Synthesis of a Green Nano-Silica Material Using Beneficiated Waste Dunites and Its Application in Concrete

Nano-silica, one of the substances boosting the field of nanomaterials, can be produced by dissolving olivine in acid. The dissolution of olivine is a convenient alternative route to the existing methods of nano-silica production (neutralization of sodium silicate and flame hydrolysis) because the olivine dissolution is a low temperature process making this method cheaper and greener. Furthermore, this process can use waste olivine materials for the production of nano-silica. The produced nano-silica has a specific surface area between 100 and 400 m2/g;a primary particle size between 10 and 25 nm, which is agglomerated in clusters;and an impurity content below 5 wt.%. In addition, olivine nano-silica can be classified as a pozzolanic material with an activity index of 101%. The optimum replacement level of olivine nano-silica in conventional vibrated concrete is around 5% by volume resulting in: 1) a compressive strength increase of 20%;2) a CO2 emission reduction of 3%. Therefore, the use of the olivine nano-silica in CVC does not only improve the compressive strength but also reduce the CO2 emissions.

Systematic review of the efficacy of data-driven urban building energy models during extreme heat in cities:Current trends and future outlook

Energy demand fluctuations due to low probability high impact(LPHI)micro-climatic events such as urban heat island effect(UHI)and heatwaves,pose significant challenges for urban infrastructure,particularly within urban built-clusters.Mapping short term load forecasting(STLF)of buildings in urban micro-climatic setting(UMS)is obscured by the complex interplay of surrounding morphology,micro-climate and inter-building energy dynamics.Conventional urban building energy modelling(UBEM)approaches to provide quantitative insights about building energy consumption often neglect the synergistic impacts of micro-climate and urban morphology in short temporal scale.Reduced order modelling,unavailability of rich urban datasets such as building key performance indicators for building archetypes-characterization,limit the inter-building energy dynamics consideration into UBEMs.In addition,mismatch of resolutions of spatio-temporal datasets(meso to micro scale transition),LPHI events extent prediction around UMS as well as its accurate quantitative inclusion in UBEM input organization step pose another degree of limitations.This review aims to direct attention towards an integrated-UBEM(i-UBEM)framework to capture the building load fluctuation over multi-scale spatio–temporal scenario.It highlights usage of emerging data-driven hybrid approaches,after systematically analysing developments and limitations of recent physical,data-driven artificial intelligence and machine learning(AI-ML)based modelling approaches.It also discusses the potential integration of google earth engine(GEE)-cloud computing platform in UBEM input organization step to(i)map the land surface temperature(LST)data(quantitative attribute implying LPHI event occurrence),(ii)manage and pre-process high-resolution spatio-temporal UBEM input-datasets.Further the potential of digital twin,central structed data models to integrate along UBEM workflow to reduce uncertainties related to building archetype characterizations is explored.It has also found that a trade-off between high-fidelity baseline simulation models and computationally efficient platform support or co-simulation platform integration is essential to capture LPHI induced inter-building energy dynamics.

Evaluating different levels of information on the calibration of building energy simulation models

A poorly calibrated model undermines confidence in the effectiveness of building energy simulation, impeding the widespread application of advanced energy conservation measures (ECMs). Striking a balance between information-gathering efforts and achieving sufficient model credibility is crucial but often obscured by ambiguities. To address this gap, we model and calibrate a test bed with different levels of information (LOI). Beginning with an initial model based on building geometry (LOI 1), we progressively introduce additional information, including nameplate information (LOI 2), envelope conductivity (LOI 3), zone infiltration rate (LOI 4), AHU fan power (LOI 5), and HVAC data (LOI 6). The models are evaluated for accuracy, consistency, and the robustness of their predictions. Our results indicate that adding more information for calibration leads to improved data fit. However, this improvement is not uniform across all observed outputs due to identifiability issues. Furthermore, for energy-saving analysis, adding more information can significantly affect the projected energy savings by up to two times. Nevertheless, for ECM ranking, models that did not meet ASHRAE 14 accuracy thresholds can yield correct retrofit decisions. These findings underscore equifinality in modeling complex building systems. Clearly, predictive accuracy is not synonymous with model credibility. Therefore, to balance efforts in information-gathering and model reliability, it is crucial to (1) determine the minimum level of information required for calibration compatible with its intended purpose and (2) calibrate models with information closely linked to all outputs of interest, particularly when simultaneous accuracy for multiple outputs is necessary.

