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Urban infrastructure assets (e.g. roads, water pipes) perform critical functions to the health and well-being of society. Although it has been widely recognised that different infrastructure assets are highly interconnected, infrastructure management in practice such as planning, installation and maintenance are often undertaken by different stakeholders without considering these dependencies due to the lack of relevant data and cross-domain knowledge, which may cause unexpected cascading social, economic and environmental effects. In this paper, we present a knowledge based decision support system for urban infrastructure inter-asset management. By considering various infrastructure assets (e.g. road, ground, cable), triggers (e.g. pipe leaking) and potential consequences (e.g. traffic disruption) as a holistic system, we model each sub-domain using a modular ontology and encapsulate the interdependence between them using a set of rules. Moreover, qualitative likelihood is assigned to each rule by domain experts (e.g. civil engineers) to encode the uncertainty of knowledge, and an inference engine is applied to predict the potential consequences of a given trigger with location specific data and the encoded rules. A web-based prototype system has been developed based on the above concept and demonstrated to a wide range of stakeholders. The system can assist in the process of decision making by aiding data collation and integration, as well as presenting potential consequences of possible triggers, advising on whether additional information is needed or suggesting ways of obtaining such information. The work shows an intelligent approach to integrate and process multi-source data to pioneer a novel way to aid a complex decision process with a high social impact.

Summary Estimating the permeability of superficial deposits is fundamental to many aspects of catchment science, but can be problematic where insufficient in situ measurements are available from pumping tests in piezometers. Consequently, common practice is to estimate permeability from the material description or, where available, particle size distribution using a formula such as Hazen. In this study, we examine the relationships between particle size, relative density and hydraulic conductivity in superficial deposits in Morayshire, Northern Scotland: a heterogeneous environment typical of many catchments subject to previous glaciations. The superficial deposits comprise glaciofluvial sands and gravels, glacial tills and moraines, raised marine sediments, and blown sands. Thirty-eight sites were investigated: hydraulic conductivity measurements were made using repeated Guelph permeameter measurements, cone resistance was measured in situ with a Panda dynamic cone penetrometer; material descriptions were made in accordance with BS5930:1999; and disturbed samples were taken for particle size analysis. Overall hydraulic conductivity ( K ) varied from 0.001 m/d to >40 m/d; glacial till had the lowest K (median 0.027 m/d) and glacial moraine the highest K (median 30 m/d). However, within each geological unit there was great variability in measured hydraulic conductivity values. Multiple linear regression of the data indicated that log d 10 and relative density (indicated by cone resistance or BS5930:1999 soil state description) were independent predictors of log K and together gave a relationship with an R 2 of 0.80. Material description using the largest fraction (e.g. sand or gravel) had little predictive power. Therefore, in heterogeneous catchments, the permeability of superficial deposits is most strongly related to the finest fraction ( d 10 ) and relative density of the material. In situ Guelph permeameter measurements at outcrops with good geological characterisation provide an easy and reliable method of determining the permeability of particular units of superficial deposits.