Case Studies in Construction Materials最新文献_第9页

Clay activation through CO2-derived oxalic acid for advancing its reactivity and strength of limestone calcined clay cement (LC3) 二氧化碳衍生草酸活化粘土提高石灰石煅烧粘土水泥（LC3）的反应性和强度

IF 6.6 2区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Case Studies in Construction Materials

Pub Date : 2025-12-11 DOI: 10.1016/j.cscm.2025.e05684

Miral Fatima , Mounir Ltifi , Khuram Rashid , Idrees Zafar

The widespread deployment of limestone calcined clay cement (LC³) is constrained by its dependence on high-grade kaolinitic clays. Abundant, low-grade clays often exhibit poor pozzolanic reactivity and require tailored activation strategies. This study proposes a novel clay activation approach using oxalic acid, an organic acid producible through electrochemical CO₂ reduction utilizing a waste carbon stream, for the development of LC³. Three activation regimes were examined: thermal activation (TH), thermal followed by oxalic acid immersion (TI), and co-calcination with oxalic acid (CT). Comprehensive characterization (XRF, QXRD, R³) reveals that the CT method uniquely enhances reactivity by promoting selective leaching of Fe₂O₃ and enriching Al₂O₃ content, while also inducing mineralogical transitions from quartz to more reactive phases like cristobalite. The R³ test confirmed CT’s superiority, showing the highest bound water content (14.4 %) and showed a significant correlation with strength at all ages (correlation co-efficient ranging from 0.89 to 0.94). In LC³ binders, CT-activated clay yielded a more balanced hydration phase assemblage, accelerating early-age hydration. This translated directly to superior mechanical performance; LC³-CT blends nearly met the ASTM strength criterion (i.e., 42.5 MPa) benchmark at 28 days (within 1 % deviation), significantly outperforming LC³-TH blends (10 % deficit). Despite the added acid, the LC³-CT system maintains a compelling environmental advantage, achieving 21–23 % reductions in CO₂ emissions compared to OPC, alongside cost savings of 8–11 %. Results establish CT activation as a technically superior and environmentally sustainable pathway for valorizing low-grade clays. By simultaneously enhancing reactivity and leveraging CO₂ utilization, this approach strengthens the foundation for next-generation, low-carbon cement technologies.

石灰石煅烧粘土水泥（LC3）的广泛应用受到其对高岭石粘土的依赖的限制。丰富的低品位粘土通常表现出较差的火山反应性，需要定制活化策略。本研究提出了一种新的粘土活化方法，利用草酸（一种有机酸，通过利用废碳流电化学CO2还原产生）来开发LC3。研究了三种活化方式：热活化（TH）、热后草酸浸（TI）和草酸共烧（CT）。综合表征（XRF， QXRD, R3）表明，CT方法通过促进Fe2O3的选择性浸出和富集Al2O3含量，同时诱导矿物从石英向方石英等更具反应性的相转变，从而独特地提高了反应性。R3测试证实了CT的优势，显示出最高的束缚水含量（14.4 %），并且在所有年龄段都与强度具有显著的相关性（相关系数范围为0.89 ~ 0.94）。在LC3粘合剂中，ct活化粘土产生了更平衡的水化相组合，加速了早期水化。这直接转化为优越的机械性能；LC3-CT共混物在28天内几乎达到ASTM强度标准（即42.5 MPa）基准（偏差在1 %以内），显著优于LC3-TH共混物（偏差为10 %）。尽管添加了酸，LC3-CT体系仍保持着令人信服的环境优势，与OPC相比，二氧化碳排放量减少了21 - 23% %，成本节省了8 - 11% %。结果表明，连续油管活化是一种技术上优越、环境上可持续的低品位粘土活化途径。通过同时提高反应性和利用二氧化碳，这种方法为下一代低碳水泥技术奠定了基础。

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引用次数: 0

Optimizing cement-treated recycled concrete aggregates for road bases using secondary additive 优化水泥处理再生混凝土集料的道路基层使用二次添加剂

IF 6.6 2区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Case Studies in Construction Materials

