A multi-domain Matrix (MDM) is used to identify and analyse rework loops within the Building Design (BD) process caused by building regulations. To model this process, we used information from official documents, plans of work, email archives, and anecdotal evidence as a case study. Findings reveal that MDM can identify the rework loops caused by building regulations, this information forms the basis to improve current BD processes. The complexity of the rework loop including the processes, people and, deliverables involved in the rework loop is also identified. Further analysis can also use the MDM to estimate the costs incurred with each rework cycle. This MDM can also identify rework loops in other engineering design processes due to design changes.

This study is part of a PhD project exploring engineering productivity in the construction industry using DRM. The results present an MDM that provides situation-specific insight, offering areas of process improvement, and support through engineering productivity measurement. This is one of the few studies that models rework loops in the BD process caused by regulations.

Design process modelling is well-founded in fields of mechanical engineering, and product design and development but not in Building Design (BD). This paper looks at the selection process when choosing appropriate models for specific BD processes. The paper adapts process model selection criteria from Trauer's work and combines it with anecdotal evidence from the authors to select these models. The selection criteria were ranked, categorised, and applied to BD processes explained. Process models related to each selection criteria were then selected from backward snowballing of literature.

We develop a model that successfully learns social and organizational human network structure using ambient sensing data from distributed plug load energy sensors in commercial buildings. A key goal for the design and operation of commercial buildings is to support the success of organizations within them. In modern workspaces, a particularly important goal is collaboration, which relies on physical interactions among individuals. Learning the true socio-organizational relational ties among workers can therefore help managers of buildings and organizations make decisions that improve collaboration. In this paper, we introduce the Interaction Model, a method for inferring human network structure that leverages data from distributed plug load energy sensors. In a case study, we benchmark our method against network data obtained through a survey and compare its performance to other data-driven tools. We find that unlike previous methods, our method infers a network that is correlated with the survey network to a statistically significant degree (graph correlation of 0.46, significant at the 0.01 confidence level). We additionally find that our method requires only 10 weeks of sensing data, enabling dynamic network measurement. Learning human network structure through data-driven means can enable the design and operation of spaces that encourage, rather than inhibit, the success of organizations.

We propose a novel formal framework (called 3D-NCDC-ASP) to represent and reason about cardinal directions between extended objects in 3-dimensional (3D) space, using Answer Set Programming (ASP). 3D-NCDC-ASP extends Cardinal Directional Calculus (CDC) with a new type of default constraints, and NCDC-ASP to 3D. 3D-NCDC-ASP provides a flexible platform offering different types of reasoning: Nonmonotonic reasoning with defaults, checking consistency of a set of constraints on 3D cardinal directions between objects, explaining inconsistencies, and inferring missing CDC relations. We prove the soundness of 3D-NCDC-ASP, and illustrate its usefulness with applications.

Older people living in residential and nursing care homes spend a large proportion of their time within the boundaries of the home, and may depend on the environment to compensate for their physical or cognitive frailties. Regulations and guidelines on the design of care buildings have accumulated over time with little knowledge of their impact on the quality of life of building users. The Design in Caring Environments Study (DICE) collected cross-sectional data on building design and quality of life in 38 care homes in and near Sheffield, Yorkshire. Quality of life was assessed using methods which included all residents regardless of their frailty, and staff morale was also assessed. The physical environment was measured on 11 user-related domains using a new tool, the Sheffield Care Environment Assessment Matrix (SCEAM). Significant positive associations were found between several aspects of the built environment and the residents' quality of life. There was evidence that a focus on safety and health requirements could be creating risk-averse environments which act against quality of life, particularly for the least frail residents. Staff morale was associated with attributes of a non-institutional environment for residents rather than with the facilities provided for the staff. The new tool for assessing building design has potential applications in further research and for care providers.

A case-based design functionality is a natural and intuitive addition to a design tool that can augment human capabilities and help designers remember and retrieve appropriate cases. SEED-Config, a design environment for conceptual building design, was developed to incorporate a case-based reasoning functionality to provide designers with initial potential solutions. The case representation in SEED-Config is the BENT information model, which records design knowledge, supports the hierarchical decomposition of design cases, offers multiple views, and encapsulates the outcome of the design in addition to the problem specification and the design solution. The case library was implemented in an object-oriented database management system to accumulate cases automatically and to provide efficient query facilities. The case retrieval aspect of SEED-Config offers three different methods to find the most useful cases stored in the case library: task-based, lineage-based, and customized. Case retrieval responds to the exploratory nature of the design process and supports versatile case retrieval by providing multiple paths to each case. The case adaptation aspect, which adjusts the selected case to the new problem to provide a complete solution, uses an adaptation method called derivational replay. The case-based design capabilities are completely integrated within the design environment from which the cases originate.