Inspection of building work, particularly the pipe systems, is a labor-intensive manual process prone to human errors and inefficiencies in detecting spatial conflicts and verifying geometric characteristics. Although technologies such as laser scanning can digitize the piping system with high accuracy, they are not suitable for practical inspection tasks due to high computational demands and the need for manual verifications. In contrast, red-green-blue-depth (RGB-D) sensors provide color and depth data in the form of images which can be processed more efficiently. These sensors are available on mixed reality (MR) devices, offering the additional capability of mixed reality visualization and comparison of virtual models such as a Building Information Model (BIM) with the real world. We propose an automated approach for geometric pipe inspection using BIM and RGB-D data provided by an MR device. The method employs the Segment Anything Model (SAM) to automatically segment pipes from RGB image, and utilizes depth data to reconstruct three-dimensional (3D) representations of the pipe centerline and estimate the diameter. By comparing 3D centerlines with their corresponding counterparts in aligned BIM, the method detects discrepancies in pipe dimensions and positions, reducing manual verification and human error. The proposed method was evaluated using one synthetic and two real data sets, demonstrating its ability to perform geometric inspections at various scan ranges, pipe sizes, and spacing. The results identified the optimal scan range based on the pipe network complexity, demonstrating that our method enhances accuracy and efficiency while reducing computational burden, making it a promising tool for automated pipe inspections in construction.

This study aims to enhance building energy efficiency and resilience during retrofitting stages to address future climate change impacts by examining a representative commercial building model across various cities. To this end, comprehensive retrofit parameters for the building envelope, including passive, active, and renewable design elements, were integrated into a simulation-based optimisation framework to minimise building energy use, enhance thermal comfort, and reduce life-cycle costs. The optimisation process revealed the significant influence of varying climate scenarios on building performance and the effectiveness of different retrofit strategies. Results demonstrate that renewable energy generation has the potential to meet cooling, heating, and lighting demands under diverse conditions. Seasonal and temperate climates show potential for retrofitted buildings to meet net-zero energy buildings (NZEB) by 2050, with retrofit costs maintaining comparable to current levels. However, due to global temperature warming, the risk of overheating is anticipated to increase significantly across all cities, from an average of 4.91 % under current conditions to 23.98 % in future scenarios, posing substantial challenges in managing heat-related stress for occupants. Moreover, by employing the heuristic multi-criteria decision-making (MCDM) framework to identify optimal retrofit solutions for different regions, the findings underscore the limitations of current climate-based design approaches and recommend tailored strategies that consider both interactions between retrofit parameters and local climate conditions to effectively achieve zero-energy targets in the future.

Accurate detection and measurement of building elements are essential for efficient automated inspection and quality assessment in construction. This study evaluates the effectiveness of the Segment Anything Model (SAM) for pipe segmentation using a Mixed Reality-based dataset and introduces an automated method for pipe 3D centreline reconstruction and diameter estimation. The impact of the input point prompt distribution and number on segmentation accuracy is analyzed, identifying optimal configurations for improved performance. Using depth data and pose information from the MR device, the proposed approach reconstructs the 3D centreline and estimates pipe diameters with high reliability. The method is evaluated in a real experimental pipe network. The results indicate that the use of five-point prompts in a uniform distribution achieves approximately 90% precision and recall for pipe segmentation, with median position and diameter errors of 33 mm and 10 mm, respectively. The findings highlight the ability of the MR system to achieve accurate pipe positioning and diameter estimation, particularly in pipe networks with moderate complexity and fewer thin pipes, where segmentation and measurement challenges are minimized.

The objective of this study is to investigate the structural behaviour of a novel adhesive-free timber-steel composite (AFTSC) system as a high-performance floor panel for sustainable mid- and high-rise construction. Local plantation Eucalyptus globulus timber boards and laser cut mild-steel were used to fabricate the test specimens. Four-point bending tests were carried out to experimentally record the force, displacement, and failure mechanism of the panels. An analytical model informed by material grading tests was generated and compared against the experimental results. The novel AFTSC panels maintained near full composite action past 40 % of ultimate load and consistently exhibited substantial ductile behaviour. In addition, the effective ultimate bending capacity of the timber components was found to increase by up to 20 % when included in the AFTSC panels. This demonstrates the high-performance credentials of the novel AFTSC system along with the potential to valorise plantation hardwoods such as Eucalyptus globulus.

