Digital modelling for sustainability

Digital modelling tools are the next generation of computer aided design (CAD) tools for the construction industry. They allow a designer to build a virtual model of the building project before the building is constructed. This supports a whole range of analysis, and the identification and resolution of problems before they arise on-site, in ways that were previously not feasible.

Three-dimensional geological modelling and multivariate statistical analysis of water chemistry data to analyse and visualise aquifer structure and groundwater composition in the Wairau Plain, Marlborough District, New Zealand

Concerns regarding groundwater contamination with nitrate and the long-term sustainability of groundwater resources have prompted the development of a multi-layered three dimensional (3D) geological model to characterise the aquifer geometry of the Wairau Plain, Marlborough District, New Zealand. The 3D geological model which consists of eight litho-stratigraphic units has been subsequently used to synthesise hydrogeological and hydrogeochemical data for different aquifers in an approach that aims to demonstrate how integration of water chemistry data within the physical framework of a 3D geological model can help to better understand and conceptualise groundwater systems in complex geological settings. Multivariate statistical techniques(e.g. Principal Component Analysis and Hierarchical Cluster Analysis) were applied to groundwater chemistry data to identify hydrochemical facies which are characteristic of distinct evolutionary pathways and a common hydrologic history of groundwaters. Principal Component Analysis on hydrochemical data demonstrated that natural water-rock interactions, redox potential and human agricultural impact are the key controls of groundwater quality in the Wairau Plain. Hierarchical Cluster Analysis revealed distinct hydrochemical water quality groups in the Wairau Plain groundwater system. Visualisation of the results of the multivariate statistical analyses and distribution of groundwater nitrate concentrations in the context of aquifer lithology highlighted the link between groundwater chemistry and the lithology of host aquifers. The methodology followed in this study can be applied in a variety of hydrogeological settings to synthesise geological, hydrogeological and hydrochemical data and present them in a format readily understood by a wide range of stakeholders. This enables a more efficient communication of the results of scientific studies to the wider community.

Assessment of flood-related recharge to alluvial aquifers of an irrigated catchment following extended drought conditions using 3D visualisation and environmental tracers, Lockyer Valley, southeast Queensland, Australia.

The Lockyer Valley in southeast Queensland, Australia, hosts an economically significant alluvial aquifer system which has been impacted by prolonged drought conditions (~1997 to ~ 2009). Throughout this time, the system was under continued groundwater extraction, resulting in severe aquifer depletion. By 2008, much of the aquifer was at <30% of storage but some relief occurred with rains in early 2009. However, between December 2010 and January 2011, most of southeast Queensland experienced unprecedented flooding, which generated significant aquifer recharge. In order to understand the spatial and temporal controls of groundwater recharge in the alluvium, a detailed 3D lithological property model of gravels, sands and clays was developed using GOCAD software. The spatial distribution of recharge throughout the catchment was assessed using hydrograph data from about 400 groundwater observation wells screened at the base of the alluvium. Water levels from these bores were integrated into a catchment-wide 3D geological model using the 3D geological modelling software GOCAD; the model highlights the complexity of recharge mechanisms. To support this analysis, groundwater tracers (e.g. major and minor ions, stable isotopes, 3H and 14C) were used as independent verification. The use of these complementary methods has allowed the identification of zones where alluvial recharge primarily occurs from stream water during episodic flood events. However, the study also demonstrates that in some sections of the alluvium, rainfall recharge and discharge from the underlying basement into the alluvium are the primary recharge mechanisms of the alluvium. This is indicated by the absence of any response to the flood, as well as the observed old radiocarbon ages and distinct basement water chemistry signatures at these locations. Within the 3D geological model, integration of water chemistry and time-series displays of water level surfaces before and after the flood suggests that the spatial variations of the flood response in the alluvium are primarily controlled by the valley morphology and lithological variations within the alluvium. The integration of time-series of groundwater level surfaces in the 3D geological model also enables the quantification of the volumetric change of groundwater stored in the unconfined sections of this alluvial aquifer during drought and following flood events. The 3D representation and analysis of hydraulic and recharge information has considerable advantages over the traditional 2D approach. For example, while many studies focus on singular aspects of catchment dynamics and groundwater-surface water interactions, the 3D approach is capable of integrating multiple types of information (topography, geological, hydraulic, water chemistry and spatial) into a single representation which provides valuable insights into the major factors controlling aquifer processes.

Review of 3D model development with application to groundwater studies in New Zealand 1996 – 2011

Three dimensional geological modelling techniques have been applied since 1996 with an aim to characterise the lithological and chronological units of New Zealand’s many diverse aquifers. Models of property-scattered data have also been applied to assess physical properties of aquifers and the distribution of groundwater chemistry, including groundwater age, to inform an understanding of groundwater systems. These models, fundamental to understanding groundwater recharge, flow and discharge have found many uses as outlined in this paper.

Understanding hydrochemical processes using 3D models and hydrochemical facies in Wairau Plain groundwater, Marlborough

Three dimensional models and groundwater quality are combined to better understand and conceptualise groundwater systems in complex geological settings in the Wairau Plain, Marlborough. Hydrochemical facies, which are characteristic of distinct evolutionary pathways and a common hydrologic history of groundwaters, are identified within geological formations to assess natural water-rock interactions, redox potential and human agricultural impact on groundwater quality in the Wairau Plain.

