The building sector faces increasing pressure to reduce embodied carbon as operational emissions decline and life-cycle impacts of construction materials gain greater attention. Structural components such as roof purlins are widely used in industrial and commercial buildings and therefore represent an important opportunity for reducing material-related environmental burdens. This study presents a life-cycle environmental and economic assessment of alternative roof purlin systems manufactured from conventional and circular materials within a performance-based structural design framework.A life-cycle assessment and life-cycle cost analysis was conducted in accordance with EN 15804 + A2, covering Modules A1–A3 and C1–C4, with Module D benefits reported separately, using Australian life-cycle inventory datasets. Five purlin systems were evaluated, including recycled waste composite (RWP), cold-formed steel (CFS), timber (TP), fibre-reinforced polymer (FRP), and aluminium alloy (AAP). All systems were structurally optimised to achieve equivalent load-bearing capacity and serviceability over a 50-year service life, using 1 m2 of supported roof area as the functional unit. Results indicate that TP exhibits the lowest global warming potential (1.21 kg CO2-eq/m2), while RWP provides competitive environmental performance (6.96 kg CO2-eq/m2) and the lowest life-cycle cost (4.93 A$/m2). FRP and AAP exhibit the highest impacts under the primary Modules A–C comparison, although metallic systems show improved apparent performance when Module D benefits are reported separately. Integrated environmental–economic evaluation indicates that TP and RWP consistently outperform alternatives across multiple decision scenarios.The findings demonstrate that combining structural optimisation with appropriate material selection enables significant reductions in embodied carbon and cost and provides a decision-support framework for sustainable material selection under varying environmental and economic priorities.

The integration of machine learning (ML) in cyber physical systems (CPS) is a complex task due to the challenges that arise in terms of real-time decision making, safety, reliability, device heterogeneity, and data privacy. There are also open research questions that must be addressed in order to fully realize the potential of ML in CPS. Federated learning (FL), a distributed approach to ML, has become increasingly popular in recent years. It allows models to be trained using data from decentralized sources. This approach has been gaining popularity in the CPS field, as it integrates computer, communication, and physical processes. Therefore, the purpose of this work is to provide a comprehensive analysis of the most recent developments of FL-CPS, including the numerous application areas, system topologies, and algorithms developed in recent years. The paper starts by discussing recent advances in both FL and CPS, followed by their integration. Then, the paper compares the application of FL in CPS with its applications in the internet of things (IoT) in further depth to show their connections and distinctions. Furthermore, the article scrutinizes how FL is utilized in critical CPS applications, e.g., intelligent transportation systems, cybersecurity services, smart cities, and smart healthcare solutions. The study also includes critical insights and lessons learned from various FL-CPS implementations. The paper's concluding section delves into significant concerns and suggests avenues for further research in this fast-paced and dynamic era.

Cracking remains a primary durability challenge in concrete infrastructure, particularly in water-retaining and marine environments. This study evaluates recycled textile-derived cellulose as a sustainable self-healing agent for cementitious composites. Specimens with 0-5% cellulose replacement underwent controlled surface cracking and internal mechanical damage, with healing quantified over 28 days via optical microscopy, micro-CT, mechanical testing, and microstructural characterization. Results demonstrated that 2% cellulose incorporation was the optimum dosage, enhancing both surface and internal healing. Surface crack closure was 10.46% for narrow cracks and 4.87% for the control, while internal healing efficiency achieved 22.1% compared to 11.8% for plain mortar. Micro-CT revealed a 61.2% reduction in elongated crack-like voids, and compressive strength recovery peaked at 123.5%. SEM/EDS identified C-S-H, calcium carbonate, and calcium hydroxide precipitation, driven by cellulose-mediated internal curing and fiber bridging. The hydrophilic cellulose network sustains crack repair through internal moisture retention, without external healing agents.