The construction industry faces challenges stemming from labor-intensive processes that are prone to errors and inefficiencies. Recent advancements in construction automation, particularly in building work inspection, have leveraged technologies such as mixed reality (MR) visualization. MR enables visualization of a virtual model, such as a building information model (BIM), aligned with the real environment. However, this requires accurate estimation of the MR camera pose (position and orientation) within the BIM and the real environment. The current pose estimation methods, such as simultaneous localization and mapping (SLAM), suffer from drift of the estimated pose resulting in misalignment in MR visualization, which increases with time. This paper presents an innovative method to refine the estimated camera poses for precise BIM alignment with MR visualization. By reconstructing a 3D wireframe model of the building from the images captured by the MR and matching it with the BIM, our method refine the camera poses, effectively eliminating the drift. Extensive evaluation on virtual and real data sets demonstrates high reliability and accuracy of the approach, achieved within an efficient processing time. These results emphasize the practical advantages of our method in large-scale building work inspection tasks.

This study evaluates climate-resilient retrofit strategies for existing Australian homes, using projected 2050s weather data under future moderate (RCP4.5) and high-emission (RCP8.5) scenarios. To this end, a brute-force parametric analysis was employed, simulating 120,960 unique retrofit combinations (envelope insulation, exterior colour, fenestration upgrade, and the application of ceiling fans) for a representative building archetype. The assessment covered annual heating/cooling energy consumption and thermal resilience, a home’s ability to maintain safe conditions during extreme events, across four distinct climate zones (Brisbane, Canberra, Melbourne, and Sydney). Results show a climate-driven shift in retrofit priorities and achievable benchmarks. In warmer climates, rising cooling loads dominate: Brisbane’s median star rating for the entire design population falls from 5.6 (present) to 2.9 (2050s-RCP8.5), and even the most optimised design fails to meet Australia’s national 7-star energy performance benchmark by mid-century. Conversely, in cooler climates (Canberra, Melbourne), reduced winter demand initially improves annual performance (>30 % heating reduction), before it reaches a turning point where future cooling loads rise and negate these benefits. Thus, the configuration of an optimised retrofit design is not static but must evolve in response to climate change. While a highly insulated and airtight building envelope improves annual energy performance, it can pose severe overheating risks during power outages. However, pairing this high-performance envelope with adaptive strategies (natural ventilation) proves transformative, significantly enhancing passive survivability during future heatwaves. Ultimately, a holistic, systems-based approach that integrates passive measures and adaptive strategies is essential to ensure that retrofitted homes are not only energy-efficient year-round but also safe and climate-resilient in the face of extreme future conditions.

Large class sizes are frequently necessitated by financial, resource, and logistical constraints. Teaching large classes presents pedagogical challenges impacting instructional quality and student learning, with student engagement emerging as a critical issue. This chapter explores instructional approaches to mitigate potential negative effects on engagement and learning, based on a rapid literature review. Key approaches identified include instructional strategies, active learning, collaborative learning, technology integration, flipped classrooms, peer instruction, learning assistants, and frequent formative assessments. Results highlight active learning and technology-enhanced strategies as strongly correlated with improved student engagement and academic outcomes, particularly when effectively integrated. The practical implementation challenges are resource demands, and increased instructor preparation. The chapter concludes with recommendations for prioritising structured collaborative activities, interactive technologies, and targeted instructor training to enhance engagement.

High-performance engineered wood products (EWPs) and composite mass timber products (CMTPs) are being employed more frequently in residential projects with increasing interest in more sustainable systems that achieve long-spans. The relative performance of these systems is not readily apparent, with individual manufacturers offering proprietary products assembled from specific timber resources. In addition, the suitability of producing these long-span systems using plantation hardwoods is currently unknown. This research investigated the comparative performance of four typical EWPs and CMTPs; 1. solid slab, 2. thin-walled cassette, 3. T-sections, and 4. slab on beam. Key performance metrics of depth, mass, stiffness, vibration response, fire performance and global warming potential were assessed. The mechanical performance of two high-strength plantation hardwood varieties (Eucalyptus nitens and Eucalyptus globulus) were determined experimentally and subsequently used to re-calculate performance criteria for the previously assessed typologies. Substantial improvements over the typical softwood varieties were identified, particularly in structural efficiency, global warming potential and fire performance. This highlights the potential for value adding to plantation hardwoods by using them in high-performance long-span engineered floor products.

