Low-carbon high-strength engineered geopolymer composites (HS-EGC) with full-volume fly ash precursor: Role of silica modulus

Journal of CO2 Utilization, Год журнала: 2024, Номер 88, С. 102948 - 102948

Опубликована: Окт. 1, 2024

Язык: Английский

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Life cycle assessment of carbon emissions for cross-sea tunnel: A case study of Shenzhen-Zhongshan Bridge and Tunnel in China

Case Studies in Construction Materials, Год журнала: 2024, Номер 21, С. e03502 - e03502

Опубликована: Июль 9, 2024

Due to significant population concentration and capital influx in Guangdong-Hong Kong-Macao Greater Bay Area, the construction of cross-sea tunnels with consumption various resources materials, has been frequently witnessed. However, there is a lack knowledge regarding how carbon emissions transportation infrastructure are generated across its life-cycle stages. This study proposes life cycle assessment (LCA) approach for quantifying exploring reduction potentials case world-renowned tunnel project Area. The results find that this contributes approximately 849 kilotons CO2eq an emission intensity 1.1 per meter. materialization stage largest contributor (474.9 CO2eq), followed by service (248.3 CO2eq, accounting 29.2 %). Some raw materials can be offset using recycled materials. discarded concrete, block, stone, sand, occupying over 90 % total waste weight could achieve 93.5 potentially. It provides opportunity reveal engineering details world-class super complex infrastructure. makes one first attempts quantify infrastructure, which enriches foundational dataset environmental impact emerging field. findings provide scientific references formulating targeted low-carbon strategies different

Язык: Английский

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Targeted and Highly Effective Technique for Healing the Aggregate-Asphalt Interface

Colloids and Surfaces A Physicochemical and Engineering Aspects, Год журнала: 2025, Номер unknown, С. 136165 - 136165

Опубликована: Янв. 1, 2025

Язык: Английский

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The Evaluation and Prediction of Flame Retardancy of Asphalt Mixture Based on PCA-RBF Neural Network Model

Materials, Год журнала: 2024, Номер 17(13), С. 3298 - 3298

Опубликована: Июль 4, 2024

Warm mix flame retardant asphalt mixture can reduce the energy dissipation and harmful gas emissions during pavement construction, as well mitigate adverse effects of road fires. For this, this paper studies design performance a modified with combination warm agent retardant, retardancy are evaluated. Additionally, prediction model based on radial basis function (RBF) neural network is established. On basis, principal components analysis (PCA) used to analyze most significant evaluation indicators affecting retardancy, finally, three-dimensional finite element developed loading structure. The results show that compared virgin mixture, shows reduction in mixing compaction temperatures by approximately 12 °C. high-temperature improved, while low-temperature moisture stability slightly decrease, but its significantly enhanced. RBF revealed established has high accuracy, allowing for precise retardancy. Finally, PCA identified combustion time effect displacements fire were lower than all directions under loading.

Язык: Английский

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Steel slag aggregate property improvement in cold mixed asphalt mixtures through surface modification treatment

Journal of Cleaner Production, Год журнала: 2024, Номер 477, С. 143889 - 143889

Опубликована: Окт. 1, 2024

Язык: Английский

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Multiscale enhancement mechanism of low-temperature performance for degraded recycled waste rubber asphalt binders: MD simulation and microscopic investigation

Journal of Cleaner Production, Год журнала: 2024, Номер 461, С. 142647 - 142647

Опубликована: Май 22, 2024

Язык: Английский

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Comparative study on the prediction of the unconfined compressive strength of the one-part geopolymer stabilized soil by using different hybrid machine learning models

Case Studies in Construction Materials, Год журнала: 2024, Номер 21, С. e03439 - e03439

Опубликована: Июль 26, 2024

With the development of green, low-carbon, and sustainable economic systems, issues high pollution energy consumption in construction materials have become increasingly prominent. This study focuses on adopting one-part geopolymer (OPG) soil stabilization for underground engineering, which exhibits environmental low-carbon advantages. The unconfined compressive strength (UCS) serves as a crucial parameter assessing stabilized soil's performance. However, it is necessary to conduct large number experiments, inducing costs time consumption. In this study, one multiple linear regression model, Decision Tree (DT) five ensemble machine learning (ML) models (i.e. Random Forest [RF], Extra [ET], Gradient Boosting [GB], [GBDT], Extreme [XGBoost]), hybrid those single with Particle Swarm Optimization (PSO) PSO-RF, PSO-ET, PSO-GB, PSO-GBDT, PSO-XGBoost) were adopted compared achieve better prediction UCS OPG-stabilized soil. Furthermore, interpretable method including SHAP PDP (1D 2D), was employed investigate precise mechanisms by input parameters influenced output label. results revealed that model delivered lowest accuracy, PSO-XGBoost PSO-ET exhibited best performance R2 value 0.9964 0.9928, respectively. addition, Curing exerted most significant impact UCS, followed FA/GGBFS, Molarity, Water/Binder, NaOH/Precursor. Compared method, offered more intuitive approach reveal relationship between inputs output. outcome shed new light application ML engineering.

Язык: Английский

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A probabilistic investigation of long-term field performance of warm mix asphalt pavements

Case Studies in Construction Materials, Год журнала: 2024, Номер 21, С. e03460 - e03460

Опубликована: Июль 4, 2024

Warm-mix asphalt (WMA) has been gaining popularity in sustainable road engineering due to its reduced environmental impacts. However, uncertainties remain regarding long-term pavement performance compared hot mix (HMA). Especially, the critical factors influencing field of WMA are yet be determined. Hence, this study analyzes overlaid with WMA, using data from Specific Pavement Studies 10 (SPS-10) (LTPP) program. The evaluation includes 58 sections nine projects across United States, encompassing two types (foaming processing and chemical additive) their corresponding HMA control sections. Performance indicators such as alligator cracking, longitudinal cracking wheel path, transverse rutting, roughness (measured by international index, IRI) were considered. Per-state per-climate region Bayesian multilevel models built account for site- region-level heterogeneity. results demonstrated that performed equally well counterparts under similar conditions, indicating potential widespread application meeting both standards conservation requirements. Also, technologies did not differ performance, thus was a dominant factor deciding technology types. Furthermore, proved highly effective varying construction sites regions.

Язык: Английский

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Enhancing Mechanical Properties and Crack Resistance of High-strength SHCC/ECC for Durable Transportation through Ethylene-Vinyl Acetate Polymer Modification

Case Studies in Construction Materials, Год журнала: 2024, Номер unknown, С. e03878 - e03878

Опубликована: Окт. 1, 2024

Язык: Английский

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Designing Climate Change (CC)-Resilient Asphalt Pavement Structures: A Comprehensive Literature Review on Adaptation Measures and Advanced Soil Constitutive Models

Results in Engineering, Год журнала: 2024, Номер 24, С. 103648 - 103648

Опубликована: Дек. 1, 2024

Язык: Английский

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