The use of concrete composites with textile waste provides a sustainable path for circular construction. This paper reports the effects of the incorporation of textile-derived cellulose on the performance of cementitious composites. The study investigated the effect of the substitution of cement with microcellulose, 0 to 5%, on the compressive strength of cement paste. Isothermal calorimetry revealed cellulose delays initial hydration and increases the cumulative heat release over time. Chemical and microstructural analytical techniques such as thermogravimetric analysis, nuclear magnetic resonance, mercury intrusion porosimetry, and scanning electron microscopy were employed to examine the reaction kinetics of the cement when incorporating recycled cellulose. The research findings highlighted that recycled textile cellulose notably impacts the cement paste hydration process and the properties developed. Optimal cellulose content was identified as 1% by cement weight.

Calcination is a common activation method for clays used as supplementary cementitious materials (SCMs). This study explores an ecofriendly and sustainable clay activation approach, namely mechanochemical calcination (MCC), which combines calcination with a high shear grinding process. Two natural mixed layer clays, dominated by kaolinite and illite, along with the respective virgin pure clays (kaolinite and illite), were treated via calcination, mechanochemical activation, and MCC. The study evaluated their mineralogical transformation, microstructure, and compressive strengths. The findings revealed that mechanochemical activation reduced particle size by up to 73

Heating, Ventilation, and Air Conditioning (HVAC) maintenance requires forward prediction of component maintenance needs, yet remains dominated by detection and diagnosis. Where prediction exists, components are often modelled as independent sensor-monitored entities, obscuring inter-subsystem degradation propagation and interpretability. Physics-based forecasting depends on thermodynamic parameters requiring instrumentation seldom available. Computerised Maintenance Management System (CMMS) work orders, a knowledge repository of longitudinal maintenance, however, remain underused for prediction. Thus, this study proposes a graph-based unplanned work order prediction at the component-level using time-series CMMS data as the dominant source of information. A large language model-based knowledge extraction pipeline was first developed to transform work order text into structured entities. A time-expanded heterogeneous graph with component and work order nodes connected through twelve edges encoding hydronic, air, control, correlation, spatial, and maintenance relationships was then constructed to model plausible degradation propagation. Next, a sparse gated Graph Neural Network Mixture of Experts (GNN-MoE) analysis model is proposed. HVAC subsystem-aligned expert models, such as heating and cooling, are implemented using Graph Transformer, Graph Attention Network v2, or GraphSAGE matched to sub-graph topology. Threshold-constrained evaluation supports predictions at 14, 30, 90, and 365-day horizons, to support maintenance cycles, validated on a multisite dataset of 19,411 work orders. 14-day horizon achieves 70% precision with 27 times lift at 2.6% prevalence for immediate maintenance dispatch. A 365-day horizon achieves 89.5% recall for long-term maintenance planning. Ablations highlight graph and text feature influence, and interpretable expert routing patterns reflect maintenance dynamics aligned with HVAC sub-systems.

In response to the decreasing availability of high-grade kaolinitic clays, there is a growing shift toward using natural waste clays as supplementary cementitious materials. The performance of these waste clays is generally evaluated based on either strength or environmental impact, but the combination of both is rarely considered. This study undertakes a strength-normalized life cycle assessment to understand the interaction between these two parameters and utilizes the output to identify the most suitable activation process. The study used five Australian natural waste clays activated by calcination (600–900 °C) and high-shear mechanical grinding, replacing 30

Concrete production is a major source of global emissions, and incorporating supplementary cementitious materials (SCMs) offers a pathway to lower carbon construction. This study evaluates the life cycle and cost performance of eight alternative SCMs in Australia: recycled concrete powder (RCP), clay brick powder (CBP), recycled glass powder (RGP), lithium slag powder (LSP), red mud powder (RMP), limestone calcined clay (LC2), steel slag powder (SSP) and pond ash (PA). The study considers 83 mixes containing these SCMs, as reported in the literature, which achieved 30-40 MPa. The results showed that LC2 achieved the lowest GWP (283 kg CO2-eq/m(3); 8.22 kg CO2-eq/MPa) and the lowest cost (similar to 133 AU$/m(3)). RGP and LSP also reduced ADPF to 71-70 MJ/MPa, while RMP and LSP achieved the lowest strength-normalised costs (3.8-3.9 AU$/MPa). CBP and PA performed worse due to poor reactivity and higher costs (>4.3 AU$/MPa). These findings highlight the potential of LC2, RGP, LSP, and RMP to support circular, low-carbon concrete in Australia.

