Recent findings reported in the contemporary literature have underlined the criticality of including climate change effects in models of deterioration of civil engineering structures. Highway bridges, when exposed to unfavorable environmental conditions and climate change settings and when simultaneously situated in moderately to highly active seismic zones, may experience reduced structural performance during earthquake events. For bridges in such regions, past studies have revealed the criticality of considering deterioration when computing seismic losses, which may constitute a significant percentage of the life-cycle cost planning for the structure. In acknowledging the potential effects of climate change on bridge deterioration and seismic fragility, a renewed systematic assessment of seismic life-cycle cost then becomes necessary for informed decision making and economic investment. Addressing this critical need, we propose a novel framework for evaluating the lifetime seismic losses of highway bridges that considers earthquake hazards, aging effects, and global warming due to climate change. The proposed framework was first demonstrated on two structurally distinct case-study bridges––multispan continuous (MSC) steel and MSC concrete-girder bridges located in the southeastern United States. Both bridge types comprise multiple seismically vulnerable bridge components that are also prone to the adverse effects of environmental degradation. It was found that there was a substantial underprediction in the lifetime seismic loss––by 6.7% for the MSC steel bridge and 13.2% for the MSC concrete bridge––when expected future climate change was neglected. Additionally, the proposed framework was applied to a seismically designed MSC concrete-girder bridge in order to evaluate the influence of seismic design practices on the lifetime seismic losses of aging highway bridges, including climate change effects.

Geopolymer foam concrete (GFC) has emerged as a sustainable alternative in construction, utilizing waste-derived binders to enhance material properties while addressing environmental concerns. This review examines the impact of precursor materials, foam agents, nanomaterials, fibers, and curing temperatures on GFC’s pore structure and compressive strength. GFC typically achieves compressive strengths of 1–10 MPa, with porosity significantly influencing performance. For instance, GFC with densities of 280–865 kg/m³ exhibits compressive strengths of 1.10–8.13 MPa and thermal conductivities of 0.08–0.20 W/(m K), making it suitable for insulation applications. The integration of alternative materials, such as industrial by-products, enhances sustainability without compromising mechanical properties. Precise control over curing parameters, particularly heating rates, is critical to optimizing porosity and strength. The addition of nanomaterials improves mechanical performance and introduces self-sensing capabilities, expanding GFC’s potential for advanced industrial applications. Fibers further enhance toughness and crack resistance, broadening its usability. Machine learning algorithms offer promising tools for optimizing GFC formulations and structural designs, enabling the development of high-performance, sustainable materials tailored to specific engineering needs. By systematically evaluating the effects of alternative materials, nanotechnology, fibers, and curing conditions, this review highlights the potential of GFC to revolutionize sustainable construction. Finally, GFC represents a transformative advancement in eco-friendly construction materials. Its integration of waste-derived binders, nanomaterials, and advanced optimization techniques positions it as a key solution for sustainable infrastructure, balancing performance, durability, and environmental responsibility.

Aging affected structural components negatively impact the lateral load–resisting capacity and fragility of highway bridges during seismic shaking. Until now, a majority of existing literature on corrosion deterioration of highway bridges assumed a constant or time-varying degradation rate that is independent of initial climate conditions or their variations along service life. Recent research on climate change indicates a time-dependent shift in atmospheric temperature and relative humidity due to global warming. These effects, compounded along the service life with heightened chloride ingress, require careful consideration for bridges located in regions near marine sources and characterized by moderate to high seismicity. The present study proposes a multihazard framework to evaluate seismic fragility of highway bridges considering earthquake hazard, aging effects, and global warming due to climate change. The framework, when demonstrated on case-study multispan continuous steel girder bridges in the central and southeastern United States, reveals that potential climate change effects are likely to further exacerbate the seismic performance of aging bridge structures.

