Construction on very soft peaty clay remains a major geotechnical challenge due to its high compressibility and low-bearing capacity. The deep mixing method (DMM) is widely adopted for in situ stabilization using cement; however, environmental concerns associated with cement production have driven the search for sustainable alternatives such as geopolymers using low-carbon materials. Existing studies predominantly rely on dried peat, processed precursors such as fly ash or calcined ground rice husk ash (RHA), and high concentrations of alkali activators such as sodium silicate (Na2SiO3) and sodium hydroxide (NaOH), which increase both environmental and economic burdens. This study develops a novel waste-based geopolymer incorporating untreated brick kiln-derived RHA, activated solely with low-concentration NaOH, while completely eliminating Na2SiO3. The avoidance of precursor pre-treatment and Na2SiO3 significantly reduces processing energy, cost, and associated environmental emissions. A systematic investigation was conducted to determine the optimum mixing time for maximizing strength under field-relevant conditions. Mechanical performance was evaluated using unconfined compressive strength tests considering variations in binder content, curing duration (7, 28 days), alkali concentration (6, 3 M), and alkali-to-binder ratio (0.3, 0.5, 0.7). Failure characteristics were examined, and an integrated framework combining cost analysis, life cycle assessment, and grey relation analysis was employed to optimize mix design. The optimized geopolymer achieved 2.2 times higher strength than cement-treated soil, with 25% cost reduction and more than 85% reduction in environmental impact. These findings demonstrate a scalable and sustainable solution for stabilizing highly organic soils, while promoting the valorization of supplementary cementitious materials without energy-intensive preprocessing.

ABSTRACT Increasing penetrations of variable renewable energy sources like wind and solar photovoltaic (PV) systems are challenging power system stability worldwide. Leveraging demand‐side behavior is becoming more popular to help overcome contemporary issues concerning balancing electricity generation and demand. As significant energy users with the potential to act as electricity producers through renewable energy sources, buildings are attractive assets for contributing to power system control from energy efficiency and demand response perspectives. Meanwhile, the proliferation of “smarter” buildings equipped with network‐connected sensors and devices using Internet of Things (IoT) platforms produces significant data volumes that lend themselves to novel artificial intelligence (AI) and machine learning (ML) methods that we can apply across the suite of demand response design steps. This paper reviews the application of AI and ML methods across these steps, which include building energy analysis and auditing, modeling and predicting building load demand, detecting and classifying building energy and power flexibility, implementing flexible building load control, and participating in demand response and other ancillary service markets. Throughout the paper, we comprehensively analyze the application of various AI and ML methods, highlighting their effectiveness and limitations. We also identify emerging pertinent challenges of interest to practitioners and researchers examining the implementation of such approaches for building demand response provision. This article is categorized under: Cities and Transportation > Buildings Energy and Power Systems > Energy Infrastructure Energy and Power Systems > Energy Management

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.

Surface pH is a well-established measurable indicator of microbiologically induced concrete corrosion (MICC) in sewer infrastructure, as it directly governs the biochemical conversion of hydrogen sulfide (H2S) into sulfuric acid (H2SO4). However, predicting surface pH remains challenging due to complex corrosion processes, a lack of long-term monitoring data, and difficulties in obtaining measurements in dynamic sewer environments. Existing deterministic models often overlook spatio-temporal variability and struggle to reliably predict the initiation and rate of corrosive pH decline. In this study, experimental data capturing key environmental parameters under laboratory simulated sewer conditions were utilised. Multiple data-driven machine learning (ML) algorithms were explored and evaluated; in addition, the proposed lightweight neural network (LNN) was developed to predict surface pH as an initial detection method for MICC. Among the six ML algorithms, the LNN achieved the highest predictive performance (R2 = 0.983 on testing dataset), statistically outperforming all the models (Wilcoxon signed-rank test, W = 28,570, n = 549, p < 0.001). SHapley Additive exPlanations (SHAP) and Sensitivity analyses were applied to evaluate the individual contributions of the parameters, indicating that exposure time and H2S concentration were consistently identified as the primary drivers of surface pH decline. These findings confirm that prolonged H2S exposure under saturated humidity conditions drives progressive surface acidification, accelerating biogenic H2SO4 production and cementitious phase dissolution in sewer concrete. The proposed framework supports the potential of ML-based surface pH prediction as a data-driven tool for proactive corrosion monitoring in sewer infrastructure.