Leveraging Geospatial Technologies for Resource Optimization in Livestock Management

Geospatial technologies can be leveraged to optimize the available resources for better productivity and sustainability. The resources can be human, software and hardware equipment and their effective management can enhance operational efficiency through better and informed decision making. This review article examines the application of geospatial technologies, including GPS, GIS, and remote sensing, for optimizing resource utilization in livestock management. It compares these technologies to traditional livestock management practices and highlights their potential to improve animal tracking, feed intake monitoring, disease monitoring, pasture selection, and rangeland management. Previously, animal management practices were labor-intensive, time-consuming, and required more precision for optimal animal health and productivity. Digital technologies, including Artificial Intelligence (AI) and Machine Learning (ML) have transformed the livestock sector through precision livestock management. However, major challenges such as high cost, availability and accessibility to these technologies have deterred their implementation. To fully realize the benefits and tremendous contribution of these digital technologies and to address the challenges associated with their widespread adoption, the review proposes a collaborative approach between different stakeholders in the livestock sector including livestock farmers, researchers, veterinarians, industry professionals, technology developers, the private sector, financial institutions and government to share knowledge and expertise. The collaboration would facilitate the integration of various strategies to ensure the effective and wide adoption of digital technologies in livestock management by supporting the development of user-friendly and accessible tools tailored to specific livestock management and production systems.

Titania-Silica Composites: A Review on the Photocatalytic Activity and Synthesis Methods

The photocatalyic activity of titania is a very promising mechanism that has many possible applications like purification of air and water [1]-[4]. To make it even more attractive, titania can be combined with silica to increase the photocatalytic efficiency and durability of the photocatalytic material, while lowering the production costs [1]. In this article, relevant literature is reviewed to obtain an overview about the chemistry and physics behind some of the different parameters that lead to cost-effective photocatalytic titania-silica composites. The first part of this review deals with the mechanisms involved in the photocatalytic activity, then the chemistry behind certain methods for the synthesis of the titania-silica composites is discussed, and in the last and third part of this review, the influence of silica supports on titania is discussed. These three sections represent three different fields of research that are combined in this review to obtain better insights on the photocatalytic titania-silica composites. While many research subjects in these fields have been well known for some time now, some subjects are only more recently resolved and some subjects are still under discussion (e.g. the cause for the increased hydrophilic surface of titania after illumination). This article aims to review the most important literature to give an overview of the current situation of the fundamentals of photocatalysis and synthesis of the cost-effective photocatalyic composites. It is found that the most cost-effective photocatalytic titania-silica composites are the ones that have a thin anatase layer coated on silica with a large specific surface area, and are prepared with the precipitation or sol-gel methods.

Evaluation of Inverse Modeling Techniques for Pinpointing Water Leakages at Building Constructions

The location and nature of the moisture leakages are sometimes difficult to detect. Moreover, the relation between observed inside surface moisture patterns and where the moisture enters the construction is often not clear. The objective of this paper is to investigate inverse modeling techniques as a tool for the detection of moisture leakage locations in building constructions from inside surface moisture patterns. It is concluded that although the presented methodology is promising, more research is needed to confirm its usability.

