Doctor of Philosophy in Engineering and The Built Environment

London South Bank University

UK,England

 0 Shortlist

36 Months

Duration

CAD 17,600/year

Tuition Fee

CAD 0 FREE

Application Fee

Sep 2025

Apply Date

UK, England

Type: University

Location Type: Urban

Founded: 1992

Total Students: 16,840 +

Int. Students: 12,725 +

Campus Detail

Main Campus Address

103 Borough Rd, London SE1 0AA, United Kingdom

Doctor of Philosophy in Engineering and The Built Environment

Program Overview

LSBU is the top modern university in London for world-leading and internationally excellent research in General Engineering, which includes The Built Environment and Architecture research - Research Excellence Framework (REF) 2014.

The wide range of research activities in our School, together with the mix of academic staff, post-doctoral research fellows and visiting professors, allows us to offer a stimulating and diverse environment.

Aims:

  • This PhD programme aims to design, build, and test an intelligent low-cost and low carbon noise monitoring system using green technology. The low carbon system will be capable of monitoring environmental noise for a longer period.

Methods:

  • There is a growing need (regulation-driven) to monitor airports with a low cost, robust, and simple system. The AI and machine learning algorithms will be mitigating most of the laborious work. The expected research impact will show how such a system could be implemented around a noisy environment such as an airport to monitor environmental noise. An environmental sound monitor will be built and programmed using low-cost commercial electronics such as MEMS microphones, drastically reducing the cost of implementation. The system developed will be tested to international standards for compliance. Another new aspect of this research is that the system will be networked to create a platform. This platform would capture the sound environment and tag sound event spectrograms at the local level through Edge AI classification.
  • A central server will host the main AI. This will improve the accuracy of the classification through directed learning, without requiring user intervention. This will reduce the laborious nature of tagging unusual sound events which breach planning, construction, or demolition conditions.