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Steel-reinforced concrete-filled steel tubular (SRCFST) columns have been increasingly popular in engineering practice for the columns' excellent seismic and fire performance. Significant design progress guidance has been made through continuous numerical and experimental research in recent years. This paper tested and analysed the residual loading capacity of SRCFST columns under axial loading after experiencing non-uniform ISO-834 standard fire.

The experimental research covered the main parameter of heating conditions, 1-side and 2-side fire, through two specimens. Two specimens were heated and loaded simultaneously in the furnace for 240 min. After cooling, the columns were moved to the hydraulic loading system and loaded to failure to determine the columns' residual capacity.

The experimental results indicated that the non-uniform heating area plays an essential role in the overall performance of SRCFST columns, the increasing heating area of columns results in lower residual loading capacity and stiffness. The SRCFST columns still had a high loading capacity after heating and loading in the fire.

The comparison of experimental data against design results showed that the design method generated a 16% safety margin for S2H4 and a 39% safety margin for S1H4.

Climate change reports from New Zealand claim that climate change will impact some cities such as Auckland from a heating-dominated to a cooling-dominated climate. The benefits and risks of climate change on buildings' thermal performance are still unknown. This paper examines the impacts of climate change on the energy performance of residential buildings in New Zealand and provides insight into changes in trends in energy consumption by quantifying the impacts of climate change.

The present paper used a downscaling method to generate weather data for three locations in New Zealand: Auckland, Wellington and Christchurch. The weather data sets were applied to the energy simulation of a residential case study as a reference building using a validated building energy analysis tool (EnergyPlus).

The result indicated that in Wellington and Christchurch, heating would be the major thermal load of residential buildings, while in Auckland, the main thermal load will change from heating to cooling in future years. The revised R-values for the building code will affect the pattern of dominant heating and cooling demands in buildings in Auckland in the future, while in Wellington and Christchurch, the heating load will be higher than the cooling load.

The findings of this study gave a broader insight into the risks and opportunities of climate change for the thermal performance of buildings. The results established the significance of considering climate change in energy performance analysis to inform the appropriate building codes for the design of residential buildings to avoid future costly changes to buildings.

At the beginning of the Corona Virus Disease 2019 (COVID-19) pandemic, a digitalized construction environments surfaced in the heating, ventilation and air conditioning (HVAC) systems in the form of a modern delivery system called demand controlled ventilation (DCV). Demand controlled ventilation has the potential to solve the building ventilation's biggest problem of managing indoor air quality (IAQ) for controlling COVID-19 transmission in indoor environments. However, the improper evaluation and information management of infection prevention on dense crowd activities such as measurement errors and volatile organic compound (VOC) generation failure rates, is fragmented so the aim of this research is to integrate this and explore potentials with machine learning algorithms (MLAs).

The method used is a thorough systematic literature review (SLR) approach. The results of this research consist of a detailed description of the DCV system and digitalized construction process of its IAQ elements.

The discussion revealed that DCV has a potential for being further integrated by perceiving it as a MLAs and hereby enabling the management of IAQ level from the perspective of health risk function mechanism (i.e. VOC and CO₂) for maintaining a comfortable thermal environment and save energy of public and private buildings (PPBs). The appropriate MLA can also be selected in different occupancy patterns for seasonal variations, ventilation behavior, building type and locations, as well as current indoor air pollution control strategies. Furthermore, the conceptual framework showed that MLA application such as algorithm design/Model Predictive Control (MPC) integration can alleviate the high spread limitation of COVID-19 in the indoor environment.

Finally, the research concludes that a large unexploited potential within integration and innovation is recognized in the DCV system and MLAs which can be improved to optimize level of IAQ from the perspective of health throughout the building sector DCV process systems. The requirements of CO₂ based DCV along with VOC concentrations monitoring practice should be taken into consideration through further research and experience with adaption and implementation from the ventilation control initial stage of the DCV process.

This study has assessed the thermal performance of locally fabricated bio-based building envelopes made of coconut and corn husk composite bricks to reduce building wall heat transmission load and energy consumption towards green building adaptation.

Samples of coconut fiber (coir) and corn husk fiber bricks were fabricated and tested for their thermophysical properties using the Transient Plane Source (TPS) 2500s instrument. A simulation was conducted using Dynamic Energy Response of Building - Lunds Tekniska Hogskola (DEROB-LTH) to determine indoor temperature variation over 24 h. The time lag and decrement factor, two important parameters in evaluating building envelopes, were also determined.