Analysing Delay Impact from Potential Risk Factors on Project Delivery of Oil and Gas Pipeline:A Case Study in IRAQ

The aim of this paper is to present the design and specifications of an integrated Delay Analysis Framework(DAF),which could be used to quantify the delay caused by the Risk Factors(RFs)in Oil and Gas Pipelines(OGPs)projects in a simple and systematic way.The main inputs of the DAF are(i)the potential list of RFs in the projects and their impact levels on the projects and the estimated maximum and minimum duration of each task.Monte Carlo Simulation integrated within@Risk simulator was the key process algorithm that used to quantify the impact of delay caused by the associated RFs.The key output of the DAF is the amount of potential delay caused by RFs in the OGP project.The functionalities of the developed DAF were evaluated using a case study of newly developed OGP project,in the south of Iraq.It is found that the case study project might have delayed by 45 days if neglected the consideration of the RFs associated with the project at the construction stage.The paper concludes that identifying the associated RFs and analysing the potential delay in advance will help in reducing the construction delay and improving the effectiveness of the project delivery by taking suitable risk mitigation measures.

Investigating the use cases of a novel heat battery in Dutch residential buildings

Recent advances in thermochemical storage technology have introduced a novel closed-loop thermal energy storage(TES)system,known as the heat battery(HB),which is believed to have great potential for aiding the energy transition in the built environment because of its higher energy density and neglectable storage loss compared to conventional TES systems.In order to investigate the potential use cases of the HB and provide practical feedback for its further development,this research employs a simulation-based approach to analyze its influence on building performance in various use cases within Dutch residential buildings.Stakeholders including the homeowner,distribution system operator,and district heating system operator are identified,and a preliminary list of use cases is defined based on relevant literature and input from the HB developer.The simulation approach is conducted to predict key performance indicators for each stakeholder.The Kruskal-Wallis test was employed to sort and scrutinize the simulation outcomes and discern the significance of each use case element.The findings demonstrated that the HB holds the potential to diminish both the operational energy cost by up to 30%for the homeowners and the peak heating load transmitted from the building to the district heating system.

Generative pre-trained transformers(GPT)-based automated data mining for building energy management:Advantages,limitations and the future

Advanced data mining methods have shown a promising capacity in building energy management.However,in the past decade,such methods are rarely applied in practice,since they highly rely on users to customize solutions according to the characteristics of target building energy systems.Hence,the major barrier is that the practical applications of such methods remain laborious.It is necessary to enable computers to have the human-like ability to solve data mining tasks.Generative pre-trained transformers(GPT)might be capable of addressing this issue,as some GPT models such as GPT-3.5 and GPT-4 have shown powerful abilities on interaction with humans,code generation,and inference with common sense and domain knowledge.This study explores the potential of the most advanced GPT model(GPT-4)in three data mining scenarios of building energy management,i.e.,energy load prediction,fault diagnosis,and anomaly detection.A performance evaluation framework is proposed to verify the capabilities of GPT-4 on generating energy load prediction codes,diagnosing device faults,and detecting abnormal system operation patterns.It is demonstrated that GPT-4 can automatically solve most of the data mining tasks in this domain,which overcomes the barrier of practical applications of data mining methods in this domain.In the exploration of GPT-4,its advantages and limitations are also discussed comprehensively for revealing future research directions in this domain.

Demystifying energy savings from dynamic temperature setpoints under weather and occupancy variability

Building temperature setpoints affect both HVAC energy consumption and occupant comfort.To reduce HVAC energy usage,researchers often investigate how system operations can be optimized under weather and occupancy variability subject to a fixed setpoint that minimizes any possible discomfort.While previous research has explored the selection of dynamic setpoints to minimize HVAC energy consumption based on outdoor temperature,they have often neglected the impact of varying occupancy rates on the setpoints.This paper aims to demystify energy savings derived from fixed and dynamic temperature setpoints under weather and occupancy variability and explores the additional energy savings that can be achieved through dynamic temperature setpoints.An exhaustive HVAC zone temperature setpoint optimizer was developed to determine dynamic setpoints with respect to weather and occupancy(i.e.,setpoints that minimize HVAC energy consumption at different occupancy rates based on outdoor weather).U.S.DOE reference building energy models for small,medium,and large office buildings were simulated at 17 climate zones,4 occupancy rates(25%,50%,75%,100%)and 7 setpoints(19.5℃to 25.5℃at 1℃interval).It was found that,both fixed and dynamic setpoints benefit from the energy reduction of approximately 2-4%from the lower heat generated by the occupants at lower occupancy rates.However,at outdoor temperatures between 5℃and 32℃where occupant heat loads can swing the building between heating,free-running,and cooling modes,dynamic setpoints yield additional 2-10%energy savings,compared to fixed setpoints.