Polymer concrete (PC) is an advanced composite whose engineered combination of synthetic resins and aggregates yields markedly improved mechanical performance and durability versus ordinary Portland cement systems. This review synthesises findings to (1) trace the historical development of PC, (2) categorise common resin classes (epoxy, polyester, vinyl ester, furan, polyurethane), (3) consolidate mix-design principles and additive/fibre strategies, and (4) quantify static and dynamic mechanical behaviour (compressive, tensile, flexural strength, fracture toughness, damping and strain-rate sensitivity) under varied curing and environmental exposures. Key outcomes include identification of an optimal resin-content window (≈12–17 wt%) that balances strength and cost, documented trade-offs between resin type and thermal/chemical durability, and the pronounced superiority of PC in impact and vibration-damping applications. The review critically examines sustainability challenges (embodied energy, recyclability, and life cycle impacts) and surveys mitigation pathways such as recycled fillers, bio-based resins, and circular economy practices. Finally, it maps future research priorities: standardised testing protocols for structural use, development of low-carbon and recyclable polymer matrices, and deployment of machine learning for mix design and property prediction. This integrated roadmap informs engineers and researchers seeking to deploy PC in structural, marine, transportation and defence applications while guiding research toward sustainable, data-driven innovation.

This study investigated the influence of various manufacturing conditions - including moulding pressure, postcuring, and aging - on the microstructure and mechanical properties (flexural and tensile) of epoxy matrix composites incorporating recovered glass particles at weight fractions ranging from 84 wt% to 90 wt%. The study focused on understanding how these conditions affect the interfacial bonding between the glass particles, epoxy matrix, and void content to establish a correlation between microstructure and mechanical performance before and after ceramification. The findings revealed that increasing moulding pressure from 1.1 MPa to 6.6 MPa reduced void content, increased composite density, and significantly improved flexural properties. The impact of post-curing on the composites' flexural performance was also examined, and it was found that adjusting the epoxy matrix weight fraction from 6 wt% to 12 wt% further influenced the composite's mechanical properties. Xray computed tomography (CT) and scanning electron microscopy (SEM) analyses revealed changes in composite porosity and interfacial bonding, enabling the correlation of these microstructural changes with variations in mechanical properties for both non-ceramified and ceramified composites. Ceramification induced additional microstructural changes, including the formation of voids, which influenced the composites' mechanical properties. Additionally, the effect of integrating steel wire mesh with 6.5 mm apertures on the mechanical performance of the glass/epoxy composites, both before and after ceramification, was explored.

Critical Infrastructures (CI) are vital for societal and economic stability, yet their resilience against disasters remains inadequately understood with the increasing interdependencies among the CIs. A better understanding of these interdependencies and the dynamic nature of CI functionalities is crucial for advancing disaster resilience assessment within engineering systems. This paper introduces a novel approach using a Dynamic Bayesian Network (DBN) to assess resilience in interdependent CI systems. The DBN method enables a probabilistic evaluation of system resilience by incorporating interdependencies and capturing the temporal dynamics of system capacities. This approach offers a more detailed perspective on resilience by modelling system functionality using expected values of different functionality states over time. Using a case study in Sri Lankan electricity, water distribution, and road infrastructure sectors and 34 experts, this study examines the complex network of CIs. It demonstrates the applicability of the proposed methodology. P-values of the Chi-Square test performed between the variation of model-predicted resilience and expert assessments are significantly less than 0.05, confirming the model's validity. Additionally, this study explores the expansion of the methodology for resilience assessment under multiple hazards, emphasizing its real-world effectiveness. The findings highlight the efficacy of the proposed methodology and its potential to assist asset managers, owners, and decision-makers in informed resilience planning and optimization strategies. This comprehensive approach fills critical gaps in existing methodologies, offering a robust framework for assessing CI resilience in a dynamic and systematic nature.

