Parametric modeling has been proposed as an effective means to embed domain expertise in models of buildings. As information technology becomes more powerful in terms of the ability to manipulate large parametric models, the potential grows to build increasingly sophisticated functional systems for designing, modeling and fabricating buildings. Implementing more powerful systems implies greater functional specificity, which requires elicitation and capture of increasingly detailed and complex domain-specific semantics and knowledge. This paper explores the extent to which design and engineering knowledge can be practically embedded in production software for building information modeling (BIM). It focuses on a building object behavior (BOB) description notation and method, developed as a shorthand protocol for designing, validating and sharing the design intent of parametric objects. Examples are drawn from an advanced BIM system development project for precast concrete.
Piping in industrial projects is a critical and costly process as thousands of unique pipe spools go through design, fabrication, interim processing, delivery, storage, installation and inspection. Current methods for tracking pipe spools through this long supply chain are subject to many problems. This paper evaluates the use of RFID technology as a possible solution to some of these problems through automation of the current tracking process. The technical feasibility of RFID applications is analyzed based on field tests. A model of current tracking process is presented to identify potential economic benefits from using RFID technology in automated tracking.
Tool availability is a critical factor in the productivity of construction crews. In an effort to improve the efficiency of tracking tools and improve their availability, this research effort developed a tool tracking and inventory system which is also capable of storing operation and maintenance (O&M) data using commercially available active radio frequency identification (RFID) tags. With participation of two electrical construction firms, the system was tested on a number of construction jobsites. The project demonstrated that active RFID can be used to inventory small tools and store pertinent O&M data on the tools in construction environments despite metal interference and low temperatures. Economics, lack of standardization, and lack of direction and range data from the tags were identified as the most significant constraints limiting active RFID commercialization for tool tracking.
Defects experienced during construction are costly and preventable. However, inspection programs employed today cannot adequately detect and manage defects that occur on construction sites, as they are based on measurements at specific locations and times, and are not integrated into complete electronic models. Emerging sensing technologies and project modeling capabilities motivate the development of a formalism that can be used for active quality control on construction sites. In this paper, we outline a process of acquiring and updating detailed design information, identifying inspection goals, inspection planning, as-built data acquisition and analysis, and defect detection and management. We discuss the validation of this formalism based on four case studies.
Numerous attempts to use ultrasonic pulse velocity (UPV) as a measure of compressive strength of concrete has been made due to obvious advantages of non-destructive testing methods. The present study is conducted for prediction of compressive strength of concrete based on weight and UPV for two different concrete mixtures (namely M20 and M30) involving specimens of two different sizes and shapes as a result of need for rapid test method for predicting long-term compressive strength of concrete. The prediction is done using multiple regression analysis and artificial neural networks. A comparison between two methods depicts that artificial neural networks can be used to predict the compressive strength of concrete effectively. The results are plotted as experimentally evaluated compressive strength versus predicted strength through both methods of analysis.
The detection of cracks in concrete infrastructure is a problem of great interest. In particular, the detection of cracks in buried pipes is a crucial step in assessing the degree of pipe deterioration for municipal and utility operators. The key challenge is that whereas joints and laterals have a predictable appearance, the randomness and irregularity of cracks make them difficult to model. Our previous work has led to a segmented pipe image (with holes, joints, and laterals eliminated) obtained by a morphological approach. This paper presents the development of a statistical filter for the detection of cracks in the pipes. We propose a two-step approach. The first step is local and is used to extract crack features from the buried pipe images; we present two such detectors as well as a method for fusing them. The second step is global and defines the cracks among the segment candidates by processes of cleaning and linking. The influences of the parameters on crack detection are studied and results are presented for various pipe images.
Safe egress is one of the key design issues identified by facility planners, manager and inspectors. Computational tools are now available for the simulation and design of emergency evacuation and egress. However, these tools rely heavily on assumptions about individual human and social behaviors, which have been found to be oversimplified, inconsistent and even incorrect. Furthermore, the behaviors are usually incorporated into the computational model in an ad hoc manner. This paper presents a framework for studying human and social behavior, from the perspectives of human decision-making and social interaction, and for incorporating such behavior systematically in a dynamic computational model suitable for emergency egress analysis.
This paper brings together the visions for information and communications technologies (ICT) in construction and the changing requirements of the construction industry. Drawing on case studies, previous research and future scenarios outlined by research road mapping projects, it illustrates how the areas identified as having potential for improvement can be addressed through the use of mobile IT. This vision is then developed into a scenario for communicating to industry professionals the construction site of the future. Their feedback on this scenario is used to assess the viability of the research propositions within mainstream construction. Finally, the paper examines the implications for the construction industry should the vision for the future be adopted; the potential for new islands of automation; the effects on our human resources; and the potential impact on knowledge management initiatives.