Cement production has become a significant contributor to global CO2 emissions, underscoring the urgent need for sustainable cementitious alternatives. Among these, activated clays, notably metakaolin (MK), together with other calcined clays (CC), have emerged as promising Supplementary Cementitious Materials (SCMs) due to their high pozzolanic reactivity and environmental benefits. However, binary systems with either CC/MK or nanomaterials (NMs) often face intrinsic limitations, such as the inability of MK/CC to refine nanopores and the tendency of NM to agglomerate at higher dosages, thereby compromising mechanical integrity. This review evaluates the reaction mechanisms and performance characteristics of ternary CC/MK-NM cement composites. The review reveals that the ternary system exhibits a distinct micro-nano synergy, wherein NM accelerates early-age hydration via nucleation effects, while MK/CC sustain long-term pozzolanic reactions. CC-NM blended concrete composites have demonstrated the potential for peak relative strength increments of up to 65% achieved under optimized conditions, alongside marked enhancements in durability. Despite these advancements, challenges such as increased superplasticizer demand, difficulties in dispersion, and dosage optimization remain critical to ensure consistent performance. In summary, this paper provides an overview of current CC/MK-NM cement composite research, offering mechanistic insights and directs research toward the design of sustainable, high-performance cementitious materials for future application in construction.

Concrete waste recycling is increasingly recognized as a strategy for reducing the environmental burdens associated with end-of-life construction materials, landfilling, and virgin material extraction. Within the transition to a circular economy, recycled concrete aggregate has gained growing attention as a secondary aggregate resource for concrete production. However, before recycled aggregate concrete (RAC) can be widely promoted as an environmentally preferable alternative, its environmental performance must be evaluated through rigorous, transparent, and standardized life cycle assessments (LCAs). This paper presents a PRISMA-guided systematic review of 37 RAC LCA studies and conducts an ISO-informed methodological audit across the four phases of the ISO 14040/44 framework. The review identifies recurring limitations: 82

Stockpiles of glass fines generated from nonrecyclable glass packaging pose significant environmental and human health issues including groundwater and soil contamination. This study explores the suitability of glass fines for developing sustainable composites for building and construction. Glass-reinforced polymer (GRP) composites offer strength and sustainability for construction, but their flammability limits their use in fire-prone environments. This research focuses on the thermal stability, elevated-temperature mechanical properties, and fire properties of GRP composites. The research addresses polymer matrix flammability issues through ceramification, a process that transforms the flammable polymer into a thermally more stable binder. Although ceramification enhances the thermal performance of polymer composites, it reduces the flexural strength, tensile strength, and compression strength decreased by 53%, 60%, and 9%, respectively. This study also evaluates in situ thermomechanical and postheat mechanical performance of ceramified GRP composites exposed to temperatures ranging from 50 degrees C to 250 degrees C. The mechanical properties of the ceramified GRP composites at elevated temperature are relatively lower than measured for the same materials at ambient temperature. However, the mechanical properties of heat-exposed ceramified composites partially recovered upon cooling to ambient conditions. The ceramified GRP composite microstructure and material properties were analyzed via scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), and X-ray diffraction (XRD) analysis. The findings from this work revealed significant reductions in the flammability and smoke production propensities of ceramified GRP composites compared with their nonceramified counterparts, making them suitable for fire-prone infrastructure. The findings from this study revealed substantial reductions in heat release rates (by a factor of 5) and smoke production (by a factor of 19) upon ceramification of GRP composites compared with their nonceramified counterparts, highlighting their suitability for use in fire-prone infrastructure.