常温及火灾后自应力轻骨料钢管混凝土柱轴压力学性能试验研究

自应力钢管陶粒混凝土不仅具有轻质、高强的特点,并可弥补轻集料混凝土因弹性模量较小而导致钢管约束不足的缺陷。本文通过对4组12根受火后的钢管自应力陶粒混凝土短柱及9根未受火钢管混凝土短柱受力性能的对比试验,分析了不同参数的钢管自应力陶粒混凝土短柱火灾后轴压承载力和破坏形态。重点讨论了试件的自应力大小(膨胀剂掺量)、含钢率及受火条件等因素对钢管自应力陶粒混凝土短柱火灾后轴压承载力及相关力学性能的影响。结果表明,自应力大小对钢管陶粒混凝土短柱火灾后轴压承载力的影响与试件含钢率有关,且初始自应力对含钢率相对较低试件的火灾后轴压性能的改善效果更加显著;试件含钢率越高,轴压力作用下的火灾后钢管自应力陶粒混凝土短柱的延性则越好。膨胀剂掺量为51 kg/m3(P2型)的试件,无论受火温度为700℃还是900℃,其火灾后的线弹性刚度受试件含钢率影响的规律基本一致。

Chiller faults detection and diagnosis with sensor network and adaptive 1D CNN

Computer-empowered detection of possible faults for Heating,Ventilation and Air-Conditioning(HVAC)subsystems,e.g.,chillers,is one of the most important applications in Artificial Intelligence(AI)integrated Internet of Things(IoT).The cyber-physical system greatly enhances the safety and security of the working facilities,reducing time,saving energy and protecting humans’health.Under the current trends of smart building design and energy management optimization,Automated Fault Detection and Diagnosis(AFDD)of chillers integrated with IoT is highly demanded.Recent studies show that standard machine learning techniques,such as Principal Component Analysis(PCA),Support Vector Machine(SVM)and tree-structure-based algorithms,are useful in capturing various chiller faults with high accuracy rates.With the fast development of deep learning technology,Convolutional Neural Networks(CNNs)have been widely and successfully applied to various fields.However,for chiller AFDD,few existing works are adopting CNN and its extensions in the feature extraction and classification processes.In this study,we propose to perform chiller FDD using a CNN-based approach.The proposed approach has two distinct advantages over existing machine learning-based chiller AFDD methods.First,the CNN-based approach does not require the feature selection/extraction process.Since CNN is reputable with its feature extraction capability,the feature extraction and classification processes are merged,leading to a more neat AFDD framework compared to traditional approaches.Second,the classification accuracy is significantly improved compared to traditional methods using the CNN-based approach.

Transforming Strijp S： From Philips＇ Industrial Site to New Residential and Creative Area

In the contemporary city, the transformation of abandoned industrial landscapes offers great potential for experimentation and in creating new urban environments. Industrial heritage plays an essential role in this respect and it goes beyond its strong cultural and symbolic value. In fact, industrial-cultural heritage is able to re-enact people＇s capability to maintain the culture of the past while looking forward with appropriate and innovative design solutions. This paper focuses on the design of regeneration of the former Philips＇ electronic industrial area named Strijp S in Eindhoven, the Netherlands. The master plan is pioneer in experimental rules on development areas due to the Crisis and Recovery Act （2010）, a new Dutch law in response to development and economical crisis. The authors identify four criteria of analysis in order to show the complexity of this urban transformation and assess the results of this work-in-progress transformation. Moreover, it will discuss the challenges for industrial architecture/heritage when incorporating different objectives of redevelopment and to which extent the local dynamics are linked to the design of reuse. As conclusion, the paper will debate on the comprehensive urban framework, which puts forward sustainable design criteria, sets out principles in temporary and long-term reuse of the old buildings, and on their implication in terms of urban quality of indoor and outdoor spaces.