The time lag of the bio-based composite building envelope was found to be in the range of 4.2–4.6 h for 100 mm thickness block and 10.64–11.5 h for 200 mm thickness block. The decrement factor was also determined to be in the range of 0.87–0.88. The bio-based composite building envelopes were able to maintain the indoor temperature of the model from 25.4 to 27.4 °C, providing a closely stable indoor thermal comfort despite varying outdoor temperatures. The temperature variation in 24 h, was very stable for about 8 h before a degree increment, providing a comfortable indoor temperature for occupants and the need not to rely on air conditions and other mechanical forms of cooling. Potential energy savings also peaked at 529.14 kWh per year.

The findings of this study present opportunities to building developers and engineers in terms of selecting vernacular materials for building envelopes towards green building adaptation, energy savings, reduced construction costs and job creation.

This study presents for the first time, time lag and decrement factor for bio-based composite building envelopes for green building adaptation in hot climates, as found in Ghana.

This paper aims to present the application of a tailored systematic engineering design procedure to the concept design of a small production plant for compostable packaging made by straw fibres and bioplastic. In particular, the obtained boxes are intended to be used for wine bottles.

A systematic procedure has been adopted, which underpins on a comprehensive analysis of the design requirements and the function modelling of the process. By considering well-known models of the engineering design process, the work focuses on the early design stages that precede the embodiment design of the whole components of the plant.

The followed design approach allowed to preliminarily evaluate different alternatives of the process from a functional point of view, thus allowing to identify the preferred conceptual process solution. Based on the identified functional sequence, a first evaluation of the potential productivity and the required human resources has been performed.

The procedure shown in this work has been applied only for the considered case of compostable packaging, and other applications are needed to optimize it. Nevertheless, the adopted systematic approach can be adapted for any context where it is necessary to conceive a new production plant for artefacts made by innovative materials.

The work presented in this paper represents one of the few practical examples available in the literature where systematic conceptual design procedures are presented. More specifically, to the best of the authors’ knowledge, this is the very first application of systematic design methods to compostable packaging production.

The purpose of this study is to achieve lower and lower temperature as infrared sensors works faster and better used for space application. For getting good quality images from space, the infrared sensors are need to keep in cryogenic temperature. Cooling to cryogenic temperatures is necessary for space-borne sensors used for space applications. Infrared sensors work faster or better at lower temperatures. It is the need for time to achieve lower and lower temperatures.

This study presents the investigation of the critical Stirling cryocooler parameters that influence the cold end temperature. In the paper, the design approach, the dimensions gained through thermal analysis, experimental procedure and testing results are discussed.

The effect of parameters such as multilayer insulation, helium gas charging pressure, compressor input voltage and cooling load was investigated. The performance of gold-plated and aluminized multilayer insulation is checked. The tests were done with multilayer insulation covering inside and outside the Perspex cover.

By using aluminized multilayer insulation inside and outside the Perspex cover, the improvement of 16 K in cool-down temperature was achieved. The cryocooler is charged with helium gas. The pressure varies between 14 and 18 bar. The optimum cooling is obtained for 17 bar gas pressure. The piston stroke increased as the compressor voltage increased, resulting in total helium gas compression. The optimum cool-down temperature was attained at 85 V.

The cryocooler is designed to achieve the cool-down temperature of 2 W cooling load at 100 K. The lowest cool-down temperature recorded was 105 K at a 2 W cooling load. Multilayer insulation is the major item that keeps the thermal radiation from the sun from reaching the copper tip.

As data centres grow in size and complexity, traditional air-cooling methods are becoming less effective and more expensive. Immersion cooling, where servers are submerged in a dielectric fluid, has emerged as a promising alternative. Ensuring reliable operations in data centre applications requires the development of an effective control framework for immersion cooling systems, which necessitates the prediction of server temperature. While deep learning-based temperature prediction models have shown effectiveness, further enhancement is needed to improve their prediction accuracy. This study aims to develop a temperature prediction model using Long Short-Term Memory (LSTM) Networks based on recursive encoder-decoder architecture.

This paper explores the use of deep learning algorithms to predict the temperature of a heater in a two-phase immersion-cooled system using NOVEC 7100. The performance of recursive-long short-term memory-encoder-decoder (R-LSTM-ED), recursive-convolutional neural network-LSTM (R-CNN-LSTM) and R-LSTM approaches are compared using mean absolute error, root mean square error, mean absolute percentage error and coefficient of determination (R²) as performance metrics. The impact of window size, sampling period and noise within training data on the performance of the model is investigated.

The R-LSTM-ED consistently outperforms the R-LSTM model by 6%, 15.8% and 12.5%, and R-CNN-LSTM model by 4%, 11% and 12.3% in all forecast ranges of 10, 30 and 60 s, respectively, averaged across all the workloads considered in the study. The optimum sampling period based on the study is found to be 2 s and the window size to be 60 s. The performance of the model deteriorates significantly as the noise level reaches 10%.

