Using machine learning for automated detection of ambiguity in building requirements

The rule interpretation step is yet to be fully automated in the compliance checking process, hindering the automation of compliance checking. Whilst existing research has developed numerous methods for automated interpretation of building requirements, none can identify ambiguous requirements. As part of interpreting ambiguous clauses automatically, this research proposed a supervised machine learning method to detect ambiguity automatically, where the best-performing model achieved recall, precision and accuracy scores of 99.0%, 71.1%, and 78.2%, respectively. This research contributes to the body of knowledge by developing a method for automated detection of ambiguity in building requirements to support automated compliance checking

Capillary nanowaves on surfactant-laden liquid films with surface viscosity and elasticity

Thermal motions of molecules can generate nanowaves on the free surface of a liquid film. As nanofilms are susceptible to the contamination of surfactants, this work investigates the effects of surfactants on dynamics of nanowaves on a bounded film with a finite depth, using both molecular dynamics simulations and analytical theories. In molecular simulations, a bead-spring model is adopted to simulate surfactants, where beads are connected by the finite extensive nonlinear elastic potentials. Fourier transforms of the film surface profiles $h(x,t)$ extracted from molecular simulations are performed to obtain the static spectrum $|h_q|_{\mathrm{rms}}$ and temporal correlations of surface modes $$. It is shown that the spectral amplitude is increased for the contaminated liquid surface compared to the clean surface because surfactants can decrease surface tension. A higher concentration of surfactants on the surface not only decreases the surface tension but also causes elastic energy to the free surface, as the scaling of spectral amplitude with wavenumbers changes from $|h_q|_{\mathrm{rms}}\sim q^{-1}$ to $|h_q|_{\mathrm{rms}}\sim q^{-2}$ for modes with large wavenumbers. Regarding the temporal correlations of surface modes, it is observed that the presence of surfactants leads to a slower decay, which, however, cannot be predicted by only considering the decreased surface tension. Based on the Boussinesq Scriven model for surface viscosity, a linear stability analysis of Stokes flow for films with arbitrary depth is conducted and the obtained dispersion relation considering surface viscosity can justify the simulation results

Novel Muscle Monitoring by Radiomyography(RMG) and Application to Hand Gesture Recognition

Conventional electromyography (EMG) measures the continuous neural activity during muscle contraction, but lacks explicit quantification of the actual contraction. Mechanomyography (MMG) and accelerometers only measure body surface motion, while ultrasound, CT-scan and MRI are restricted to in-clinic snapshots. Here we propose a novel radiomyography (RMG) for continuous muscle actuation sensing that can be wearable and touchless, capturing both superficial and deep muscle groups. We verified RMG experimentally by a forearm wearable sensor for detailed hand gesture recognition. We first converted the radio sensing outputs to the time-frequency spectrogram, and then employed the vision transformer (ViT) deep learning network as the classification model, which can recognize 23 gestures with an average accuracy up to 99% on 8 subjects. By transfer learning, high adaptivity to user difference and sensor variation were achieved at an average accuracy up to 97%. We further demonstrated RMG to monitor eye and leg muscles and achieved high accuracy for eye movement and body postures tracking. RMG can be used with synchronous EMG to derive stimulation-actuation waveforms for many future applications in kinesiology, physiotherapy, rehabilitation, and human-machine interface

Towards fully-automated code compliance checking of building regulations: challenges for rule interpretation and representation

Before the building design is finalised, it needs to be checked against regulations. Traditionally, manual compliance checking is error-prone and time-consuming. As a solution, automatic compliance checking (ACC) was proposed. Many studies have focused on the crucial ACC rule interpretation process, yet no research has synthesised the themes and identified future research opportunities. This paper thus aims to fill this gap by conducting a systematic literature review and identifying challenges facing this field. Findings revealed that the representation development process lacks a methodological backdrop. Understandings of rules, representations, and relationships between them are insufficient. Potential solutions were proposed to address these challenges

Unpacking Ambiguity in Building Requirements to Support Automated Compliance Checking

In the architecture, engineering, and construction (AEC) industry, manual compliance checking is labor-intensive, time-consuming, expensive, and error-prone. Automated compliance checking (ACC) has been extensively studied in the past 50 years to improve the productivity and accuracy of the compliance checking process. While numerous ACC systems have been proposed, these systems can only deal with requirements that include quantitative metrics or specified properties. This leaves the remaining 53% of building requirements to be checked manually, mainly due to the ambiguity embedded in them. In the literature, little is known about the ambiguity of building requirements, which impedes their accurate interpretation and automated checking. This research thus aims to address this issue and establish a taxonomy of ambiguity. Building requirements in health building notes (HBNs) are analyzed using an inductive approach. The results show that some ambiguous clauses in building requirements reflect regulators’ intention while others are unintentional, resulting from the use of language, tacit knowledge, and ACC-specific reasons. This research is valuable for compliance-checking researchers and practitioners because it unpacks ambiguity in building requirements, laying a solid foundation for addressing ambiguity appropriately

Automated generation of SPARQL queries from semantic mark-up

Previous work has shown that semantic mark-up of normative documents can be consumed directly by a rule-engine or can be automatically transformed to a number of existing rule representations. This work investigates the feasibility of automatically transforming examples of normative documents into SPARQL and testing the result against typical building information models. The desirability of using SPARQL is discussed

statistical methods on risk management of extreme events

The goal of the dissertation is the investigation of financial risk analysis methodologies, using the schemes for extreme value modeling as well as techniques from copula modeling. Extreme value theory is concerned with probabilistic and statistical questions re- lated to unusual behavior or rare events. The subject has a rich mathematical theory and also a long tradition of applications in a variety of areas. We are interested in its application in risk management, with a focus on estimating and forcasting the Value-at-Risk of financial time series data. Extremal data are inherently scarce, thus making inference challenging. In order to obtain good estimates for risk measures, we develop a two-stage approach: (1) fitting the GARCH-type models at the first stage to describe the volatility clustering and other stylized facts of financial time series; (2) using the extreme value theory based models to fit to the tails of the residuals. Additionally, the performance measures provide information in terms of the comparison of the two-stage semi-parametric approach with the parametric methodologies, through robust backtesting. Copula is a particular branch of probability theory, with which, given sufficient data, we can separate the marginal behavior of individual risks and their dependence structure from a multivariate random variable. Linear correlation is widely used to model dependence but has limitations as a measure of association and thus we opt to use copulas to analyze the dependence structure and build models for our different problems arising in risk management. For this part of the dissertation, we take a look at different copula families, highlight for some when they are most appropriate to use for a particular application, discuss some of their drawbacks as diverse scenarios occur in different risk management models, and explore the possibility of developing the copula modeling to reflect the complicated dependence structure of portfolios

Cue word guided question generation with BERT model fine-tuned on natural question dataset

This thesis aims to develop an efficient question generator for an automated tutoring system. Given a context passage with an answer, the question generator asks questions to help the reader learn new material. By utilizing the BERT model, this thesis experiments on generating type-specific questions with a cue word. This thesis also uses an RNN encoder-decoder architecture for question generation on SQuAD as a comparative baseline and fine-tune the BERT question generation model on Google's Natural Question dataset. Ultimately, I deliver a RESTful API by the end of this year-long master program