Search Results (2,272)

The urgent demand for environmentally responsible construction practices has intensified interest in geopolymer concrete mixtures, which offer low-carbon alternatives to conventional Portland cement by enabling the valorization of industrial by-products. Since the large volume of waste generated by mining activities represents a significant environmental liability, this research aimed to utilize the alkali activation technique in mixtures of soil and bauxite residue, commonly known as red mud (RM), for application in green construction. All raw materials were characterized based on their physical and chemical properties. To evaluate the influence of waste content on the mechanical behavior of the geopolymers, specimens were prepared with soil contents ranging from 70% to 100% and RM dosages ranging from 0% to 30%. These mixtures underwent compaction tests using the standard Proctor energy method to determine maximum dry density and optimum moisture content. Using the optimal mixture compositions, specimens were prepared for unconfined compressive strength (UCS) tests, with NaOH at a concentration of 6 mol/L added as an activator. The experimental tests provided UCS results ranging from 2.23 MPa to 3.05 MPa. X-ray diffraction (XRD) analyses were performed on raw materials and mixtures containing 70% soil and 30% waste to assess changes in mineralogical compositions due to waste incorporation. The results confirmed the potential of alkali activation for stabilizing mixtures of soil and RM for sustainable construction. Full article

This paper reviews various emerging alternative SCMs derived from minerals and biomass sources, industrial byproducts, and underutilized waste streams. The paper compiles and evaluates physicochemical properties, reaction mechanisms in cementitious systems, resource availability, supply chain dynamics, technology readiness, the impact on concrete performance, and environmental and cost factors for each candidate SCM. Specifically, the review examines wood ash from bioenergy plants, volcanic and sedimentary natural pozzolans, and construction and demolition waste. This includes recycled concrete fines, asphalt plants’ rock dust (baghouse fines), aggregate production fines, and post-consumer waste, particularly municipal solid waste incinerator ash and wastewater sludge ash. Additionally, the paper explores innovative additives such as cellulose and chitin nanomaterials and calcium–silicate–hydrate nanoseeds to address challenges of slower strength development and rheological changes. The key contribution of this review is a multifactor framework for assessing alternative SCMs, emphasizing availability, supply chain, market readiness, and environmental performance, combined with an engineering performance review. Full article

Rheological properties are essential to ultra-high performance concrete (UHPC), and it is necessary to guarantee a relatively lower viscosity to avoid fiber segregation and mechanical degradation. In this study, an innovative physical-chemical integrated approach, namely the simultaneous use of fly ash microbeads and a modified low-viscosity polycarboxylic acid superplasticizer (JN-PCE), was proposed to regulate the rheological performance of UHPC containing industrial by-products. The effect of varying microbead dosage, different superplasticizers, and their combined influence on the rheological parameters, mechanical characteristics, and microstructure evolution were systematically explored in this study. The results demonstrated that the addition of 1.5% JN-PCE led to significant improvements in the UHPC properties including a flow expansion of 775 mm, a static yield stress of 376.9 Pa, a dynamic yield stress of 188.01 Pa, a plastic viscosity of 160.87 Pa·s, and a 28-day compressive strength of 136.6 MPa. Moreover, when a combination of 10% microbeads and 1.5% JN-PCE was used, the UHPC exhibited a flow expansion of 730 mm, a static yield stress of 693.5 Pa, a dynamic yield stress of 542.90 Pa, a plastic viscosity of 202.40 Pa·s, and a 28-day compressive strength of 142.1 MPa. This study thus offers valuable insights into optimizing low-viscosity UHPC formulations using eco-friendly additives for construction applications. Full article

Coal gangue, an industrial byproduct of coal mining, was traditionally utilized in concrete production as a coarse aggregate. However, recent advancements have expanded its application by processing it into fine powder for use as a supplementary cementitious material (SCM), partially replacing cement. This approach not only enhances the sustainable reuse of coal gangue but also contributes to reducing cement consumption and associated carbon emissions. Nevertheless, the incorporation of coal gangue may adversely affect the mechanical strength and long-term durability of concrete. This review provides a systematic analysis of recent research on coal gangue-modified concrete. It begins by classifying the functional roles of coal gangue in concrete mixtures, followed by a critical evaluation of its impact on mechanical properties and durability—both as an aggregate an as a mineral admixture. When 30% of the aggregate is replaced with activated coal gangue, the average compressive strength of concrete increases by 15%. When coal gangue replaces less than 20% of the cement, the compressive strength of concrete can reach 95% of the reference strength. Second, the review evaluates the modification effects of various mineral admixtures, elucidating their mechanisms for enhancing mechanical properties and durability in coal gangue-based concrete. Finally, it examines the underlying interaction mechanisms between these admixtures and coal gangue, while identifying key future research directions for optimizing admixture formulations. By providing a comprehensive and critical analysis of current research, this paper serves as a valuable reference for developing high-performance coal gangue concrete with increased substitution rates and tailored admixture systems. Ultimately, this work advances the design of sustainable, low-cement concrete using industrial byproducts, enabling performance-driven applications and supporting next-generation green construction materials. Full article