Greening of the construction sector has witnessed the widespread practice of recycling fly ash into building materials for decades. The control of ash waste quality is an important aspect for its viability as a supplementary cementitious material, where the detailed physical and chemical characteristics play crucial roles. To better facilitate informed greener design, a novel method is leveraged to study particularly the influence of amorphous content in fly ash on binder hydration. In this study, a coupled kinetic-thermodynamic approach is implemented to realise comprehensive hydration assessment. The adopted kinetics-based model is developed following unified theory to quantify the hydration/reaction degrees for cement and fly ash. Such kinetic information is further utilised in thermodynamic analysis, powered by Gibbs energy minimisation method, to evaluate the time-dependent phase assemblage of cementitious system upon continuous hydration. The applied technique is carefully verified against a series of reported tests, before further exploited to conduct numerical explorations. Computational findings highlight the influence of amorphous content in fly ash on the chemo-physical–mechanical properties of concrete products, providing insights for future sustainable construction material design.

This study investigates the primary data collected at a used cooking oil (UCO) recycling facility to quantify its environmental impact when used as a rejuvenator in high content reclaimed asphalt pavement (RAP) mixes. Annual energy consumption data sets on transportation, storage, filtration, machinery, and purification are assessed using the life cycle assessment (LCA) methodology with the LCA software Simapro 9.4 to evaluate the influential parameters and processes in reducing emissions. Pearson correlations of the production process show a higher association of carbon footprint with two dominant recycling factors: (1) electricity consumption (r = 0.89) and (2) fuel consumption for transportation (r = 0.37). A comprehensive LCA conducted on asphalt pavements with various RAP contents shows that a 28.81 % reduction of carbon emissions can be achieved during the construction phase by adding 60 % rejuvenated RAP. However, the relative uncertainty around RAP moisture content at the time of usage and faster pavement deterioration over the service life when high RAP content is incorporated into asphalt mixes might hinder the environmental advantage of adding RAP during the maintenance phase. Non-properly treated, blended, and rejuvenated RAP can be susceptible to early cracking, thus resulting in increased periodic maintenance. Moreover, the break-even point of economic appraisal suggest over 50 % RAP content is beneficiary even at the higher discount rates (12 %). Conducted LCA and sensitivity analyses emphasise the importance of collecting primary data from recycling facilities and considering both the construction and maintenance phases for implementing effective sustainable strategies.

Expansive soil, characterized by volumetric changes, presents formidable challenges to road infrastructure. The limitations of Ca-based treatments have spurred the investigation of geopolymers, yet Na2SiO3 in the alkaline activator compromised the sustainability. In response, a novel waste-derived rice husk ash (RHA)-based silicate solution was introduced for geopolymerbased expansive subgrade stabilization (Slag-RHA-GP), though its environmental sustainability remains unexplored. This study conducts a cradle-to-construction life-cycle assessment of SlagRHA-GP while comparing results with cement (OPC) and conventional Na2SiO3-based geopolymer treatment (GP) based on a hypothetical case study. Normalized mid-point impacts indicate that the Slag-RHA-GP binder exhibits similar to 43 % improved environmental performance compared to GP. RHA-silicate substitution reduces ecosystem impacts by similar to 79 % at the raw material production stage. Moreover, except for Hg and Se, heavy metal leaching of Slag-RHA-GP remains well below permissible levels. Slag-RHA-GP demonstrated a 31.5 % cost-effectiveness and an overall sustainability advantage of 28.9 % and 6.4 % over conventional GP and OPC, respectively. The findings will support the future adoption of sustainable geopolymer stabilizers for in-situ expansive subgrade treatment, aligning with emission neutrality goals.