Most high-rise building construction projects rely on tower cranes to perform lifting and hoisting activities. In practice, tower cranes are managed based on demand, urgency, and prioritized work tasks that must be performed within a set period of time in the field. As a computer tool, simulation has proved to be effective in modeling complex construction operations and can be a substantial help in aiding practitioners in construction planning. However, the use of simulation has fallen far below its maximum potential due to a lack of appropriate support tools which would allow construction managers to use simulation tools for themselves. Special purpose simulation (SPS) and 3D visualization of simulated operations are two potential means that enable domain experts, who are knowledgeable in give domains, but not familiar with simulation, to easily model an operation within their domain and analyze the simulation results. This paper presents a practical methodology for integrating 3D visualization with SPS for tower crane operation. An integrated system was built in a 3D Studio MAX environment and tested in the construction of the new civil and environmental engineering building at the University of Alberta. This paper demonstrates that 3D visualization is helpful in the verification and validation of simulation results, and can effectively communicate the essence of a simulated operation, thus improving the accessibility of simulation as a decision making aid.
This paper summarizes ongoing research aimed at developing knowledge, methods and tools required to implement automated robotic crane erection processes for the construction industry. In the proposed approach, construction cranes are treated as multi-degree-of-freedom robots and modeled in a virtual environment. Virtual cranes are provided with motion-planning algorithms that enable them to find collision-free and time-efficient paths for each piece that needs to be erected. Inverse kinematics are then used to determine the crane motions required to move elements in previously computed paths. By using an effective method to coordinate the tasks and motions of multiple cranes, the system is also extended to construction projects that require simultaneous use of closely-spaced cranes. The virtual crane model provides realistic visualizations of erection processes and detailed erection schedules.
Knowledge management involves creating, securing, coordinating, combining, retrieving and distributing knowledge. Knowledge can be reused and shared among engineers and experts to enhance construction processes and decrease the time and cost of solving problems. This study presents a novel and practical method to capture and represent construction project knowledge by using network knowledge maps. Network Knowledge Maps (NKM) gives users an overview of available and missing knowledge in core project areas, enabling tacit and explicit knowledge to be managed appropriately. This study addresses application of knowledge management in the construction phase of construction projects, and presents a construction Map-based Knowledge Management (MBKM) concept and system for contractors. The MBKM system is then utilized in selected case studies involving a High-Tech factory building enterprise in Taiwan to verify the proposed methodology and indicate the effectiveness of sharing knowledge, particularly in the construction phase. Knowledge can be captured and managed to benefit future projects by effectively utilizing information and web technologies during the construction phase of a project. The results of this study demonstrate that an MBKM-like system can be applied effectively in knowledge management systems in the construction industry by using map-based knowledge management and web technology.
In recent years, a number of advanced Information and Communication Technology (ICT) solutions have been developed to assist in the management of business processes and working environments. Mobile computing and wireless communication are two such technologies which have been adapted for use in hybrid systems that can monitor and manage industrial health, safety and welfare activities. Within the construction sector, plant and equipment operation has been shown to be one of the leading causes of accidents and injuries, not least because the sector has witnessed an exponential reliance upon mechanical resources. In an attempt to reduce vehicle accidents, this work reports upon a conceptual model that utilises advanced ICT solutions to produce an innovative and proactive health and safety management system entitled . Using a combination of Global Positioning Systems (GPS), smart sensors and wireless networks, SightSafety can track operatives and plant, notify management and employees of pending danger and ultimately contribute to reducing accident rates. The system also provides reports on dangerous occurrences that have happened, thereby enabling managers to learn from any experiences acquired, or mistakes made, during the construction of a project.
Today, building information modelling (BIM) plays a crucial role in the research and development fields of construction information integration and interoperability. This paper, from an information technology point of view, outlines the definition and aims of the “3D to nD Modelling” project, a platform grant-funded project by UK’s British Engineering and Physics Sciences Research Council (EPSRC). It presents a scenario of widening BIM implementation into the overall aspects involved in the whole life cycle of a building project. Industry foundation classes (IFC) as a standard BIM specification has been adopted as a central information repository in order to deliver the integrated building information throughout the nD-driven assessments, evaluation and decision-making. This paper also focuses on the development of an IFC-viewer, which is defined as the holistic interface of the nD modelling tool. The techniques and methods including the auxiliary tools adopted in this development are detailed. This development presents a practical and economic way to reveal and retrieve the information of IFC models inclusively, structurally and visually.
