3D) imaging technologies, in particular 3D laser scanners, are becoming more accessible and more accurate. These advances are providing engineers and architects with vast quantities of raw, geometric data. Whereas this data is visually appealing and intuitive to the human eye, it contains very little meaning beyond that. The research presented in this paper presents and compares methods for attributing meaning to dense 3D point clouds. Two of the methods developed and presented utilize two-dimensional (2D) and 3D Hough transforms to represent the points as lines and planes. The third method uses point segmentation techniques to group points belonging to the same plane. The initial focus is on structural steel systems and connections modeling for analysis of reuse. The advantages and disadvantages of each method are outlined, and each method is evaluated for its potential to provide engineers and architects with useful and meaningful point clouds from 3D laser scanners. The point segmentation techniques exhibit the most potential by allowing for the location and orientation of any surface to be identified.