Matineh Eybpoosh

Identification of Risk Paths in International Construction Projects Using Structural Equation Modeling

The major aim of this research is to demonstrate that causal relationships exist among various risk factors that necessitate identification of risk paths, rather than individual risk factors, during risk assessment of construction projects. International construction projects have more complex risk-emergence patterns because they are affected by global and foreign country conditions and project-related factors. Identification of a network of interactive risk paths, each of which initiated from diverse vulnerabilities of the project system, is considered to be a better reflection of the real conditions of construction projects than the use of generic risk checklists. In this study, using the data from 166 projects carried out by Turkish contractors in international markets and utilizing structural equation modeling (SEM) techniques, 36 interrelated risk paths were identified and the total effects of each vulnerability factor and risk path on cost overrun were assessed. SEM findings demonstrate that every r...

Developing As-built Building Information Model Using Construction Process History Captured by a Laser Scanner and a Camera

Availability and accuracy of building information is critical for a variety of tasks during the lifecycle of facilities. Currently Building information modelling (BIM) is mostly used for design and construction. When the as-designed BIM is not updated with the construction changes, it can contain inaccurate information. A way to collect the as-is conditions is to capture how a facility is changing over time. Vision-based data capture technologies, namely laser scanners and cameras, are being widely used. However, occluded components and the challenges associated with reverse engineering of complex construction objects can result in incomplete as-built data. This paper presents a case study, in which a laser scanner and a camera were used to capture the construction history and develop a more complete as-built BIM. A progressive approach is followed to mitigate challenges associated with cluttered construction data. Components/features occluded in any captured scans were reconsidered throughout a continuous planning and data capturing process. Other sources such as as-designed documents are supplemented to as-built data for extraction of information items required for the as-built BIM. The discussions include more ample description of the background research and the addressed problem, followed by detailed description of this study’s approach. Lessons learned, findings and recommendations for future research are summarized.

Preparing Civil Engineers for International Collaboration in Construction Management

Economic globalization is increasingly affecting both the construction industry and academia. It is changing the traditional roles of civil engineers and construction managers. Cross-cultural collaboration and communication skills, multinational team management skills, the ability to overcome the social challenges of geographically distributed teams, and familiarity with construction materials, standards, and methods of foreign countries are vital for modern construction professionals. However, the traditional skills and education style of engineers and construction managers do not equip them to successfully deal with such issues. This paper describes the experiences of a university course International Collaborative Construction Management that was developed to educate the next generation of civil engineers to be more internationally savvy. Throughout the three years that the course has been conducted to date, students in Turkey, the United States, Israel, and Brazil were grouped in multinational teams. ...

Investigation on the Effects of Environmental and Operational Conditions (EOC) on Diffuse-Field Ultrasonic Guided-Waves in Pipes

In spite of many favorable characteristics of guided-waves for Nondestructive Evaluation (NDE) of pipes, real-world application of these systems is still quite limited. Beside the complexities derived from multi-modal, dispersive and multi-path characteristics of guided-waves, one of the main challenges in guided-wave based NDE of pipelines is sensitivity of these systems to variations of environmental and operational conditions (EOC). This paper investigates the effects of varying EOCs on guilded-wave based NDE of pipelines. We first provide a review of the studies to date in the field of guided-wave based testing to identify research gaps for enhancing the application of these systems in pipeline NDE. To study the identified gaps, guided-wave data from a fully operational piping system, with continuously varying flow rate and temperature, is used. Time-shift and amplitude drift effects due to flow rate variations are evaluated along with those of temperature. It is observed that masking effects of flow rate for damage detection can be at least as significant as temperature effects, and that such effects become more dominant when flow rate and temperature variations co-occur.

Nonlinear feature extraction methods for removing temperature effects in multi-mode guided-waves in pipes

Ultrasonic guided-waves propagating in pipes with varying environmental and operational conditions (EOCs) are usually the results of complex superposition of multiple modes travelling in multiple paths. Among all of the components forming a complex guided-wave signal, the arrivals scattered by damage (so called scatter signal) are of importance for damage diagnosis purposes. This paper evaluates the potentials of nonlinear decomposition methods for extracting the scatter signal from a multi-modal signal recorded from a pipe under varying temperatures. Current approaches for extracting scatter signal can be categorized as (A) baseline subtraction methods, and (B) linear decomposition methods. In this paper, we first illustrate, experimentally, the challenges for applying these methods on multi-modal signals at varying temperatures. To better analyze the experimental results, the effects of temperature on multi-modal signals are simulated. The simulation results show that different wave modes may have significantly different sensitivities to temperature variations. This brings about challenges such as shape distortion and nonlinear relations between the signals recorded at different temperatures, which prevent the aforementioned methods to be extensible to wide range of temperatures. In this paper, we examine the potential of a nonlinear decomposition method, namely nonlinear principal component analysis (NLPCA), for removing the nonlinear relation between the components of a multi-modal guided-wave signal, and thus, extracting the scatter signal. Ultrasonic pitch-catch measurements from an aluminum pipe segment in a thermally controlled laboratory are used to evaluate the detection performance of the damage-sensitive features extracted by the proposed approach. It is observed that NLPCA can successfully remove nonlinear relations between the signal bases, hence extract scatter signal, for temperature variations up to 10℃, with detection accuracies above 99%.

