Hamsa Hameed Ahmed Algodi

Thesis Name

Thesis Abstract

Australia’s housing sector faces a multifaceted crisis characterized by rising unaffordability, sustainability imperatives, and the urgent need to meet accelerated housing delivery targets. The National Housing Accord (2023–2034) outlines a national commitment to construct 1.2 million new homes by 2029. However, current projections indicate a shortfall of approximately 462,000 dwellings, driven by construction sector inefficiencies, labour shortages, and escalating material costs. Compounding these issues is the sector’s significant environmental footprint, contributing 23% of national greenhouse gas emissions, largely from residential buildings’ operational energy and embodied carbon.

In response, Australia has enforced more stringent sustainability standards such as the National Construction Code (NCC 2025), which mandates a 7-Star NatHERS energy rating and Whole-of-Home assessments. Complementary frameworks like Green Star Homes and NABERS for Residential further require net-zero alignment, carbon tracking, and indoor environment optimization. These regulations, while essential, have intensified decision-making complexity for developers, planners, and policymakers who must balance compliance, cost-effectiveness, stakeholder needs, and rapid timelines.

Value Management (VM), a structured methodology for aligning functions with stakeholder values, has long served as a cornerstone of decision-making in construction. However, traditional VM methods are increasingly outdated in today’s data-rich, fast-paced context. They suffer from limitations such as qualitative bias, limited data integration, and inflexible processes. Concurrently, vast digital datasets—ranging from Building Information Modelling (BIM) and lifecycle cost assessments to post-occupancy energy performance—remain underutilized in VM decision-making.

Three-Way Decision (TWD) models, which categorize options as Accept, Defer, or Reject, offer a robust foundation for managing uncertainty and incomplete information. When paired with intelligent automation and machine learning, TWD can enable real-time, traceable, and adaptive decision support, transforming how sustainability and compliance decisions are made.

This research proposes the development of an Intelligent Automated Data Science–Driven Three-Way Decision–Value Management (IA–DS–TWD–VM) framework tailored to Australia’s sustainable housing goals. The framework will integrate diverse real-time data sources, apply advanced analytics to optimize trade-offs, and automatically generate TWD outcomes that are transparent, and evidence based.

Research Questions:

1. How can intelligent automated structured data analytics, combined with adaptive TWD logic, be embedded within VM processes to support sustainable housing decision-making and regulatory compliance in Australia?
2. What are the measurable impacts of the IA–DS–TWD–VM framework on sustainability performance, cost efficiency, and stakeholder satisfaction in housing infrastructure projects?

Why My Research is Important/Impacts

The proposed research is highly significant for Australia’s sustainable housing and climate adaptation agenda. It will directly support the National Housing Accord by improving project delivery efficiency, affordability, and regulatory alignment through intelligent automation. The IA–DS–TWD–VM framework will offer a unique, adaptive decision-support system that ensures real-time compliance with NCC 2025, Green Star Homes, and NABERS benchmarks.

Moreover, it will enable more cost-effective and data-driven sustainability prioritization, reducing reliance on subjective judgments. By integrating real-time BIM, cost, and emissions data, the framework empowers industry stakeholders to make transparent, evidence-based decisions. Its deployment will enhance housing project accountability, improve environmental performance, and increase stakeholder confidence in public and private sector projects.

From a broader perspective, this research sets a new benchmark in the fusion of value management, data science, and policy compliance—contributing to knowledge, innovation, and practical outcomes for the built environment.