To address the demands of the low-carbon era, this study proposed a solution by using eggshell powder (ESP), fly ash, and ground granulated blast furnace slag together with alkaline solution in the preparation of lightweight geopolymer foam concrete (LWGFC). The aim of this study is to investigate the influence of replacing precursor materials with 5–20% ESP on the expansion behavior, physical, mechanical characteristics, and thermal conductivity of LWGFC. Additionally, the study examines the effect of varying the silicate modulus (SiO₂/Na₂O ratios of 1.0, 1.25, and 1.5) on the properties of LWGFC. Incorporating ESP from 5% to 20% with a constant SiO₂/Na₂O ratio reduced the initial setting time, while a high SiO₂/Na₂O ratio controlled the setting time and expansion volume. The high SiO₂/Na₂O ratio decreased the porosity and enhanced the compressive strength of the LWGFC but increased the thermal conductivity. The inclusion of more than 10% ESP content negatively affected compressive strength; however, a high SiO₂/Na₂O ratio can mitigate this detrimental effect. The thermal conductivity of optimal-content ESP mixtures with a SiO₂/Na₂O ratio of 1.0 was about 0.84 W/m·K, which is 2.1% lower than mixtures with a ratio of 1.25 and 18.6% lower than those with a ratio of 1.5. High-content ESP mixtures had a density of 1707 kg/m³, 0.97 W/m·K, and a compressive strength of 18.9 MPa at a low SiO₂/Na₂O ratio. Finally, the inclusion of ESP in the LWGFC, along with the use of an appropriate silicate modulus, resulted in improved strength development while decreasing porosity.

Structural lightweight concrete is preferred over traditional concrete due to its ability to reduce the dead load, minimize the size of load-bearing structural members, and provide more economical solutions for foundation deteriorations. This research sheds light on sustainable lightweight concrete using waste crushed clay bricks (CCB) as a lightweight aggregate. To reduce micro-crack propagation of the developed concrete, two types of fiber were implemented and investigated. Steel fibers (SF) with amounts of 0.5% and 1.0% by volume of concrete, and polypropylene fibers (PPF) with amounts of 0.1% and 0.2% by volume of concrete, were employed. Five reinforced concrete beams were made and tested in order to precisely evaluate the structural behavior of the proposed lightweight CCB concrete. Additionally, ABAQUS software for nonlinear finite element analysis has been utilized to simulate the tested beams and compare the numerical model predictions with the experimental findings. The findings revealed that the addition of SF and PPF exhibited a notable influence on enhancing the mechanical characteristics of lightweight CCB concrete. Adding 0.2% PPF increased the ultimate load and deformation capacity at failure by approximately 16% and 24%, respectively. Furthermore, after 28 days, the addition of 0.5% and 1.0% SF enhanced the compressive strength by around 11.7% and 17.6%, respectively. Moreover, a significant level of consistency between the results obtained from the numerical model and the experimental findings was observed. In general, the use of SF and PPF in CCB concrete successfully produced high-quality lightweight concrete with interesting results for use in reinforced concrete beams.

Accurate prediction of the unconfined compressive strength (UCS) of geopolymer-stabilized clayey soil is critical for geotechnical engineering. Conventional regression algorithms and even advanced machine learning approaches such as artificial neural networks often struggle to fully capture the highly non-linear interactions among soil properties and geopolymer mix parameters while maintaining computational efficiency and interpretability on limited datasets. To address these challenges, this investigation proposes a novel hybrid predictive framework based on a sector fruit fly optimization algorithm–enhanced extreme learning machine (SFOA-ELM) for improved UCS estimation. The model used 270 experimental records with 8 input variables (i.e., liquid limit, plasticity index, GGBS content, molarity). The baseline ELM achieved moderate performance (training R² = 0.9432, testing R² = 0.9054; RMSE = 1.55 MPa and 1.91 MPa, respectively). The predictive accuracy of the standard FOA-ELM improved to an R² of 0.9768 (training) and 0.9318 (testing), with RMSE values of 0.99 MPa (training) and 1.62 MPa (testing). The performance of the SFOA-ELM model improved to have excellent predictive performance with an R² value of 0.9775 (training) and 0.9446 (testing), and an RMSE value of 0.98 MPa (training) and 1.462 MPa (testing), respectively. The SFOA-ELM model reduced the testing MSE by 42% compared to ELM and by 19% compared to FOA-ELM. A SHAP analysis suggested that GGBS content, plasticity index and liquid limit were the most important for prediction. The statistical validations and residual analysis confirmed SFOA-ELM’s ability to generalize effectively and exhibit a tight distribution of errors. These findings show that the SFOA-ELM framework offers a robust, computationally efficient, and interpretable tool for geopolymer mix design and UCS prediction, providing practical decision-support for geotechnical engineering applications.