Pub Date : 2025-12-11 DOI: 10.1016/j.cscm.2025.e05687

Zainul Abedin Khan , Umashankar Balunaini , Nhu H.T. Nguyen , Susanga Costa

This study investigates the feasibility of a secondary additive treated recycled concrete aggregates (RCA) to reduce the required cement content for the construction of pavement base/subbase layers. The effectiveness of secondary additive was assessed based on extensive studies involving strength, durability and microstructural analysis considering different cement contents (2 %, 3 %, 4 %, and 5 % by weight of aggregates) and a silica-rich secondary additive (2 % and 4 %, by weight of cement). The addition of a secondary additive significantly reduced the required cement content (from 7 % to 5 %) to meet the minimum 7-day unconfined compressive strength criteria for base layers. Accordingly, 5 % cement content and 4 % additive contents are proposed in the study for base layer applications. The weight loss percentage of treated RCA specimens prepared with this optimal mix (3.1 %) is found to be less than the maximum permissible value (14 % after 12 wet-dry cycles). The 7-day cured specimens prepared with this mix showed a significantly high resilient modulus value (667 MPa). Additionally, the designed pavement section incorporating 5 % cement and 4 % additive-treated layers exhibited a 13 % reduction in pavement crust thickness compared to the non-treated pavement section. Treated RCA satisfied the requirements of pavement base/subbase layer in accordance with American, Australian, and Indian road standards and can be a viable solution towards sustainable road infrastructure. The findings demonstrate that secondary additive treated RCA can be effectively utilized in road pavement base/subbase layers with lower cement dosage and promoting sustainable road construction using recycled waste materials.

本研究探讨了二级添加剂处理再生混凝土骨料（RCA）的可行性，以减少路面基层/次基层施工所需的水泥含量。二次添加剂的有效性评估基于广泛的研究，包括强度、耐久性和微观结构分析，考虑不同的水泥含量（按骨料重量计2%、3%、4%和5% %）和富含二氧化硅的二次添加剂（按水泥重量计2%和4% %）。二级添加剂的加入显著降低了所需的水泥含量（从7 %降至5 %），以满足基层最低7天无侧限抗压强度标准。据此，在基层应用研究中提出水泥掺量为5 %，添加剂掺量为4 %。用这种最佳混合物制备的处理过的RCA试样的失重率（3.1 %）小于最大允许值（12个干湿循环后14 %）。用该混合料制备的7天固化试样显示出显著高的弹性模量值（667 MPa）。此外，与未处理的路面部分相比，含有5 %水泥和4 %添加剂处理层的设计路面部分的路面外壳厚度减少了13 %。处理后的RCA满足美国、澳大利亚和印度道路标准对路面基层/次基层的要求，是可持续道路基础设施的可行解决方案。研究结果表明，经二次添加剂处理的RCA可有效地用于低水泥用量的路面基层/亚基层，促进再生废弃物的可持续道路建设。

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引用次数: 0

Strength characterisation of fly ash blended 3D printed concrete enhanced with explainable machine learning 粉煤灰混合3D打印混凝土的强度特征与可解释的机器学习增强

IF 6.6 2区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Case Studies in Construction Materials

Pub Date : 2025-12-11 DOI: 10.1016/j.cscm.2025.e05682

Imtiaz Iqbal , Waleed Bin Inqiad , Tala Kasim , Svetlana Besklubova , Melak Mohammad Adil , Mujib Rahman

This study investigates the performance of 3D printed concrete incorporating fly ash as a partial cement replacement and develops a machine learning model to predict its mechanical properties. A total of 28 mixtures were prepared with varying fly ash contents (5–15 %), water-to-binder ratios, and superplasticiser dosages. Of these, seven mixes met the requirements for printability in terms of flowability, extrudability, and buildability. Experimental tests were conducted to evaluate compressive strength, flexural strength, water absorption, and sorptivity. Results showed that mixes with 5 % and 7.5 % fly ash achieved improved strength and durability, whereas higher fly ash levels reduced early-age performance due to clinker dilution and slower pozzolanic activity. Microstructural analyses confirmed the presence of C–S–H, portlandite, and ettringite, with fly ash contributing to pore refinement and matrix densification. To enhance predictive capability, a TPE-optimised Extreme Gradient Boosting (TPE-XGB) model was developed using data obtained from laboratory testing. The model achieved excellent accuracy (R² > 0.997) in predicting compressive and flexural strength. A graphical user interface integrating SHAP visualisation was created to provide transparent predictions, supporting practical implementation. The findings highlight the potential of fly ash to improve the sustainability of 3D printed concrete at optimised dosages and demonstrate the value of interpretable machine learning tools in mix design optimisation.

