Using experiments and expert judgment to model catchability of Pacific rockfishes in trawl surveys, with application to bocaccio (Sebastes paucispinis) off British Columbia

The time series of abundance indices for many groundfish populations, as determined from trawl surveys, are often imprecise and short, causing stock assessment estimates of abundance to be imprecise. To improve precision, prior probability distributions (priors) have been developed for parameters in stock assessment models by using meta-analysis, expert judgment on catchability, and empirically based modeling. This article presents a synthetic approach for formulating priors for rockfish trawl survey catchability (qgross). A multivariate prior for qgross for different surveys is formulated by using 1) a correction factor for bias in estimating fish density between trawlable and untrawlable areas, 2) expert judgment on trawl net catchability, 3) observations from trawl survey experiments, and 4) data on the fraction of population biomass in each of the areas surveyed. The method is illustrated by using bocaccio (Sebastes paucipinis) in British Columbia. Results indicate that expert judgment can be updated markedly by observing the catch-rate ratio from different trawl gears in the same areas. The marginal priors for qgross are consistent with empirical estimates obtained by fitting a stock assessment model to the survey data under a noninformative prior for qgross. Despite high prior uncertainty (prior coefficients of variation ≥0.8) and high prior correlation between qgross, the prior for qgross still enhances the precision of key stock assessment quantities.

Expert system for prestressed concrete bridge design

The development of an expert system, BRIDEX, for the design of prestressed concrete bridges is discussed in this paper. Design of multi-span continuous pre-stressed concrete bridges pose considerable difficulties to designers because of the large number of parameters involved and their complex interactions. The design is often perceived as an iterative process of generation, evaluation and modification of trial designs. It takes years of experience to develop an understanding of the design process. BRIDEX is aimed at providing guidance to the designers by suggesting appropriate range of values for the design parameters. The knowledge within BRIDEX is mainly based on fundamental principles developed by a careful study of the intricacies involved in the design process, while heuristics are used only to supplement this knowledge. The BRIDEX approach ensures that the whole design evolves sequentially as the design proceeds, module after module.