By-products (wastes or residues) of renewable materials have the potential to be manufactured into higher value fibre insulation products for the Australian market. Currently, such products have been imported for servicing the Australian market. This presents a potential opportunity to divert considerable quantities of waste from landfill and produce a high performance, locally made, low carbon, natural fibre insulation product for the Australian domestic and commercial building industry. This article assesses the hemp-based bulk insulations available in the Australian market.

Sustainably sourced engineered wood products (EWP) such as cross-laminated timber (CLT) floor panels are viable alternatives to conventional concrete and steel, however their inherent natural variability increases perceived risks and limits wide-spread adoption. The reliability index ((3) is the metric used by design standards to communicate allowable structural risk. Although not included directly in any structural capacity equations, it governs all the partial resistance factors and load amplification factors that inform those design code calculations. CLT floors have significant partial resistance factors due to the natural variability of the material that effectively penalise the calculated design strength of those products. By exploring their reliability there is the potential to allow these products to be designed more efficiently, leading to increased competitiveness among traditional materials and less waste. A novel statistical capacity prediction model that goes beyond a simple averaged-value strength assumption to include consideration of the variability between individual boards within a panel was validated against experimental data. A CLT database of 237 out-of-plane bending test results was collected for the first time to inform an appropriate model error factor. Monte Carlo simulation (MCS) approaches were adopted to predict the reliability levels of CLT panels by defining the material, geometrical and loading parameters as random variables and assigning each a mean, coefficient of variation and appropriate distribution. The target and calculated reliability levels were compared across both Eurocode 5 and the North American codes ANSI/APA PRG 320 and CSA O86 as the preeminent CLT design codes internationally. This study found that the calculated reliability levels met or exceeded both codes in most scenarios. The impacts of width, load ratio, material strength distribution, and CLT layups on the expected failure probabilities were explored with sensitivity analyses. The MCS framework was also applied to the calibration of an appropriate resistance factor for CLT design to inform the potential creation of an Australian CLT design code. A resistance factor of 0.85 was calibrated which falls between the equivalent European and North American code resistance factors.

Mixed Reality (MR) global localization involves precisely tracking the device’s position and orientation within a digital representation, such as Building Information Model (BIM). Existing model-based MR global localization approaches have difficulty addressing environmental changes between the BIM and real-world, particularly in dynamic construction sites. Additionally, a significant challenge in MR systems arises from localization drift, where the gradual accumulation of positional errors over time can lead to inaccuracies in determining the device’s position and orientation within the virtual model. We develop a method that extracts structural elements of the building, referred to as a wireframe, which are less likely to change due to their inherent permanence. The extraction of these features is computationally inexpensive enough that can be performed on MR device, ensuring a reliable and continuous global localization over time, thereby overcoming issues associated with localization drift. The method incorporates a deep Convolutional Neural Network (CNN) to extract the 2D wireframes from images. The reconstruction of 3D wireframes is achieved by utilizing the extracted 2D wireframe along with their depth information. The simplified 3D wireframe is subsequently aligned with the BIM. Real-world experiments demonstrate the method’s effectiveness in 3D wireframe extraction and alignment with the BIM, successfully mitigating drift issues by 4cm in prolonged corridor scans.

Mixed reality (MR) visualization, which involves displaying virtual content mixed with a view of the real-world, has recently been explored for the inspection of building work. This process requires aligning a building information model (BIM) with physical construction sites. However, achieving accurate alignment poses challenges in MR applications. Marker-based methods, although more reliable, have limitations in complex environments where pre-installed markers are inaccessible in certain locations and their installation and maintenance are costly and labor-intensive. This paper introduces a new offset-based marker placement method to align BIM models in MR. This approach involves placing markers offset from the corners determined by the geometric features of the building. The markers are placed during the registration process, without the need for a pre-installation stage. The effectiveness of the method is demonstrated on a site with pipes of different sizes, showing that the offset-based markers provide accurate BIM alignment, comparable to conventional marker-based methods.

