The resilience of civil engineering structures has traditionally been associated with the design of individual elements with sufficient capacity to respond appropriately to adverse events. This has traditionally employed ‘robust’ design procedures that focus on defining safety factors for individual adverse events and providing redundancy. As such, construction materials have traditionally been designed to specific technical specifications. Furthermore, material degradation is viewed as inevitable and mitigation necessitates expensive maintenance regimes. Based on a better understanding of natural biological systems, biomimetic materials that have the ability to adapt and respond to their environment have recently been developed. This fundamental change has the potential to facilitate the creation of a wide range of ‘smart’ materials and intelligent structures that can self-sense and self-repair without the need for external intervention which could transform infrastructure. This paper presents an overview of the development, application and commercial perspectives of a suite of complementary self-healing cementitious systems that have been developed as part of a national team and led to the first UK full-scale field trials on self-healing concrete.

We investigate opportunities offered by telematics and analytics to enable better informed, and more integrated, collaborative management decisions on construction sites. We focus on efficient refuelling of assets across construction sites. More specifically, we develop decision support models that, by leveraging data supplied by different assets, schedule refuelling operations by minimising the distance travelled by the bowser truck as well as fuel shortages. Motivated by a practical case study elicited in the context of a project we recently conducted at Crossrail, we introduce the Dynamic Bowser Routing Problem. In this problem the decision maker aims to dynamically refuel, by dispatching a bowser truck, a set of assets which consume fuel and whose location changes over time; the goal is to ensure that assets do not run out of fuel and that the bowser covers the minimum possible distance. We investigate deterministic and stochastic variants of this problem and introduce effective and scalable mathematical programming models to tackle these cases. We demonstrate the effectiveness of our approaches in the context of an extensive computational study designed around data collected on site as well as supplied by our project partners.

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