Double-skin façade simulation with computational fluid dynamics:A review of simulation trends,validation methods and research gaps

Dynamic simulation of a double-skin façade(DSF)with computational fluid dynamics(CFD)can be challenging due to the lack of validated models and benchmarking datasets.Furthermore,there is a lack of consensus in the scientific community on what constitutes a successfully validated DSF model.The present review study identifies simulation trends and research gaps for DSFs simulated with CFD.Additionally,this article presents a series of CFD simulations in which key aspects of the DSF modelling are varied:2D or 3D modelling approaches,turbulence viscosity models(TVMs),radiation models,and wall function.These simulation results are compared to the empirical data(both temperature and velocity fields)of a benchmark test with laboratory-controlled boundary conditions.This analysis shows that using the k-εRNG model with enhanced wall treatment and surface-to-surface(S2S)radiation model yields the best results for the 2D case of natural convection flow.Moreover,it is shown that accounting for the velocity field in the validation process is essential to ensure the suitability of a model.Finally,the authors advocate for the use of selected dimensionless numbers to improve the comparability of the different DSF scientific studies.This would also help to identify relevant experimental datasets for validation and suitable CFD simulation settings for specific DSF cases.

Design,construction,and thermal performamce evaluation of an innovative bio-based ventilated facade

The energy consumption of the construction sector and its overall environmental impact has greater potential for improvement than those of many other sectors.Most energy consumed throughout the lifecycle of a building is expended during its operation and maintenance,for which the building envelope plays an important role.This study reports on the design,construction,and thermal performance evaluation of a ventilated facade.The facade should be quickly assembled,disassembled,and stored in containers for easy onward transport.Such features comply with the Rules and Buildins Code of the Solar Decathlon Middle East 2018 and the relevant Eurocodes.The facade is constructed using bio-based materials in keeping with the principles of a circular economy.The exterior cladding consists of sanitary paper,grass,reeds,recycled textiles,drinking water treatment waste,bio-based polyester resin,and other materials.Temperature and the air velocity measurements recorded on the facade in Dubai showed that the facade had contributed to cool temperatures within the apartment,particularly during the hottest hours of the day.The facade is a promising option for climates with hot summers and mild winters as it contributes to reducing energy consumption and the environmental impact of building materials.

Promoting conservation or change? The UNESCO label of world heritage (re)shaping urban morphology in the Old Town of Lijiang, China

The UNESCO label of World Heritage can attract a high number of tourists to heritage properties for their Outstanding Universal Values(OUV).Although international studies have demonstrated the World Heritage system can contribute to stimulating cultural tourism and boosting the local economy,how this heritage-led development process promotes urban conservation and morphological changes in cities is yet to be explored adequately.This paper aims to explore the urban morphology of a historic Chinese city after its inscription on the UNESCO World Heritage List,taking the Old Town of Lijiang as case.The methods of spatial plan analysis and discourse analysis are used to analyse the data.The research findings have demonstrated that,theoretically,the Conzenian approach of urban morphology can be an effective framework for World Heritage OUV-monitoring.Methodologically,the analysis of spatial plans and multi-stakeholder interviews can reveal local urban morphology from macro to micro levels.Case-specifically in Lijiang,urban physical characteristics and building structures have been well maintained while traditional social fabric and neighbourhood networks have almost vanished.This research has incorporated the urban morphology approach into(world)heritage management in China,as well as worldwide,in order to maintain the balance between urban conservation and economic growth.

Three-dimensional modeling with national coverage:case of The Netherlands

Three-dimensional technologies have matured over the years.At the same time,3D information is becoming increasingly important in many applications.Still it is not straightforward to apply the solutions that work on prototypes,small areas or for specific projects to 3D modeling of a whole nation.In the Netherlands,two initiatives are ongoing to address the issues of nation-wide 3D modeling.First,the initiative that aims at establishing and implementing a national 3D standard for large-scale topography with support of all stakeholders.Collecting and maintaining the large-scale data are the responsibility of local governments(mainly municipalities).The second initiative is led by the Kadaster(the organization responsible for topographic mapping in the Netherlands)and aims at automatically generating a 3D version of the 1:10 k object-oriented data-set based on a smart combination of the two-dimensional data with high-resolution laser data.Both initiatives are presented in this paper including results,open issues,and future plans.