Reliable anchorage of cast-in-place headed bolts in unreinforced concrete is vital in structural and industrial applications, where inaccurate strength predictions can compromise safety and efficiency. This study develops and validates an elastic–plastic concrete model within LS-DYNA to assess the tensile performance of headed anchors with varying embedment depth-to-diameter ratios. A parametric analysis is conducted, considering different concrete strengths, anchor sizes, and steel yield strengths. The results show notable deviations from the Concrete Capacity Design (CCD) method, particularly under high-strength concrete and reduced embedment ratios. The CCD method underestimates capacity at 30–40 MPa and overestimates it at 20 MPa. A correction coefficient is proposed to improve embedment depth estimation. The findings offer practical guidance for safer and more accurate anchor design. Full article

In a few years, most investment firms will deploy Generative AI (GenAI) and large language models (LLMs) for reduced-cost stock trading decisions. If GenAI-run investment decisions from most firms are heavily coordinated, they could all give a “sell” signal simultaneously, triggering market crashes. Likewise, simultaneous “buy” signals from GenAI-run investment decisions could cause market bubbles with algorithmically inflated prices. In this way, coordinated actions from LLMs introduce systemic risk into the global financial system. Existing risk analysis for GenAI focuses on endogenous risk from model performance. In comparison, exogenous risk from external factors like macroeconomic changes, natural disasters, or sudden regulatory changes, is understudied. This research fills the gap by creating a framework for measuring exogenous (systemic) risk from LLMs acting in the stock trading system. This research develops a concrete, quantitative framework to understand the systemic risk brought by using GenAI in stock investment by measuring the covariance between LLM stock price predictions across three industries (technology, automobiles, and communications) produced by eight large language models developed across the United States, Europe, and China. This paper also identifies potential data-driven technical, cultural, and regulatory mechanisms for governing AI to prevent negative financial and societal consequences. Full article

With rapid industrialization, large quantities of industrial solid waste are generated annually. In Panzhihua, China, approximately 300,000 tons of limestone mine dust filter residue (LMDFR) is produced. This study investigates the properties of LMDFR and its potential as a supplementary cementitious material. LMDFR was blended with fly ash (FA) to replace 30% of cement in mortar. Tests were conducted to measure the mortar’s flowability and its compressive and flexural strengths after 7 and 28 days of curing, and XRD, SEM, TG, and DSC analyses were conducted on 28-day specimens. LMDFR primarily comprises ≥95% CaCO₃, with a specific surface area of ~1.3 m²/g and density of 2.694 g/cm³. Mortar flowability increased with LMDFR content, reaching 112.83% when used alone. Flexural strength was largely unaffected, while the 7-day compressive strength significantly improved. However, the 28-day strength decreased when LMDFR was used alone, with a 28-day activity index of 61.10%, compared with 71.52% for FA. A 1:1 blend of LMDFR and FA improved the activity index to 83.18%. Microstructural and thermal results corroborated strength and flowability trends. In conclusion, LMDFR demonstrates promising potential as a supplementary cementitious material in concrete applications. When blended with fly ash at a 1:1 ratio, the composite admixture significantly enhances flowability and early compressive strength while maintaining adequate long-term performance. This synergistic combination not only improves the physical properties of cement mortar but also provides a sustainable solution for the large-scale utilization of industrial solid waste. Full article

