Cost and safety optimisation in 'Berlin' type retaining walls

Studies for earth retaining wall structures provide engineers with the values for the response of design characteristics which represents the stability and the required budget for the completion of a project. Fundamental theories guide engineers to combinations of design variables values. These values have a direct relation to the responses of the earth retaining wall structure. The requirement of this analysis is that there is no proven technique which ensures the best combination of design variables for the simultaneous optimisation of safety factor and overall cost of a project. This paper presents an integration of the desirability analysis which provides the multivariate optimisation with the performance of few experimental runs based on statistical tools and finite elements methodology. The methodology provides a 24% higher safety factor and 50% lower overall cost comparing to the results of an experienced foundation engineering company

ANOVA results for the PCR data of epidemiological typing of <i>Pseudomonas aeruginosa</i> above (MINITAB 16.2).

<p>*Not calculable.</p><p>ANOVA results for the PCR data of epidemiological typing of <i>Pseudomonas aeruginosa</i> above (MINITAB 16.2).</p

Product Screening to Multicustomer Preferences: Multiresponse Unreplicated Nested Super-ranking

Modern production methods demand the synchronous multicharacteristic optimization of goods. There is a need to diversify a basic product to the importance placed on its individual quality components by a wide spectrum of concerned customers. This work shows how the super-ranking concept may be utilized taking into account relative weights among the implicated responses. The theoretical development is focused on the difficult situation where the optimization is attempted through unreplicated and saturated fractional factorial designs. A nested super-ranking scheme is devised to accommodate a dual weight assignment, first by setting up a single consolidated response per implicated customer and then, in a subsequent step, by incorporating a customer importance rating thus rendering an overall single master response. A demonstration of the proposed method on a pragmatic problem arising in aluminum milling involves optimization due to seven controlling factors concurrently influencing nine product responses modulated by six preference ratings set by a given customer base, respectively. Key benefits of this method are the offered ease of intermixing numerical and categorical data in mainstream multiresponse optimization problems, and keeping customer preferences in perspective through economical, short-cycle screening while relaxing stringent data normality and possible multidistributional effects among the implicated quality characteristics

Main effects plot for the means of the filtration time.

<p>Main effects plot for the means of the filtration time.</p