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If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -The 4D CAD model has been accepted for better conceptualizing and comprehending the sequences and spatial constraints in a construction schedule. The purpose of this paper is to identify the deficiencies of the visualization of the 4D CAD model and to propose improvements. Design/methodology/approach -The presentation abilities of the existing 4D CAD model are analyzed and compared with the other conventional methods, namely Gantt chart, network diagram, and the calendar. Four aspects of the visualization are addressed, namely the overview of a schedule, the duration of an activity, the relationship of an activity, and the project progress tracking. The proposed improvements employed different visual properties of 3D CAD objects such as color, line weight, and line type to represent the different activities' performing statuses. A prototype of the 4D CAD model with enhanced visualization was developed on a construction project case. Findings -The model evaluation showed that this development could enhance the visualization of the 4D CAD model and provide a more informative construction schedule. Original/value -It is anticipated that the 4D CAD model with these enhancements can substitute for conventional presentation methods of construction schedules.

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The application of an artificial neural network (ANN) to forecast the construction duration of buildings at the predesign stage is described in this paper. A three‐layered back‐propagation (BP) network consisting of 11 input nodes has been constructed. Ten binary input nodes represent basic information on building features (i.e. building function, structural system, foundation, height, exterior finishing, quality of interior decorating, and accessibility to the site), and one real‐value input represents functional area. The input nodes are fully connected to one output node through hidden nodes. The network was implemented on a Pentium‐150 based microcomputer using a neurocomputer program written in C+ +. The Generalized Delta Rule (GDR) was used as learning algorithm. One hundred and thirty‐six buildings built during the period 1987–95 in the Greater Bangkok area were used for training and testing the network. The determination of the optimum number of hidden nodes, learning rate, and momentum were based on trial‐and‐error. The best network was found to consist of six hidden nodes, with a learning rate of 0.6, and null momentum. It was trained for 44700 epochs within 943 s such that the mean squared error (judgement) of training and test samples were reduced to 1.17 × 10−7 and 3.10 × 10−6, respectively. The network can forecast construction du‐ration at the predesign stage with an average error of 13.6%.