本研究调查了3D打印混凝土的性能，其中掺入粉煤灰作为部分水泥替代品，并开发了一个机器学习模型来预测其力学性能。共制备了28种不同粉煤灰含量（5-15 %）、水胶比和高效增塑剂用量的混合物。其中，7种混合物在流动性、可挤压性和可构建性方面满足印刷性要求。进行了抗压强度、抗折强度、吸水率和吸附性的实验测试。结果表明，掺5% %和7.5% %粉煤灰的混合料的强度和耐久性都有所提高，而掺量较高的粉煤灰由于熟料稀释和火山灰活性降低而降低了早期性能。显微结构分析证实了C-S-H、波特兰铁矿和钙矾石的存在，粉煤灰有助于孔隙细化和基质致密化。为了提高预测能力，利用实验室测试数据开发了tpe优化的极限梯度增强（TPE-XGB）模型。该模型在预测抗压和抗弯强度方面具有很好的精度（R²> 0.997）。创建了一个集成了SHAP可视化的图形用户界面，以提供透明的预测，支持实际实现。研究结果强调了粉煤灰在优化剂量下提高3D打印混凝土可持续性的潜力，并展示了可解释的机器学习工具在配合比设计优化中的价值。

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引用次数: 0

A deep learning framework for microstructural analysis of nano-modified cementitious composites using metal intrusion and BSE imaging 基于金属侵入和BSE成像的纳米改性胶凝复合材料微观结构分析的深度学习框架

IF 6.6 2区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Case Studies in Construction Materials

Pub Date : 2025-12-11 DOI: 10.1016/j.cscm.2025.e05694

Zixuan Yu , Shuai Liang , Tong Liu , Yifei Ma , Yining Lin , Yuan Gao , Yanming Liu , Jinpeng Wang

Nanomodification is a promising technology for advancing construction materials toward multifunctionality, intelligence, and sustainability. However, current assessments of its effects on microstructure modification require improvements in objectivity, quantification, and pattern generalization. This study proposes a novel characterization-analysis framework, integrating metal intrusion technology with deep learning to enable expertise-independent extraction and evaluation of microstructure characteristics. Using image-based algorithms, the optimization of nanopore structure in cementitious waste rockfill material is first described. Based on a dataset comprising over 4000 microscopic images, the proposed deep learning model achieved a maximum 82.0 % accuracy at a 39.7 μm × 39.7 μm observation scale in distinguishing microstructure images of samples with various water-cement ratios and graphene oxide (GO) reinforcement. Porosity and fractal dimension show weak correlation with classification accuracy, suggesting insufficient description of these parameters on microstructure characteristics. The class activation mapping algorithm further revealed that the model consistently prioritized pore structure identification. The deep Taylor decomposition (DTD) algorithm extracted microstructure features that concentrated on the pore distribution near hydration products, where the GO groups exhibited denser and less continuous pore structure. Finally, a coefficient of variation matrix was employed to fuse micropores image data with DTD features data to generate typical pore probability distribution maps. Nanomodified pore structures exhibit discretized spatial distributions and lower overall pore probabilities, especially at low water-cement ratios. The established framework paves the way for intelligent, automated analysis of nanomodified microstructure, offering significant potential for future construction engineering applications of nanomaterials and deep learning technologies.

纳米修饰是一种很有前途的技术，可以推动建筑材料朝着多功能、智能化和可持续性的方向发展。然而，目前对其微观结构变化影响的评估需要在客观性、量化和模式泛化方面进行改进。本研究提出了一种新的表征分析框架，将金属入侵技术与深度学习相结合，实现了与专业知识无关的微观结构特征提取和评估。采用基于图像的算法，对胶凝废石填料的纳米孔结构进行了优化研究。基于超过4000张微观图像的数据集，在39.7 μm × 39.7 μm的观察尺度上，所提出的深度学习模型在区分不同水灰比和氧化石墨烯（GO）增强的样品的微观图像方面达到了82.0 %的最高准确率。孔隙度和分形维数与分类精度相关性较弱，说明孔隙度和分形维数对微观结构特征的描述不够充分。类激活映射算法进一步表明，该模型始终优先考虑孔隙结构识别。深度泰勒分解（deep Taylor decomposition， DTD）算法提取的微观结构特征集中在水化产物附近的孔隙分布上，其中氧化石墨烯基团表现出更致密、更不连续的孔隙结构。最后，利用变异系数矩阵将微孔隙图像数据与DTD特征数据融合，生成典型孔隙概率分布图。纳米修饰的孔隙结构表现出离散的空间分布和较低的总体孔隙概率，特别是在低水灰比时。所建立的框架为纳米修饰微观结构的智能、自动化分析铺平了道路，为纳米材料和深度学习技术的未来建筑工程应用提供了巨大的潜力。