Coal gangue concrete (CGC), a recycled cementitious material derived from industrial solid waste, presents both opportunities and challenges for structural applications due to its heterogeneous composition and variable mechanical behavior. This study develops an ensemble learning framework—incorporating XGBoost, LightGBM, and CatBoost—to predict four key constitutive parameters based on experimental data. The predicted parameters are subsequently incorporated into an ABAQUS finite element model to simulate the compressive–bending response of CGC columns, with simulation results aligning well with experimental observations in terms of failure mode, load development, and deformation characteristics. To enhance model interpretability, a hybrid approach is adopted, combining permutation-based global feature importance analysis with SHAP (SHapley Additive exPlanations)-derived local explanations. This joint framework captures both the overall influence of each feature and its context-dependent effects, revealing a three-stage stiffness evolution pattern—brittle, quasi-ductile, and re-brittle—governed by gangue replacement levels and consistent with micromechanical mechanisms and numerical responses. Coupled feature interactions, such as between gangue content and crush index, are shown to exacerbate stiffness loss through interfacial weakening and pore development. This integrated approach delivers both predictive accuracy and mechanistic transparency, providing a reference for developing physically interpretable, data-driven constitutive models and offering guidance for tailoring CGC toward ductile, energy-absorbing structural materials in seismic and sustainability-focused engineering. Full article

Tokamak technology, as the cornerstone of nuclear fusion energy, holds immense potential in achieving efficient plasma confinement and high energy densities. To comprehensively map the rapidly evolving landscape of this field, this study employs bibliometric analysis to systematically examine the research and development trends of tokamak technology from 2014 to 2024. The data are drawn from 7702 academic publications in the Scopus database, representing a global research effort. Additionally, the study incorporates 2299 tokamak-related patents from Google Patents over the same period, analyzing their technological trends to highlight the growing significance of tokamak devices. Using the R language and the Bibliometric package, the analysis explores research hotspots, institutional influences, and keyword evolution. The results reveal a multifaceted global landscape: China leads in publication output, and the United States maintains a leading role in citation impacts and technological innovation, with other notable contributions from Germany, Japan, South Korea, and various European countries. Patent trend analysis further reveals the rapid expansion of tokamak applications, particularly with significant innovations in high-temperature superconducting magnets and plasma control technologies. Nevertheless, the study identifies major challenges in the commercialization process, including plasma stability control, material durability, and the sustainability of long-term operations. To address these, the study proposes concrete future directions, emphasizing international collaboration and interdisciplinary integration. These efforts are crucial in accelerating tokamak commercialization, thereby providing a strategic roadmap for researchers, policymakers, and industry stakeholders to advance the global deployment of clean energy solutions. Full article

Phosphates are key pollutants involved in the eutrophication of water bodies, creating the need for efficient and low-cost strategies for their removal in order to meet environmental quality standards. This study presents a comparative thermodynamic evaluation of phosphate ion adsorption from aqueous solutions using two sustainable and readily available materials: autoclaved aerated concrete (AAC) and pumice stone (PS). Batch experiments were conducted under acidic (pH 3) and alkaline (pH 9) conditions to determine equilibrium adsorption capacities, and kinetic experiments were carried out for the best-performing adsorbent. Adsorption data were fitted to the Langmuir and the Freundlich isotherm models, while kinetic data were evaluated using pseudo-first-order and pseudo-second-order models. The Freundlich model showed the best correlation (R² = 0.90 − 0.97), indicating the heterogeneous nature of the adsorbent surfaces, whereas the Langmuir parameters suggested monolayer adsorption, with maximum capacities of 1006.69 mg/kg for PS and 859.20 mg/kg for AAC at pH 3. Kinetic results confirmed a pseudo-second-order behavior, indicating chemisorption as the main mechanism and the rate-limiting step in the adsorption process. To the best of our knowledge, this is the first study to compare the thermodynamic performance of AAC and PS for phosphate removal under identical experimental conditions. The findings demonstrate the potential of both materials as efficient, low-cost, and thermodynamically favorable adsorbents. Furthermore, the use of AAC, an industrial by-product, and PS, a naturally abundant volcanic material, supports resource recovery and waste valorization, aligning with the principles of the circular economy and sustainable water management. Full article

This study investigates the mechanical performance and failure mechanisms of large-span, cast-in situ hollow-core floor slabs with square-box core molds under vertical loading. A combination of in situ tests and refined numerical simulations was used to investigate the slab’s behavior. An 8 m × 8 m hollow slab from the Xinluzhou Industrial Park in Hefei, China, was subjected to five-stage cyclic loading up to 9.0 kN/m² using a distributed water tank system. Real-time strain monitoring showed that the slab remained within the elastic range, exhibiting a linear strain-load relationship and bidirectional bending stiffness, with less than 5% deviation between the X and Y directions. Finite element analysis, incorporating a concrete plastic damage model and a bilinear steel model, replicated the experimental stress distribution, with errors of less than 6.9% for reinforcement and 8.8% for concrete. The simulation predicted an ultimate load-bearing capacity of 27.2 kN/m², with initial failure indicated by diagonal cracks at the column capital edges, followed by flexural cracks at the slab mid-span. These findings clarify the bidirectional bending behavior and stress redistribution, characterized by “banded gradient” and “island-shaped” stress zones. This study provides valuable insights and design optimization strategies to improve the structural performance and safety of hollow-core floor slabs in high-rise buildings. Full article

