Web-based information management system for construction projects

This paper presents a web-based project information management (WebPIM) system for civil engineering applications, with particular emphasis on construction project management. In the proposed system, all project information is centralized in a project database residing in the project server, instead of being distributed to many different locations. By utilizing the latest web technology, the system works as an information platform for all design and construction participants of a construction project throughout the life cycle of the project. A prototype model is designed in this paper to illustrate the application of the proposed system and the hardware and software requirements for the intended application. Discussion is given on the security and speed of data transfer, as well as an effectiveness comparison among various project management methodologies

Finite element model updating for structures with parametric constraints

This paper presents a finite element (FE) model updating procedure applied to complex structures using an eigenvalue sensitivity-based updating approach. The objective of the model updating is to reduce the difference between the calculated and the measured frequencies. The method is based on the first-order Taylor-series expansion of the eigenvalues with respect to some structural parameters selected to be adjusted. These parameters are assumed to be bounded by some prescribed regions which are determined according to the degrees of uncertainty that exist in the parameters. The changes of these parameters are found iteratively by solving a constrained optimization problem. The improvement of the current study is in the use of an objective function that is the sum of a weighted frequency error norm and a weighted perturbation norm of the parameters. Two weighting matrices are introduced to provide flexibility fbr individual tuning of frequency errors and parameters' perturbations. The proposed method is applied to a 1/150 scaled suspension bridge model. Using 11 measured frequencies as reference, the FE model is updated by adjusting ten selected structural parameters. The final updated FE model for the suspension bridge model is able to produce natural frequencies in close agreement with the measured ones. Copyright (C) 2000 John Wiley & Sons, Ltd

PARALLEL CHOLESKY METHOD ON MIMD WITH SHARED-MEMORY

An efficient parallel algorithm based on the Cholesky decomposition is proposed for stiffness matrix stored in a skyline form on a MIMD machine with shared memory. Included in this study is an analysis of several factors affecting the efficiency of a direct parallel solver. Based on our analysis, a multi-level synchronization check algorithm is proposed to reduce the overhead work due to data interactions among processors, thus maximizing the parallelism. In addition, a pre-ordering algorithm is implemented to avoid waiting resulting from variable column heights of the stiffness matrix. Several benchmark problems are presented to show the speedup and efficiency of the proposed algorithms

Numerical simulation of turbulent fluctuations along the axis of a bridge

There are many potential applications where it would be useful to digitally generate the turbulent wind velocity time histories in the wind engineering study. One of the widely used numerical techniques for wind field simulation is the spectral representation method. However, the main drawback of this method is being computationally expensive for the multivariate and multidimensional processes problems. In this paper, a modified spectral representation method is proposed specifically for generation of the turbulent fluctuations along a bridge deck at the same elevation. The proposed method does not require decomposition of the spectral density matrix, thus it is more efficient than the original spectral representation method. As an example, the method is applied to the simulation of the wind field for a long-span suspension bridge. A comparison of the simulated covariance functions with their target functions is also included. (C) 1998 Elsevier Science Ltd. All rights reserved

Structural damage detection using an iterative neural network

A structural damage detection method based on parameter identification using an iterative neural network (NN) technique is proposed in this study. The NN model is first trained off-line using an initial training data set that consists of assumed structural parameters as outputs and their corresponding dynamic characteristics as inputs. The structural parameters are assumed with different levels of reduction to simulate various degrees of structural damage. The concept of orthogonal array is adopted to generate the representative combinations of parameter changes, which can significantly reduce the number of training data while maintaining the data completeness. A modified back-propagation learning algorithm is proposed which can overcome possible saturation of the sigmoid function and speed up the training process. The trained NN model is used to predict the structural parameters by feeding in measured dynamic characteristics. The predicted structural parameters are then used in the FE model to calculate the dynamic characteristics. The NN model would go through a retraining process if the calculated characteristics deviate from the measured ones. The identified structural parameters are then used to infer the location and the extent of structural damages. The proposed method is verified both numerically and experimentally using a clamped-clamped T beam. The results indicate that the current approach can identify both the location and the extent of damages in the beam