Millions of tonnes of plastic and glass waste are generated worldwide, with only a marginal amount fed back into recycling with the majority ending at landfills and stockpiles. Excessive waste production calls for additional recycling pathways. The technology being investigated in this study is based on recycled glass fines encapsulated in a high-density polyethylene (HDPE) matrix. Laboratory tests are performed on specimens at different manufacturing conditions using compression moulding, determining an optimised manufacturing method. The performance of composites prepared under different formulations is tested to identify an optimised mix design by means of statistical analysis. At this optimum ratio, flexural, tensile, and compressive strengths of 33.3 MPa, 19.6 MPa, and 12.8 MPa, are, respectively, recorded. Upon identifying the optimum dosage levels, the potential for employing HDPE from diverse origins are investigated. The microstructure, pore structure, and chemistry of optimised composite specimens are analysed to interpret the composite performance. The effective stress transfer in the composite is attributed to strong hydrogen bonds created by maleic anhydride leading to 37.6% and 8.5% improvements in compressive and flexural strengths, respectively. These research findings can facilitate the pathway for utilising plastic and glass waste in landfills/stockpiles for sustainable polymeric composites towards structural applications.

Aging infrastructure is a significant concern to many system operators who might struggle to operate, maintain, and improve systems and infrastructure assets due to spatially buried assets over a large area, uncertainty about their condition, and lack of a comprehensive planning. These challenges often lead to a reactive approach to maintenance, causing emergency situations due to unexpected asset failures. This study proposes a suite of risk-based asset management plan tools to extend the service life of a drainage pipe network with a length of 580 km managed by a city council in Australia. The proposed management plan tools are based on a reactive-proactive approach to manage risk in which proactive condition monitoring is applied to critical and important pipes for detecting failure conditions in a timely manner. A sample of critical closed-circuit television (CCTV) inspected pipes selected from pipe network is used to predict the current and future condition of the pipe network using a Markov deterioration model. The predicted condition is used to support various implementation tasks of an asset management plan and accounting requirements. The key findings of the case study include a calibrated Markov deterioration curve showing slow rate of deterioration for a drainage network, average service life estimation (120-180 years) for drainage pipes, lowest-cost inspection interval determination (i.e., 7-14 years) for detecting poor condition, and estimated annual inspection and replacement cost for a drainage network. A significant number of local governments and city councils around the world manage drainage pipe networks as high capital asset value. Extending the service life of drainage pipes with reduced life-cycle cost and managed failure risk is a challenge and an essential task for asset management. This study introduces a proactive-reactive asset management strategy and a suite of decision support tools to assist in achieving such challenging tasks. For example, instead of using the common design service life of 80-100 years for asset renewal planning, this study demonstrates, through a case study, that service life can be extended to 120-180 years, resulting in significant savings of capital investment. To control failure risk and reduce life-cycle cost during the extended service life, a suite of decision support models including an optimal inspection model, a failure risk model, and others can be used to justify annual maintenance funding, timely detecting poor condition, and suggesting appropriate maintenance and rehabilitation planning. The results show that the inspection interval can be 7-14 years, and the Markov deterioration model predicted that the current network has 7% of pipe in a failure condition, which will increase by 1.67% over the next 10 years, which can justify renewal funding.

Millions of tonnes of plastic and glass waste is produced worldwide, with only a limited quantity being recycled with the majority ending up in landfill, our oceans or in stockpiles. Even though current applications exist for recycled glass and plastic, there is still a need to introduce recyclate to different industries. This review presents an investigation aimed at mechanical and thermal property improvements by the incorporation of reinforcement material into a polymeric matrix. The performance of high-density polyethylene (HDPE) and glass composites manufactured with different levels of glass, additives and surface treatments have been evaluated by analysing several past investigations. The critical parameters influencing the mechanical and thermal performance of HDPE-Glass composites were identified to be the type of reinforcement, glass content, additives utilised, and the different manufacturing methods employed. Optimised mix designs proposed by researchers have been analysed along with reaction mechanisms. This review has identified a composite material to be further refined and optimised with the potential to be improved by integrating reclaimed waste to manufacture composites by promoting a zero-waste economy. Improvements in tensile strength up to 71% can be observed with the implementation of 20% by weight of glass reinforcement in HDPE composites. The use of maleic anhydride grafted polyethylene (MAgPE) as a compatibilizer facilitates the stress transfer between the two phases by improving their bonding with tensile and flexural strength increments up to 85% and 25%, respectively. This review is a valuable resource for future researchers as it identifies crucial research gaps in HDPE-Glass composite manufacturing and synthesises key findings in existing literature.