Effects of Planning and Data Collection Approaches on the Quality of Processed Laser Scanned Data: Lessons Learned

Several tasks in the Architectural, Engineering, Construction and Facility Management (AEC&FM) domain require real-time geospatial information. Traditional measurement methods are widely being replaced by more advanced technologies. 3D laser scanners are increasingly being used for diverse tasks during the lifecycle of a facility. However, a variety of process, environmental, scanner and analysis related factors can affect the quality of laser scanned data. Error sources, particularly those inherent in early stages of the laser scanning process, have not been well-addressed. This paper focuses on the effects of such error sources on the quality of the raw/processed data. Lessons learned through the planning, laser scanning and data processing of a facility are shared. The dataset consists of a total of 68 scans from both indoor and outdoor environments collected through using two different types of scanners. The effects of pre-scan site visits and proactive planning on reducing the consequences of different error sources are illustrated through examples. Lessons learned emphasize the need for formalization of the planning stage of laser scanning process, and data collection approaches for the AEC&FM domain applications, to enhance the quality of the raw/processed data.

Temperature variation effects on sparse representation of guided-waves for damage diagnosis in pipelines

Multiple ultrasonic guided-wave modes propagating along a pipe travel with different velocities which are themselves a function of frequency. Reflections from the features of the structure (e.g., boundaries, pipe welding, damage, etc.), and their complex superposition, adds to the complexity of guided-waves. Guided-wave based damage diagnosis of pipelines becomes even more challenging when environmental and operational conditions (EOCs) vary (e.g., temperature, flow rate, inner pressure, etc.). These complexities make guided-wave based damage diagnosis of operating pipelines a challenging task. This paper reviews the approaches to-date addressing these challenges, and highlights the preferred characteristics of a method that simplifies guided-wave signals for damage diagnosis purposes. A method is proposed to extract a sparse subset of guided-wave signals in time-domain, while retaining optimal damage information for detection purpose. In this paper, the general concept of this method is proved through an extensive set of experiments. Effects of temperature variation on detection performance of the proposed method, and on discriminatory power of the extracted damage-sensitive features are investigated. The potential of the proposed method for real-time damage detection is illustrated, for wide range of temperature variation scenarios (i.e., temperature difference between training and test data varying between -2°C and 13°C).

Effects of damage location and size on sparse representation of guided-waves for damage diagnosis of pipelines under varying temperature

In spite of their many advantages, real-world application of guided-waves for structural health monitoring (SHM) of pipelines is still quite limited. The challenges can be discussed under three headings: (1) Multiple modes, (2) Multipath reflections, and (3) Sensitivity to environmental and operational conditions (EOCs). These challenges are reviewed in the authors’ previous work. This paper is part of a study whose objective is to overcome these challenges for damage diagnosis of pipes, while addressing the limitations of the current approaches. That is, develop methods that simplify signal while retaining damage information, perform well as EOCs vary, and minimize the use of transducers. In this paper, a supervised method is proposed to extract a sparse subset of the ultrasonic guided-wave signals that contain optimal damage information for detection purposes. That is, a discriminant vector is calculated so that the projections of undamaged and damaged pipes on this vector is separated. In the training stage, data is recorded from intact pipe, and from a pipe with an artificial structural abnormality (to simulate any variation from intact condition). During the monitoring stage, test signals are projected on the discriminant vector, and these projections are used as damage-sensitive features for detection purposes. Being a supervised method, factors such as EOC variations, and difference in the characteristics of the structural abnormality in training and test data, may affect the detection performance. This paper reports the experiments investigating the extent to which the differences in damage size and damage location, as well as temperatures, can influence the discriminatory power of the extracted damage-sensitive features. The results suggest that, for practical ranges of monitoring and damage sizes of interest, the proposed method has low sensitivity to such training factors. High detection performances are obtained for temperature differences up to 14°C. The findings reported in this paper suggest that although the proposed method is a supervised approach, labeling of the training data does not require prior knowledge about the damage characteristics (e.g., size, location). Moreover, the potential of the proposed method for online monitoring is illustrated, for wide range of temperature variations and different damage scenarios.