Concrete is fundamental to modern construction, comprising 70% of all building materials and supporting an industry projected to reach $15 trillion by 2030. Predicting compressive strength—a key factor for structural safety and resource efficiency—remains a challenge, as conventional models often fail to capture the dynamic, time-dependent nature of strength development across mix compositions and curing intervals. This study proposes an integrated modeling framework using Markov Chain analysis and regression, validated on 135 samples from 27 mixtures with varying proportions of Portland Cement (PC), Fly Ash (FA), and Blast Furnace Slag (BFS) over curing periods from 3 to 180 days. The Markov Chain framework, integrated with regression analysis, models strength transitions across 10 states (9–42 MPa), with high accuracy (R² = 0.977, standard error = 3.27 MPa). Curing time (β = 0.079), PC proportion (β = 0.063), and BFS proportion (β = 0.051) are identified as key drivers, while higher FA content (β = 0.019) enhances long-term durability. Model validation using Coefficient of Variation (CoV = 15.57%) and mean absolute error confirms robust and consistent performance across mix designs. The framework supports tailored mix strategies—PC for early strength, BFS for durability, FA for sustainability—empowering engineers to optimize mix selection and curing strategies for efficient and durable concrete applications. Full article

This study focuses on buildings in the Chinese–Baroque Historic Shopping District in Harbin. In view of global climate change and high carbon emissions from the construction industry, this study aims to quantify carbon emissions during the decoration process and explore low-carbon decoration strategies that suit the local characteristics. This research adopts a four-stage framework of “data collection–quantitative analysis–strategy design–verification and optimization” and integrates Life Cycle Assessment (LCA) and multi-objective optimization theory. Data are collected through questionnaires and field investigations, and simulations and analyses are carried out using Grasshopper and Honeybee. The results show that there are differences in carbon emissions between different decoration schemes. The chosen scheme of raw concrete and paint results in relatively low carbon emissions over the 10.12-year usage cycle. Based on this, design strategies such as extending the service life of decorations, rationally renovating windows, and preferentially selecting local low-carbon materials are proposed and applied to practical projects. This study not only fills a gap in the research on the low-carbon renovation of historical commercial blocks from the perspective of LCA but also provides practical solutions for the sustainable development of historical shopping blocks in Harbin and similar regions, promoting the low-carbon transformation of cities. Full article

Concrete compressive strength is a critical property for structural performance and construction scheduling. Traditional non-destructive testing (NDT) methods, such as rebound hammer and ultrasonic pulse velocity, offer limited reliability and resolution, particularly at early ages. This study presents an AI-powered structural health monitoring (SHM) framework that integrates multi-type and multi-position piezoelectric (PZT) sensor networks with machine learning for in situ prediction of concrete compressive strength. Signals were collected from various PZT types positioned on the top, middle, bottom, and surface sides of concrete cubes during curing. A series of machine learning models were trained and evaluated using both the full and selected feature sets. Results showed that combining multiple PZT types and locations significantly improved prediction accuracy, with the best models achieving up to 95% classification accuracy using only the top 200 features. Feature importance and PCA analyses confirmed the added value of sensor heterogeneity. This study demonstrates that multi-sensor AI-enhanced SHM systems can offer a practical, non-destructive solution for real-time strength estimation, enabling earlier and more reliable construction decisions in line with industry standards. Full article

The construction industry faces significant challenges due to the physically demanding and hazardous nature of tasks such as manual filling of expansion joints with mastic. Automating mastic filling presents additional difficulties due to the variability of mastic density with temperature, which creates a constantly changing environment that requires adaptive control strategies to ensure consistent application quality. This pilot study focuses on testing a new human–robot collaborative approach for automating the mastic application in concrete expansion joints. The system learns the task from demonstrations performed by expert construction operators teleoperating the robot. This study evaluates the usability, efficiency, and adoption of robotic assistance in joint-filling tasks compared to traditional manual methods. The study analyzes execution time and joint quality measurements, psychophysiological signal analysis, and post-task user feedback. This multi-source approach enables a comprehensive assessment of task performance and both objective and subjective evaluations of technology acceptance. The findings underscore the effectiveness of automated systems in improving safety and productivity on construction sites, while also identifying key areas for technological improvement. Full article