PurposeDefining degradation in terms of physical deficiency-based condition descriptors, combined with Markov chain modelling, has been shown to provide improved predictions of degradation. However, unless these physical conditions are converted to lost value ratios (LVRs), maintenance managers would not be able to grasp the cost implications of degradation. Hence the purpose of this research is to convert the predicted deficiency-based condition ratings to lost value ratio bands.Design/methodology/approachRectification costs were found using a Building Schedule of Rates to arrive at LVRs for each of the physical degradation conditions for the 12 building elements studied (ranging from concrete elements through finishes and ceilings to doors and windows). These LVRs were allocated into five bands with LVR interval limits of 0.00, 0.10, 0.25, 0.50, 0.75 and 1.00, with the five intervening ranges corresponding to LVR Bands A to E. These computations were compared with those arrived at independently by industry professionals.FindingsElements such as doors, widows and ceilings reached the maximum LVR Band E at the worst physical Condition 5 defined. However, Condition 5 for other elements only corresponded to LVR Bands A to D. Some 83% of the LVR bands assigned to the physical conditions were in agreement with those arrived at by the professionals, or differed by only one band.Originality/valueThe conversion of deficiency-based conditions to LVR bands yielded a completely new maintenance-oriented perspective on degradation. The banding was done using a novel ranking and clustering process that identified regions of high variation in LVRs as thresholds of the bands.

The deterioration of concrete sewer structures due to bio-corrosion presents critical and escalating challenges from structural, economic and environmental perspectives. Despite decades of research, this issue remains inadequately addressed, resulting in billions of dollars in maintenance costs and a shortened service life for sewer infrastructure worldwide. This challenge is exacerbated by the absence of standardized test methods and universally accepted mitigation strategies, leaving industries and stakeholders confronting an increasingly pressing problem. This paper aims to bridge this knowledge gap by providing a comprehensive review of the complex mechanisms of bio-corrosion, focusing on the formation and accumulation of hydrogen sulfide, its conversion into sulfuric acid and the subsequent deterioration of concrete materials. The paper also explores various factors affecting bio-corrosion rates, including environmental conditions, concrete properties and wastewater characteristics. The paper further highlights existing corrosion test strategies, such as chemical tests, in-situ tests and microbial simulations tests along with their general analytical parameters. The conversion of hydrogen sulfide into sulfuric acid is a primary cause of concrete decay and its progression is influenced by environmental conditions, inherent concrete characteristics, and the composition of wastewater. Through illustrative case studies, the paper assesses the practical implications and efficacy of prevailing mitigation techniques. Coating materials provide a protective barrier against corrosive agents among the discussed techniques, while optimised concrete mix designs enhance the inherent resistance and durability of the concrete matrix. Finally, this review also outlines the future prospects and challenges in bio-corrosion research with an aim to promote the creation of more resilient and cost-efficient materials for sewer systems.