The proposed models are currently trained on data collected from an experimental setup simulating data centre loads. Future research should seek to extend the applicability of the models by incorporating time series data from immersion-cooled servers.

The proposed multivariate-recursive-prediction models are trained and tested by using real Data Centre workload traces applied to the immersion-cooled system developed in the laboratory.

This study aims to compare and evaluate the application of commonly used machine learning (ML) algorithms used to develop models for assessing energy efficiency of buildings.

This study foremostly combined building energy efficiency ratings from several data sources and used them to create predictive models using a variety of ML methods. Secondly, to test the hypothesis of ensemble techniques, this study designed a hybrid stacking ensemble approach based on the best performing bagging and boosting ensemble methods generated from its predictive analytics.

Based on performance evaluation metrics scores, the extra trees model was shown to be the best predictive model. More importantly, this study demonstrated that the cumulative result of ensemble ML algorithms is usually always better in terms of predicted accuracy than a single method. Finally, it was discovered that stacking is a superior ensemble approach for analysing building energy efficiency than bagging and boosting.

While the proposed contemporary method of analysis is assumed to be applicable in assessing energy efficiency of buildings within the sector, the unique data transformation used in this study may not, as typical of any data driven model, be transferable to the data from other regions other than the UK.

This study aids in the initial selection of appropriate and high-performing ML algorithms for future analysis. This study also assists building managers, residents, government agencies and other stakeholders in better understanding contributing factors and making better decisions about building energy performance. Furthermore, this study will assist the general public in proactively identifying buildings with high energy demands, potentially lowering energy costs by promoting avoidance behaviour and assisting government agencies in making informed decisions about energy tariffs when this novel model is integrated into an energy monitoring system.

This study fills a gap in the lack of a reason for selecting appropriate ML algorithms for assessing building energy efficiency. More importantly, this study demonstrated that the cumulative result of ensemble ML algorithms is usually always better in terms of predicted accuracy than a single method.

The residential buildings sector has a high priority in the climate change adaptation process due to significant CO₂ emissions, high energy consumption and negative environmental impacts. The article investigates how, conversely speaking, the residential buildings will be affected by climate change, and how to improve existing structures and support long-term decisions.

The climate dataset was created using the scenarios determined by the Intergovernmental Panel on Climate Change (IPCC), and this was used in the study. Different building envelope and Heating, Ventilating and Air Conditioning (HVAC) systems scenarios have been developed and simulated. Then, the best scenario was determined with comparative results, and recommendations were developed.

The findings reveal that future temperature-increase will significantly impact buildings' cooling and heating energy use. As the outdoor air temperatures increase due to climate change, the heating loads of the buildings decrease, and the cooling loads increase significantly. While the heating energy consumption of the house was calculated at 170.85 kWh/m² in 2020, this value shall decrease significantly to 115.01 kWh/m² in 2080. On the other hand, the cooling energy doubled between 2020 and 2080 and reached 106.95 kWh/m² from 53.14 kWh/m² measured in 2020.

Single-family houses constitute a significant proportion of the building stock. An in-depth analysis of such a building type is necessary to cope with the devastating consequences of climate change. The study developed and scrutinised energy performance improvement scenarios to define the climate change adaptation process' impact and proper procedure. The study is trying to create a strategy to increase the climate resistance capabilities of buildings and fill the gaps in this regard.

This study focused on identifying critical factors governing the fire response of prestressed hollow-core slabs. The hollow-core slabs used as flooring units can be subjected to elevated temperatures during a fire. The fire response of prestressed hollow-core slabs is required to develop slabs with greater fire endurance. The present study aims to determine the extent to which the hollow-core slab can sustain load during a fire without undergoing progressive collapse under extreme fire and heating scenarios.

A finite element model was generated to predict the fire response of prestressed hollow core slabs under elevated temperatures. The accuracy of the model was predicted by examining thermal and structural responses through coupled temperature displacement analysis. A sensitivity analysis was performed to study the effects of concrete properties on prediction of system response. A parametric study was conducted by varying the thickness of the slab, fire and heating scenarios.

Thermal conductivity and specific heat of concrete were determined as sensitive parameters. The thickness of the slab was identified as a critical factor at a higher load level. Asymmetric heating of the slab resulted in higher fire resistance compared with symmetric heating.

This is the first study focused on studying the effect of modeling uncertainties on the system response by sensitivity analysis under elevated temperatures. The developed model with a parametric study helps in identifying critical factors for design purposes.