Recent advances in concrete technology have identified cellulose as a promising natural fibre for sustainable constructions due to its widespread availability and excellent mechanical properties. However, predicting the mechanical performance of cellulose-modified cement composites remains challenging due to mix design variety and complex interfacial interactions. This research develops a data-driven framework to address the challenges in numerically describing the effect of diverse mixtures and cellulose, to provide robust predictions of the compressive strength of cellulose-modified cement composites. To do so, a comprehensive database is constructed by combining literature data and experimental results. The Extended Support Vector Regression (X-SVR) learning algorithm is employed in correlating strength development with diverse input variants, from which the strength class of cement, water/binder ratio, cellulose dosage, particle size of cellulose, and curing age are identified as the key influential parameters. The study presents three key innovations: construction of the first comprehensive multi-scale database spanning nano to micro cellulose in both pure cement and fly ash-blended systems, first application of X-SVR algorithm to cellulose-cement composites, and introduction of a fly ash contribution factor enabling strength prediction across fly ash blended systems. Sensitivity analysis based on the verified X-SVR aided modelling reveals that water/binder ratio exhibits the highest contribution (32 %), with curing age (28 %) and cellulose particle size (27 %) identified as the next most critical factors governing compressive strength in cellulose-modified pure cement blends. The extended model indicates that cellulose dosage accounts for 15 % of the strength contribution by fly ash.

Abstract This study examines natural waste clays (in Australia) as supplementary cementitious materials (SCMs) for concrete. Seven mixed-layer waste clays were analyzed, investigating calcination and low-energy mechanical grinding as activation techniques. The influence of calcination temperature and the duration of calcination/mechanical activation on the chemical and physical characteristics of clays was investigated. Also, the reactivity of activated clays in blended cement systems was studied. All of the calcined clays achieved a comparable gain in strength to the control mix. Results highlight the optimal parameters for enhancing clay reactivity and complying with local Australian standards. The study revealed that in the absence of detailed clay characterization, a 750°C calcination temperature and a 1-h calcination duration can be used for Australian mixed-layer clays to achieve Grade 1 pozzolans with a strength activity index (SAI) ≥ 0.85 . Additionally, high-intensity grinding using a ring mill is a plausible alternative activation method to produce highly reactive clays (Grade 2 pozzolans with SAI > 0.75 ) consisting of lesser kaolinite content ( ≤ 5 % ). Increasing the calcination temperature above 600°C reduced the specific surface area, negatively impacting reactivity, even if amorphous content increased. The specific surface area and the total clay mineral content in calcined clay are proportional to the SAI. High specific surface area ( > 15 m 2 · g − 1 ) clays with low total mineral content ( ∼ 25 % ) can provide similar strength to a clay with high clay mineral content ( ∼ 50 % ) and low specific surface area ( < 15 m 2 · g − 1 ). In mechanically activated clays, the particle fineness is proportional to SAI.

This study examines the implementation and application of Artificial Intelligence (AI) methodologies for estimating the Remaining Useful Life (RUL) of civil infrastructure assets, with the aim of supporting more effective civil infrastructure maintenance and management practices. A total of 90 publications were reviewed. Although not all were directly related to civil infrastructure RUL, the overall body of work reveals a continuous research activity since 2014, reflecting growing interest in data-driven deterioration forecasting. A key motivation for this review is to identify AI approaches that have been successfully applied to structured datasets and that demonstrate potential for practical integration into civil engineering asset-management environments. While advanced AI techniques exist, their adoption in civil infrastructure engineering and maintenance management remains limited, and many real-world systems require methods that balance predictive capability with interpretability, robustness, and compatibility with existing workflows. This study discusses a range of AI approaches—including Deep Learning (DL), Machine Learning (ML) ensemble regression, and hybrid models—highlighting their ability to capture complex degradation processes and their potential to enhance durability predictions. Challenges such as data quality, generalisability and interpretability of AI models, and the difficulty of embedding advanced analytics into current maintenance systems are identified. Opportunities for future research include improving model stability against noise, leveraging diverse data sources, addressing class imbalance, quantifying predictive uncertainty, exploring alternative degradation models, and integrating maintenance actions within RUL prediction frameworks. Overall, the findings underscore the increasing role of AI in asset-life prediction and highlight the need for approaches that remain technically sound while being feasible for implementation in civil real infrastructure-management settings.