The worldwide adaptation of Photovoltaic (PV) technology as a sustainable alternative to fossil fuels, has experienced exponential growth in recent years. However, the lack of effective waste management policies has hindered efficient PV panel disposal and recycling practices. In the absence of effective policies, the worldwide End-of-Life (EoL) PV module accumulation is predicted to reach a critical stage in the early 2030s. This study examines the environmental impacts of different EoL management practices using Life Cycle Assessment (LCA), based on a comprehensive database generated from both literature and industrial data. Five different EoL scenarios were considered for 1000 kg of Crystalline Silicon (c-Si) PV modules with a focus on Australia as a case study, while considering the energy recovery options and emphasizing the economic benefits. From a comprehensive LCA study, it is found that upcycling options reduce the environmental impacts significantly compared to downcycling, while chemical treatment emerges as a preferred option, demonstrating a reduced environmental burden. Nevertheless, the utilization of toxic chemicals during chemical treatment-based PV recycling needs to be reconsidered. Additionally, the usage of chemicals during the metal recovery process of PV module recycling caused the highest environmental burden, necessitating the importance of moving to greener treatment practices. This research study will enable the identification of optimum EoL c-Si module recycling options, contributing to sustainable waste management.

Bridge network maintenance and rehabilitation activities are crucial to prevent bridge failure from human-made and natural hazards such as deterioration, overload, collision, fire, flooding, and earthquake. Under a limited budget, understanding the bridge closure cost to social-economic-environmental aspects can help prioritizing bridge network maintenance and rehabilitation in a cost-benefit justifiable manner. In this study, the costing method is applied to calculate social-economic-environmental costs incurred due to detour routes with additional travel time and distance. In addition to the commonly used cost factors of time value cost and vehicle running cost, this study also uses probability theory to calculate the expected consequence costs of delayed rescue services of ambulance, police, and fire-fighting caused by bridge closure. A case study with a bridge network is used to demonstrate the methodology adopted and outcome benefits from study. The results of case study show that the time value cost of vehicle passengers, vehicle running cost, and ambulance delay cost are the major cost contributors. The bridge importance ranking and consequence cost of bridge closure can be used to provide prioritized inspection and repair program for bridge components that have a high ranking under limited budget and provide benefit estimations. In the real asset management of bridge networks, information on consequences of bridge closure is essential to bridge maintenance and rehabilitation (M&R) planning. For example, if the daily cost of bridge closure can be estimated particularly as a monetary value, the funding application for bridge M&R can be easily approved because it can show the M&R cost is lower than the benefit, which is to avoid the consequence cost of bridge closure. Furthermore, under a limited budget, bridges with a higher consequence cost of bridge closure in the bridge network can be prioritized for M&R planning. Understanding the consequence cost of bridge closure can also help in developing consequence-mitigated planning during actual bridge closures for planned repair. This study developed a costing methodology to estimate the daily consequence cost of bridge closure as a monetary value in terms of social-economic-environmental impacts. The results show that the time value cost of vehicle passengers and vehicle running cost are the most important contributors to the total consequence cost for bridges with available detour routes for crossing. For bridges with no alternative crossing routes, ambulance delay cost is the major contributor, followed by delay cost of police and fire-fighting services.

This study advocates establishing an indicator system for Critical Infrastructures (CIs) resilience assessment to ensure consistency and comparability in future endeavors. Resilience has emerged as a fundamental framework for effectively managing the performance of CIs in response to the challenges posed by disaster events. However, it is evident that a lack of uniformity exists in the choice and standardization of resilience assessment frameworks across the identified frameworks. This paper proposes key attributes for facilitating resilience assessment of the CIs using an in-depth literature survey for identification and two rounds of the Delphi survey in Sri Lankan Context for their verification. The outcome of the literature survey has analyzed the resilience assessment attributes under five types of capacities of resilience assessment: planning (anticipative), absorptive, restorative, and adaptive. Twenty-seven resilience attributes (Planning: 6; Absorptive: 12; Restorative: 6; Adaptive: 3) under different capacities were identified, including sub-indicators for evaluating each resilience attribute. The outcomes of the Delphi survey were analyzed through descriptive statistics. The proposed attributes received high levels of agreement from the experts, indicating their suitability and applicability for assessing the resilience of the CIs. The mean ratings of the attributes varied from 4.0 to 5.0, with the majority exceeding 4.5. The evaluation of these attributes will be useful for assessing the resilience capacity of the CIs and thereby to model the overall resilience of the CIs. The results of this study will provide a solid basis for formulating hypotheses in future research aimed at assessing CI resilience.