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Year of publication
- 2004 (123) (remove)
The increased implementation of site data capture technologies invariably results in an increase in data warehousing and database technologies to store captured data. However, restricted use of data beyond the initial application could potentially result in a loss of understanding of site processes. This could in turn lead to poor decision making at production, tactical and strategic levels. Concrete usage data have been collected from two piling processes. These data have been analysed and the results highlighted potential improvements that could be made to existing site management and estimating processes. A cost benefit analysis has been used to support decision making at the strategic level where the identified improvements require capital expenditure.
The worldwide growth of communication networks and associated technologies provide the basic infrastructure for new ways of executing the engineering process. Collaboration amongst team members seperated in time and location is of particular importance. Two broad themes can be recognized in research pertaining to distributed collaboration. One theme focusses on the technical and technological aspects of distributed work, while the other emphasises human aspects thereof. The case of finite element structural analysis in a distributed collaboratory is examined in this paper. An approach is taken which has its roots in human aspects of the structural analysis task. Based on experience of how structural engineers currently approach and execute this task while utilising standard software designed for use on local workstations only, criteria are stated for a software architechture that could support collaborative structural analysis. Aspects of a pilot application and the results of qualitative performance measurements are discussed.
The purpose of this research is to develop the method to retrieve a building name from the impression of the building. First, the images of the building are registered as database by the questionnaire. Next, the images of the objective building are compared with the degree of matching in image databases, and the building with high synthetic matching degree is retrieved. This system could get a good retrieval result. Moreover, image processing was done, and image databases are trained by neural network from the amount of characteristics of the image, and the retrieval system by image processing was examined.
An important feature of the 2003 SARS outbreak in Canada, Singapore, and Hong Kong was that many health care workers (HCWs) developed SARS after caring for patients with SARS. This has been ascribed to inadequate or ineffective patient isolation. However, it is difficult for dense cities to provide sufficient isolation facilities within a short period of time. This has raised concerns from the public for new strategies in the planning and design of isolation facilities. Considering that SARS or other infectious diseases could seriously damage our society’s development, isolation facilities that could be rapidly and economically constructed with appropriate environmental controls are essential. For this reason, the design team of the Department of Architecture collaborated with a special task force from the Faculty of Medicine, who are the frontline medical officers treating the SARS patients, to design Rapidly Assembled Isolation Patient Wards. Both architecture and medicine are well established disciplines, but they have little in common in terms of the mode of knowledge construction and practice. This induced much intellectual exploration and research interest in conducting this study. The process has provided an important reference for cross disciplinary studies between the architectural and medical domains.
Complex gridshell structures used in architecturally ambitious constructions remain as appealing as ever in the public realm. This paper describes the theory and approach behind the software realisation of a tool which helps in finding the affine self-weight geometry of gridshell structures. The software tool DOMEdesign supports the formal design process of lattice and grid shell structures based upon the laws of physics. The computer-aided simulation of suspension models is used to derive structurally favourable forms for domes and arches subject to compression load, based upon the input of simple architectonic parameters. Irregular plans, three-dimensional topography, a choice different kinds of shell lattice structures and the desired height of the dome are examples of design parameters which can be used to modify the architectural design. The provision of data export formats for structural dimensioning and visualisation software enables engineers and planners to use the data in future planning and to communicate the design to the client.
The paper describes a concept for the step-by-step computer-aided capture and representation of geometric building data in the context of planning-oriented building surveying. Selected aspects of the concept have been implemented and tested as prototypes. The process of step-by-step capture and representation is determined by the order in which the user experiences the building. Only the information that the user knows (can see) or can reasonably deduce is represented. In addition approaches to the flexible combination of different measuring techniques and geometric abstractions are described which are based upon geodetic computational adjustment.
The truss model for predicting shear resistance of reinforced concrete beams has usually been criticized because of its underestimation of the concrete shear strength especially for beams with low shear reinforcement. Two challengers are commonly encountered in any truss model and are responsible for its inaccurate shear strength prediction. First: the cracking angle is usually assumed empirically and second the shear contribution of the arching action is usually neglected. This research introduces a nouvelle approach, by using Artificial Neural Network (ANN) for accurately evaluating the shear cracking angle of reinforced and prestressed concrete beams. The model inputs include the beam geometry, concrete strength, the shear reinforcement ratio and the prestressing stress if any. ...
Indentation experiments have been carried out over the past century to determine hardness of materials. Modern indentation machines have the capability to continuously monitor load and displacement to high precision and accuracy. In recent years, research interests have focussed on methods to extract material properties from indentation load-displacement curves. Analytical methods to interpret the indentation load-displacement curves are difficult to formulate due to material and geometric nonlinearities as well as complex contact interactions. In the present study, an artificial neural network model was constructed for interpretation of indentation load-displacement curves. Large strain-large deformation finite element analyses were first carried out to simulate indentation experiments. The data from finite element analyses were then used to train the artificial neural network model. The artificial neural network model was able to accurately determine the material properties when presented with load-displacement curves which were not used in the training process. The proposed artificial neural network model is robust and directly relates the characteristics of the indentation loaddisplacement curve to the elasto-plastic material properties.
This paper describes monitoring of the in-valley discharge and underground water level at the place where the tunnel will be constructed and also, the numerical analysis for prediction applying the Tank Model and Linear Filter Method to calculate the prediction. The application of these analyses has actually allowed the change of underground water level to be grasped and more effective information system to be established by comparing the real-time monitoring data with the real-time calculation of prediction.
Collaborative Design Processes: A Class on Concurrent Collaboration in Multidisciplinary Design
(2004)
The rise of concurrent engineering in construction demands early team formation and constant communication throughout the project life cycle, but educational models in architecture, engineering and construction have been slow to adjust to this shift in project organization. Most students in these fields spend the majority of their college years working on individual projects that do not build teamwork or communication skills. Collaborative Design Processes (CDP) is a capstone design course where students from the University of Illinois at Urbana-Champaign and the University of Florida learn methods of collaborative design enhanced by the use of information technology. Students work in multidisciplinary teams to collaborate from remote locations via the Internet on the design of a facility. An innovation of this course compared to previous efforts is that students also develop process designs for the integration of technology into the work of multidisciplinary design teams. The course thus combines both active and reflective learning about collaborative design and methods. The course is designed to provide students the experience, tools, and methods needed to improve design processes and better integrate the use of technology into AEC industry work practices. This paper describes the goals, outcomes and significance of this new, interdisciplinary course for distributed AEC education. Differences from existing efforts and lessons learned to promote collaborative practices are discussed. Principal conclusions are that the course presents effective pedagogy to promote collaborative design methods, but faces challenges in both technology and in traditional intra-disciplinary training of students.