Addressing building adaptability and resilience through smart context aware materials and building components

Traditionally, construction materials are designed and manufactured to meet a prescribed specification; material degradation is viewed as inevitable and mitigation necessitates expensive maintenance regimes. More recently, based on a better knowledge of biological systems, materials that have the ability to adapt and respond to their environment are being developed. This fundamental change in material design philosophy facilitates the creation of a wide range of ‘smart’ materials and intelligent structures, including both autogenous and autonomic self-healing materials, and adaptable, self-repairing structures that can positively react to their environment.

The overarching research questions that the proposed PhD will address are:

How do we confer optimum resilience, sustainability, and continual fitness for purpose on our existing building stock?

How do we deliver new or refurbished human-centric buildings that address lifetime requirements and that are capable of performing optimally within the constraints of unknown future scenarios?

These questions translate into the following objectives i) Develop a new generation of materials that provide condition feedback and embed shape memory, energy sensitivity, and self- healing properties (enabled by self-triggering biological or other processes) and; ii) Develop a new generation of smart (parametric, modular, inter-operable, and context-aware) building components with embedded intelligence that enable buildings to be assembled from, or refurbished / retrofitted with, smarter engineered products and systems, while providing greater flexibility to satisfy larger customer choices and changing regulatory requirements.

Candidates should hold or expect to gain a first class numerate degree or a good 2.1 and/or an appropriate Master’s level qualification (or their equivalent). Applicants whose first language is not English will be required to demonstrate proficiency in the English language (IELTS 6.5 or equivalent). The candidate is expected to have good background either in tests of material properties, or in mechanics analysis and computer simulations.