This article proposes a new correlation to estimate the convective heat transfer coefficient inside the evaporation chamber (enclosed space over the basin) of a single slope solar still with a low humidity level. The Rayleigh number, aspect ratio (width to the average height of chamber) and the relative humidity of the chamber are the main variables in proposed correlation. Existing models use only Rayleigh number to characterise the convection and neglect the effect of vapour pressure. Therefore, they fail to accurately predict the convection in higher water temperatures or in unsaturated humidity levels. In this work, the natural convection Nusselt number is generated based on a theoretical approach and the constant values in the correlation are calculated using the multiple linear regression analysis on experimental data obtained in this work. The transient thermal model of solar still using the new correlation predicts daily water yield within 7.6% of experiments under different humidity levels and aspect ratios. Higher performance is obtained in larger aspect ratio and lower humidity inside solar still. The modified solar still with a relative humidity of 62% generated 3.49 kg m- 2 d-1 of water during summer in Melbourne, demonstrating an 18% increase in water yield.

Emerging remote inspection technologies are addressing the challenges of conventional building work inspections by reducing time, cost, and safety risks for inspectors, while also improving overall effectiveness. These technologies involve data collection, information extraction, and compliance checks, highlighting the need for a comprehensive understanding to enable the adoption of more efficient and secure inspection methods. This paper comprehensively reviews cutting-edge technologies, sensors, platforms, and data processing methods applicable to remote inspections across diverse building elements. It evaluates each technology’s suitability for specific building components based on key criteria, including inspected item properties and potential for assistant-based or remote inspection, with emphasis on video-based methods. Furthermore, the analysis considers technology capabilities for automation, real-time functionality, and policy implications for implementation.

This article presents a transient model of a single slope single basin passive solar still. The model considers the aspect ratio of the evaporation chamber, the thermal inertia of components and the bulk water, and the salinity level. An experimental rig was designed, fabricated, and tested for various aspect ratios of the evaporation chamber. The transmissivity of the glass cover was measured during experiments and applied as an input to the model. It was found that the transmissivity is reduced by up to 21% over the day. The developed model was validated with the experimental data. The simulations and experiments were conducted in Melbourne, Australia during a summer. The model predicted outputs agree with the measured data. The predicted performance of the solar still is about 5% more than that of measured. A sensitivity analysis was conducted on the climate pa-rameters. The simulated and measured water yield and the thermal efficiency of the solar still under a range of water depths and chamber aspect ratios are reported. A solar still with a larger aspect ratio of the evaporation chamber (shorter specific height) generates more potable water. However, the effect of lower depth of bulk water on the water yield of the system is more significant. The fabricated passive solar still generated 2.52 kg m(-2) d(-1) of potable water with a thermal efficiency of 19.2% for a water depth of 0.01 m and the salinity of 3% by wt. Based on the results of the simulations, the study provides recommendations to achieve maximum performance for conventional passive solar stills.

In this article, a novel floating salt rejecting solar still with a low vacuum condition on the evaporation chamber is developed and the performance is experimentally investigated. The new design adopts solar heat localization for interfacial evaporation and capillary water circulation to improve the evaporation rate and prevent the basin surface from residual salt accumulation. The basin is made in tubular structure composed of multi layers of porous foam and hydrophilic cellulose fabric for improved capillary water supply. The solar still consists of an external condensing coils coupled with the basin structure. It completely submerges into the water while the solar still is floating in the saline water reservoir (e.g. oceans). This enables the natural cooling of the condensing coils which increases the condensation rate. A low-cost hemispherical clear acrylic cover is used to capture the solar radiation from all directions on the basin. The system performance was examined under different scenarios. The system was found to generate distilled water at a daily rate of 4.3 L m- 2 d-1 with the distillation efficiency of 35.6% during summer in Melbourne, Australia. The life cycle cost per litre of drinking water generated by the solar still is calculated at 4.7US xa2; L-1 which is substantially lower than conventional solar stills. This system is expected to have a lower maintenance cost as it does not require as much periodic cleaning. The new system developed is a feasible alternative to address the water security challenge for water-stressed communities at remote areas or disaster-stricken areas with no access to an energy infrastructure.