Iterative constrained optimization scheme for model updating of long-span bridges

This paper presents an iterative constrained optimization scheme for the finite element (FE) model updating of long-span bridges. The objective is to minimize the differences between the calculated and the measured frequencies by changing some selected structural parameters in the EE model. An eigenvalue sensitivity matrix is first obtained from the first-order Taylor series expansion of the eigenvalues with respect to these selected parameters. A set of linear equations relating the perturbation of parameters to the differences between the calculated and the measured frequencies is then established. The selected parameters are assumed to be bounded within some prescribed regions according to the degrees of uncertainty and variation existing in the parameters based on some engineering judgement. The changes of these parameters are found in an iterative fashion by solving a quadratic programming problem. This model updating scheme is applied to both a 1/150 scaled suspension bridge model in the laboratory and an actual cable-stayed bridge in the field. The results show that the natural frequencies calculated from the FE models after this updating process can be quite close to those of the measured values

On the control of pressure oscillation in bilinear-displacement constant-pressure element

Due to the singularity in the global pressure matrix equation, the bilinear-displacement constant-pressure element produces an uncontrollable checkerboard pattern in the pressure solution. Although the well-known penalty treatment in the pressure equation overcomes the singularity problem, it still produces oscillatory pressure under certain conditions. The proposed method employs a rank-one filtering operator in the pressure equation to filter out the checkerboard mode without affecting the non-checkerboarding pressure field. The numerical results suggest that the pressure oscillation in the penalty method and the rank-one filtering method is a result of the absence of the checkerboard mode. A pressure recovery procedure is then suggested to recover the appropriate checkerboard mode. Numerical results for both solid and fluid mechanics problems are included

Updating structural parameters: An adaptive neural network approach

An adaptive neural network (NN) method is proposed for the model updating and the damage detection of structures. The NN model is first trained off-line and is retrained during iteration if needed. An improved back-propagation learning algorithm with a jump factor and a dynamical learning rate is developed to facilitate the training. The concept of orthogonal array is adopted in this study to reduce the number of training samples required. Two examples illustrate that the proposed technique is quite useful for the model updating and the damage detection of structures

An object-oriented database management system for computer-aided design of tall buildings

An effective database and database management system is the key to the success of an integrated approach to software engineering applications in general, and Computer-Aided Design (CAD) for structural applications in particular. Due to the inherent nature of CAD data such as dynamic modeling, a wide range of data types, large data volume, etc., the traditional database models, such as hierarchical, network and relational models, are unable to handle the aforementioned applications satisfactorily. An object-oriented data modeling is known to be the most effective approach. However, many of the commercial object-oriented databases are designed for information management, and they are inadequate for CAD application due to the different features of the object-hierarchy and varying data management objectives during the design cycles. This paper present a hierarchical index-based object-oriented database management model for CAD applications. To deal with the object hierarchy encountered in CAD for the design of tall buildings, the proposed database consists of several salient features: a hierarchical object model, its related storage structure, a data dictionary, a class factory and an index system. The proposed database management model has been implemented into an integrated CAD system for design application of tall buildings

Selection of training samples for model updating using neural networks

One unique feature of neural networks is that they have to be trained to function. In developing an iterative neural network technique for model updating of structures, it has been shown that the number of training samples required increases exponentially as the number of parameters to be updated increases. Training the neural network using these samples becomes a time-consuming task. In this study, we investigate the use of orthogonal arrays for the sample selection. A comparison between this orthogonal arrays method and four other methods is illustrated by two numerical examples. One is the update of the felxural rigidities of a simply supported beam and the other is the update of the material properties and the boundary conditions of a circular plate. The results indicate that the orthogonal arrays method can significantly reduce the number of training samples without affecting too much the accuracy of the neural network prediction. (C) 2002 Academic Press