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Bio: null null is an academic researcher. The author has contributed to research in topics: Precast concrete & Seismic analysis. The author has an hindex of 22, co-authored 89 publications receiving 3863 citations.

Minimum Design Loads and Associated Criteria for Buildings and Other Structures

Abstract: Prepared by the Committee on Minimum Design Loads for Buildings and Other Structures of the Codes and Standards Activities Division of the Structural Engineering Institute of ASCE Minimum Design Lo...

Specification for the Design of Cold-Formed Stainless Steel Structural Members

Abstract: Specification for the Design of Cold-Formed Stainless Steel Structural Members provides design criteria for the determination of the strength of stainless steel structural members and connections f...

A Framework for Dealing With Uncertainty due to Model Structure Error

Abstract: Although uncertainty about structures of environmental models (conceptual uncertainty) is often acknowledged to be the main source of uncertainty in model predictions, it is rarely considered in environmental modelling. Rather, formal uncertainty analyses have traditionally focused on model parameters and input data as the principal source of uncertainty in model predictions. The traditional approach to model uncertainty analysis, which considers only a single conceptual model, may fail to adequately sample the relevant space of plausible conceptual models. As such, it is prone to modelling bias and underestimation of predictive uncertainty. In this paper we review a range of strategies for assessing structural uncertainties in models. The existing strategies fall into two categories depending on whether field data are available for the predicted variable of interest. To date, most research has focussed on situations where inferences on the accuracy of a model structure can be made directly on the basis of field data. This corresponds to a situation of ‘interpolation’. However, in many cases environmental models are used for ‘extrapolation’; that is, beyond the situation and the field data available for calibration. In the present paper, a framework is presented for assessing the predictive uncertainties of environmental models used for extrapolation. It involves the use of multiple conceptual models, assessment of their pedigree and reflection on the extent to which the sampled models adequately represent the space of plausible models. � 2005 Elsevier Ltd. All rights reserved.