2D-3D Face Recognition Based on PCA and Feature Modelling

Hybrid face recognition, using image (2D) and structural (3D) information, has explored the fusion of Nearest Neighbour classifiers. This paper examines the effectiveness of feature modelling for each individual modality, 2D and 3D. Furthermore, it is demonstrated that the fusion of feature modelling techniques for the 2D and 3D modalities yields performance improvements over the individual classifiers. By fusing the feature modelling classifiers for each modality with equal weights the average Equal Error Rate improves from 12.60% for the 2D classifier and 12.10% for the 3D classifier to 7.38% for the Hybrid 2D+3D clasiffier.

Life cycle modelling and design knowledge development in 3D virtual environments : final report

Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This final report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.

Life Cycle Modelling and Design Knowledge Development in 3D Virtual Environments

Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This Industry focused report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.

Conceptual Modelling in 3D Virtual Worlds for Process Communication

Traditionally, conceptual modelling of business processes involves the use of visual grammars for the representation of, amongst other things, activities, choices and events. These grammars, while very useful for experts, are difficult to understand by naive stakeholders. Annotations of such process models have been developed to assist in understanding aspects of these grammars via map-based approaches, and further work has looked at forms of 3D conceptual models. However, no one has sought to embed the conceptual models into a fully featured 3D world, using the spatial annotations to explicate the underlying model clearly. In this paper, we present an approach to conceptual process model visualisation that enhances a 3D virtual world with annotations representing process constructs, facilitating insight into the developed model. We then present a prototype implementation of a 3D Virtual BPMN Editor that embeds BPMN process models into a 3D world. We show how this gives extra support for tasks performed by the conceptual modeller, providing better process model communication to stakeholders..

Damage assessment of 3D reinforced concrete frame under external explosion loading

Multi-storey buildings are highly vulnerable to terrorist bombing attacks in various parts of the world. Large numbers of casualties and extensive property damage result not only from blast overpressure, but also from the failing of structural components. Understanding the blast response and damage consequences of reinforced concrete (RC) building frames is therefore important when assessing multi-storey buildings designed to resist normal gravity loads. However, limited research has been conducted to identify the blast response and damage of RC frames in order to assess the vulnerability of entire buildings. This paper discusses the blast response and evaluation of damage of three-dimension (3D) RC rigid frame under potential blast loads scenarios. The explicit finite element modelling and analysis under time history blast pressure loads were carried out by LS DYNA code. Complete 3D RC frame was developed with relevant reinforcement details and material models with strain rate effect. Idealised triangular blast pressures calculated from standard manuals are applied on the front face of the model in the present investigation. The analysis results show the blast response, as displacements and material yielding of the structural elements in the RC frame. The level of damage is evaluated and classified according to the selected load case scenarios. Residual load carrying capacities are evaluated and level of damage was presented by the defined damage indices. This information is necessary to determine the vulnerability of existing multi-storey buildings with RC frames and to identify the level of damage under typical external explosion environments. It also provides basic guidance to the design of new buildings to resist blast loads.

Visualisation modelling in 3D of an alluvial aquifer system at a valley-wide scale to understand groundwater status : Lockyer, QLD

Visualisation provides a method to efficiently convey and understand the complex nature and processes of groundwater systems. This technique has been applied to the Lockyer Valley to aid in comprehending the current condition of the system. The Lockyer Valley in southeast Queensland hosts intensive irrigated agriculture sourcing groundwater from alluvial aquifers. The valley is around 3000 km2 in area and the alluvial deposits are typically 1-3 km wide and to 20-35 m deep in the main channels, reducing in size in subcatchments. The configuration of the alluvium is of a series of elongate “fingers”. In this roughly circular valley recharge to the alluvial aquifers is largely from seasonal storm events, on the surrounding ranges. The ranges are overlain by basaltic aquifers of Tertiary age, which overall are quite transmissive. Both runoff from these ranges and infiltration into the basalts provided ephemeral flow to the streams of the valley. Throughout the valley there are over 5,000 bores extracting alluvial groundwater, plus lesser numbers extracting from underlying sandstone bedrock. Although there are approximately 2500 monitoring bores, the only regularly monitored area is the formally declared management zone in the lower one third. This zone has a calibrated Modflow model (Durick and Bleakly, 2000); a broader valley Modflow model was developed in 2002 (KBR), but did not have extensive extraction data for detailed calibration. Another Modflow model focused on a central area river confluence (Wilson, 2005) with some local production data and pumping test results. A recent subcatchment simulation model incorporates a network of bores with short-period automated hydrographic measurements (Dvoracek and Cox, 2008). The above simulation models were all based on conceptual hydrogeological models of differing scale and detail.

Improving remote collaborative process modelling using embodiment in 3D virtual environments

Identifying, modelling and documenting business processes usually require the collaboration of many stakeholders that may be spread across companies in inter-organizational settings. While modern process modelling technologies are beginning to provide a number of features to support remote, they lack support for visual cues used in co-located collaboration. In this paper, we examine the importance of visual cues for collaboration tasks in collaborative process modelling. Based on this analysis, we present a prototype 3D virtual world process modelling tool that supports a number of visual cues to facilitate remote collaborative process model creation and validation. We then report on a preliminary analysis of the technology. In conclusion, we proceed to describe the future direction of our research with regards to the theoretical contributions expected from the evaluation of the tool.