Engin Burak Anil

Sensing and Field Data Capture for Construction and Facility Operations

Collection of accurate, complete, and reliable field data is not only essential for active management of construction projects involving various tasks, such as material tracking, progress monitoring, and quality assurance, but also for facility and infrastructure management during the service lives of facilities and infrastructure systems. Limitations of current manual data collection approaches in terms of speed, completeness, and accuracy render these approaches ineffective for decision support in highly dynamic environments, such as construction and facility operations. Hence, a need exists to leverage the advancements in automated field data capture technologies to support decisions during construction and facility operations. These technologies can be used not only for acquiring data about the various operations being carried out at construction and facility sites but also for gathering information about the context surrounding these operations and monitoring the workflow of activities during these o...

Efficient and Effective Quality Assessment of As-Is Building Information Models and 3D Laser-Scanned Data

Documenting as-is conditions of buildings using 3D laser scanning and Building Information Modeling (BIM) technology is being adopted as a practice for enhancing effective management of facilities. Many service providers generate as-is BIMs based on laser-scanned data. It is necessary to conduct timely and comprehensive assessments of the quality of the laser-scanned data and the as-is BIM generated from the data before using them for making decisions about facilities. This paper presents the data and as-is BIM QA requirements of civil engineers and demonstrates that the required QA information can be derived by analyzing the patterns in the deviations between the data and the as-is BIMs. We formalized this idea as a deviation analysis method for efficient and effective QA of the data and as-is BIMs. A preliminary evaluation of the results obtained through this approach show the potential of this method for achieving timely, detailed, comprehensive, and quantitative assessment of various types of data/model quality issues.

Representation Requirements of As-Is Building Information Models Generated from Laser Scanned Point Cloud Data

Obtaining and utilizing as-is information with 3D imaging technologies, such as laser scanners, during various phases of facility life-cycle is becoming common practice. Both during construction and facility operations information derived from laser scanner data can serve many purposes by providing accurate information about the conditions of the facilities at the time of the scanning. Currently, the information derived from point clouds is typically represented as building information models (BIMs). Despite the benefits of having accurate as-is BIMs, current BIM approaches and tools have limitations in representing as-is information. The reason is partly that current methods of as-is BIM generation are based on existing as-designed BIM generation and representation processes. We identified five main concepts that are unique to as-is BIMs and are not represented with existing BIMs. These characteristics are point density, noise, occlusions, model deviations, and the links between the points and building components.This paper investigates these unique characteristics of as-is conditions and discusses how representing them within a BIM can provide advantages to downstream users, such as enabling decisions based on more complete data than would otherwise be possible.

Building-Information-Modeling–Based Earthquake Damage Assessment for Reinforced Concrete Walls

AbstractEngineering analysis to quantify the effects of earthquake forces on the structural strength of components requires determining the damage mode and severity of the components. The analysis requires strength computations and visual damage assessments, which are information intensive, potentially error-prone, and slow. This study develops a building-information-modeling (BIM) based approach to support the engineering analysis of reinforced concrete structures. In the proposed approach, the damage information is represented along with the geometric, topological, and structural information. Transformation and reasoning mechanisms are proposed to utilize the information contained in the BIM to perform strength analysis and visual assessment tasks. The approach is validated on a case study building, which contains 42 damaged piers and spandrels.

Characterization of Laser Scanners for Detecting Cracks for Post-Earthquake Damage Inspection

Objective, accurate, and fast assessment of damage to buildings after an earthquake is crucial for timely remediation of material losses and safety of occupants of buildings. Laser scanners are promising sensors for collecting geometrical data regarding the damaged states of buildings, as they are able to provide high coverage and accuracy at long ranges. Yet, we have limited knowledge on the performance of laser scanners for detecting earthquake damage, and requirements of such data collection. This paper focuses on characterizing the performance of laser scanners for detecting thin cracks for damage assessment of reinforced concrete frames. We identified a series of crack parameters based on the state-of-the-art damage assessment codes and standards. Similarly, we identified parameters, which affect the performance of laser scanners for detecting cracks, based on prior research in this area. We studied the width, depth, and orientation of cracks; sampling interval of the scanner, and the range of the laser beam from the surface. Effects of these parameters on the detection of the minimum crack size were determined in an experimental setting. An automated algorithm was used to analyze the data. The results show that it is possible to detect as small as ~1mm cracks.

Information Requirements for Design and Detailing of Reinforced Concrete Frames in Multiuser Environments

AbstractThe design and detailing of reinforced concrete frames is a complex process that requires intensive real-time information exchange between various design tasks. The monolithic behavior of concrete, differences in the geometric representation of structural members during the analysis and design stages, and design code requirements throughout the process add new dimensions to the problem. Additionally, especially in large projects, reinforced concrete structures are designed and detailed by several engineers simultaneously. These complexities can be resolved and the overall quality of the design can be improved by identifying information requirements for managing the design information between the design and detailing tasks and thus between structural engineers. In this paper, requirements for managing information for multiuser design and detailing of reinforced concrete frames are identified and a prototype information model has been implemented based on existing literature. The means of performing...

Information requirements for earthquake damage assessment of structural walls

The engineering analysis for determining the remaining seismic capacity of buildings following earthquakes requires performing structural calculations, observations of the actual damage, and applying extensive engineering judgment. Additionally, the analysis should often be performed under stringent time requirements. This study identifies the information requirements for representing the damage information and performing the visual damage assessment of structural walls. The damage descriptions for seven common damage modes of structural walls were studied by employing the affinity diagramming method. The study showed that the information required to represent the damaged conditions can be grouped under five broad categories and using seventeen damage parameters. A sensitivity analysis showed that the damage parameters have varying degrees of importance. The results of the study can be used to develop formal representation of damage information in information models and potentially allow better allocation of data collection time in the field.