One of the computerized technologies for advanced infrastructure inspection methods is the application of digital image processing. Digital image processing methods have been developed for steel bridge coating inspections for the past few years. The rust percentages on steel bridge coating surfaces can be reliably computed through the use of digital image processing methods. However, previous researchers solely focused on the determination of the degree of rust defects on the steel surfaces in percentage. Therefore, an automated processor that can recognize the existence of bridge coating rust defects needs to be developed. This paper presents the development of a rust defect recognition method to determine whether rust defects exist in a given digital image by processing digital color information. For the development of the image processor, color image processing is employed, instead of grayscale image processing commonly used in previous researches, since rust defects are distinctive in color against background.
The enormity of the problem of deteriorating pipeline infrastructure is widely apparent. Since a complete rebuilding of the piping system is not financially realistic, municipal and utility operators require the ability to monitor the condition of buried pipes. Thus, reliable pipeline assessment and management tools are necessary to develop long term cost effective maintenance, repair, and rehabilitation programs. In this paper a simple, robust and efficient image segmentation algorithm for the automated analysis of scanned underground pipe images is presented. The algorithm consists of image pre-processing followed by a sequence of morphological operations to accurately segment pipe cracks, holes, joints, laterals, and collapsed surfaces, a crucial step in the classification of defects in underground pipes. The proposed approach can be completely automated and has been tested on five hundred scanned images of buried concrete sewer pipes from major cities in North America.
This paper introduces the system's perspective of the dynamic planning and control methodology (DPM), aimed to support both the strategic and the operational aspects of project management. For this objective, a new modeling framework that integrates system dynamics and network-based tools is presented in this paper. The proposed framework adopts system dynamics as a core simulation engine for strategic project management and network-based tools as a wrap layer for operational project management. To implement the DPM framework, a web-based system has been developed within a collaborative environment. The developed system provides great support to both the strategic and operational aspects of project management by integrating familiar network concepts with system dynamics to analyze the overall strategic and operational project performance.
Pipeline infrastructure is decaying at an accelerating rate due to reduced funding and insufficient quality control resulting in poor installation, little or no inspection and maintenance, and a general lack of uniformity and improvement in design, construction and operation practices. The current practice that is being followed to inspect the conditions of pipes is usually time consuming, tedious and expensive. It may also lead to diagnostic errors due to lack of concentration of human operators. Buried pipe defect classification is thus a practical and important pattern classification problem. These defects appear in the form of randomly shaped cracks and holes, broken joints and laterals, and others. This paper proposes a new neuro-fuzzy classifier that combines neural networks and concepts of fuzzy logic for the classification of defects by extracting features in segmented buried pipe images. A comparative evaluation of the -NN, fuzzy -NN, conventional backpropagation network, and proposed neuro-fuzzy projection network classifiers is carried out. Among the five neural methods implemented and tested, the proposed neuro-fuzzy classifier performs the best, with classification accuracies around 90% on real concrete pipe images.
While human factors have been well researched in virtual environments, it has not received commensurate consideration in Mixed Reality (MR) research. This paper (1) analyzes the feasibility of augmenting human abilities via MR applications in construction tasks from the perspective of cognitive engineering, (2) acknowledges the ergonomics features and research issues in MR systems, and (3) generates partial guidelines to solve ergonomics issues. Also, perceptual incompatibility was validated through an experiment comparing a head mounted display versus a desktop monitor in performing an orientation task. The perceptual incompatibility by using the monitor was significant regarding performance time, accuracy and workload.
Along with the development of the supply chain for the construction industry, as one of the key operations within the supply chain, an efficient RMC (Ready Mixed Concrete) delivering process becomes important to both of the RMC batch plant and the construction sites. However, it is almost infeasible for the RMC batch plant manger to quickly develop an efficient and balanced schedule of dispatching RMC trucks when various construction sites call for delivering in the same short period of time. Several studies have shown that an efficient dispatching schedule could improve the productivity for both of the batch plant and the construction sites. However, the results from those studies are not efficient enough for practical use. Therefore, it is necessary to build a more efficient model that optimizes the schedule of dispatching RMC trucks. This paper first analyzes RMC delivering process and then develops a systematic model based on several supply chain management (SCM) concepts. In addition, the fast messy genetic algorithms (fmGA) and the CYCLONE simulation technique are incorporated to find the optimal dispatching schedule which minimizes the total waiting duration of RMC trucks at construction sites without breaking off the operations of casting concrete. A user-friendly computer program is also built to help the batch plant manager streamline the dispatching process. Results show that this new systematic approach along with the implemented computer program can quickly generate efficient and balanced schedules of dispatching RMC trucks.