Novel typology of accelerated carbonation curing:using dry and pre-soaked biochar to tune carbon capture and mechanical properties of cementitious mortar

One of the challenges of promoting accelerated carbonation curing(ACC)of concrete as a carbon sequestration strategy is ensuring that carbonation will not deteriorate mechanical strength.This study examined the mechanical strength,water sorptivity and carbonation efficiency of ten types of mortar containing dry or pre-soaked biochar subjected to internal and/or external carbonation.The results obtained enabled a typology of ACC to be proposed,in which the carbon dioxide absorption of mortar containing various types of CO_(2)-dosed biochar ranged between 0.022%and 0.068%per unit dosage hour.In particular,the mortar containing dry biochar dosed with carbon dioxide was the top candidate for concurrently increasing both compressive strength(54.9 MPa)and carbon dioxide absorption(0.055%per unit dosage hour).Mortar containing pre-soaked biochar dosed with carbon dioxide was identified as a strategy that achieved the highest carbonation efficiency(0.068%per unit dosage hour),but it also reduced compressive strength(45.1 MPa).Collectively,the proposed typology offers a useful overview of the different ways by which biochar can be used to tune ACC in mortar,according to any technical constraints and/or intended functions of the carbonated concrete components.

Localization and mapping algorithm based on Lidar-IMU-Camera fusion

Positioning and mapping technology is a difficult and hot topic in autonomous driving environment sensing systems.In a complex traffic environment,the signal of the Global Navigation Satellite System(GNSS)will be blocked,leading to inaccurate vehicle positioning.To ensure the security of automatic electric campus vehicles,this study is based on the Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain(LEGO-LOAM)algorithm with a monocular vision system added.An algorithm framework based on Lidar-IMU-Camera(Lidar means light detection and ranging)fusion was proposed.A lightweight monocular vision odometer model was used,and the LEGO-LOAM system was employed to initialize monocular vision.The visual odometer information was taken as the initial value of the laser odometer.At the back-end opti9mization phase error state,the Kalman filtering fusion algorithm was employed to fuse the visual odometer and LEGO-LOAM system for positioning.The visual word bag model was applied to perform loopback detection.Taking the test results into account,the laser radar loopback detection was further optimized,reducing the accumulated positioning error.The real car experiment results showed that our algorithm could improve the mapping quality and positioning accuracy in the campus environment.The Lidar-IMU-Camera algorithm framework was verified on the Hong Kong city dataset UrbanNav.Compared with the LEGO-LOAM algorithm,the results show that the proposed algorithm can effectively reduce map drift,improve map resolution,and output more accurate driving trajectory information.

AI in HVAC fault detection and diagnosis:A systematic review

Recent studies show that artificial intelligence(AI),such as machine learning and deep learning,models can be adopted and have advantages in fault detection and diagnosis for building energy systems.This paper aims to conduct a comprehensive and systematic literature review on fault detection and diagnosis(FDD)methods for heating,ventilation,and air conditioning(HVAC)systems.This review covers the period from 2013 to 2023 to identify and analyze the existing research in this field.Our work concentrates explicitly on synthesizing AI-based FDD techniques,particularly summarizing these methods and offering a comprehensive classification.First,we discuss the challenges while developing FDD methods for HVAC systems.Next,we classify AI-based FDD methods into three categories:those based on traditional machine learning,deep learning,and hybrid AI models.Additionally,we also examine physical model-based methods to compare them with AI-based methods.The analysis concludes that AI-based HVAC FDD,despite its higher accuracy and reduced reliance on expert knowledge,has garnered considerable research interest compared to physics-based methods.However,it still encounters difficulties in dynamic and time-varying environments and achieving FDD resolution.Addressing these challenges is essential to facilitate the widespread adoption of AI-based FDD in HVAC.