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引用次数: 0

Sustainable cementitious materials from bamboo leaf-derived hydrochar: Process optimization and mechanical performance 竹叶衍生碳氢化合物的可持续胶凝材料：工艺优化和机械性能

IF 6.6 2区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Case Studies in Construction Materials

Pub Date : 2025-12-11 DOI: 10.1016/j.cscm.2025.e05677

Lafiya S.L , Kavitha M.Sambasivam

This research focuses on optimizing the production parameters and comprehensively characterizing bamboo leaf-derived hydrochar synthesized through hydrothermal carbonization (HTC), aiming to advance its application in sustainable cementitious materials (SCM). Response Surface Methodology (RSM) was employed to optimize process parameters. The hydrochar utilized in this study was synthesized under experimentally validated conditions of 205 °C, a 2.67:1 water-to-biomass ratio, and a residence time of 180 min, yielding a maximum solid recovery of 70.12 %. FTIR (Fourier Transform Infrared Spectroscopy), and PXRD (Powder X-ray Diffraction) confirmed deoxygenation, aromatization, and altered hydration phases, while Thermogravimetric Analysis (TGA) demonstrated improved thermal stability with lower volatile decomposition and higher residual mass at elevated temperatures. Brunauer Emmett Teller (BET) surface area analysis showed a transition to meso–macroporous structures with increased pore diameter and volume, beneficial for internal curing and durability enhancement. Mechanical testing revealed that 1.5 % hydrochar replacement improved compressive strength and microstructural integrity, as evidenced by FESEM (Field Emission Scanning Electron Microscopy) and micro-CT analyses, while higher dosages induced porosity and microcracking. The study found a strong predictive relationship in UPV strength correlation(R² = 0.8996). Adding 1.5 % bamboo hydrochar optimizes strength, showing its promise as a sustainable cement alternative and promoting eco-friendly composites. This work highlights bamboo leaves, an underutilized agro residue, as a precursor for carbon-based SCM. The mechanical enhancement and clinker reduction potential of bamboo leaf hydrochar make it a cost-effective and eco-friendly material for sustainable construction.

本研究通过优化生产参数，对水热炭化（HTC）合成的竹叶衍生烃类进行综合表征，旨在推进其在可持续胶凝材料（SCM）中的应用。采用响应面法（RSM）对工艺参数进行优化。实验条件为205℃，水生物质比2.67:1，停留时间180 min，最大固相回收率为70.12 %。FTIR（傅里叶变换红外光谱）和PXRD（粉末x射线衍射）证实了脱氧、芳族化和水化相的改变，而热重分析（TGA）证实了高温下挥发性分解降低和残余质量提高的热稳定性。Brunauer Emmett Teller （BET）表面积分析表明，随着孔径和体积的增加，材料向中-大孔结构过渡，有利于内部固化和耐久性增强。力学测试表明，FESEM（场发射扫描电镜）和micro-CT分析表明，1.5 %的碳氢化合物替代提高了抗压强度和微观结构完整性，而更高剂量的碳氢化合物会导致孔隙和微开裂。研究发现，UPV强度相关性具有较强的预测关系（R²= 0.8996）。添加1.5 %的竹炭优化强度，显示其作为可持续水泥替代品和促进环保复合材料的前景。这项工作突出了竹叶，一种未充分利用的农业残渣，作为碳基SCM的先驱。竹叶碳氢化合物的机械增强和减少熟料的潜力使其成为一种具有成本效益和环保的可持续建筑材料。

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引用次数: 0

Experimental and numerical evaluation of indirect tensile properties of concrete containing macro fibers processed from waste GFRP composites 含废旧玻璃钢复合材料宏纤维混凝土间接拉伸性能的试验与数值评价

IF 6.6 2区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Case Studies in Construction Materials

Pub Date : 2025-12-11 DOI: 10.1016/j.cscm.2025.e05686

M. Qin , P.L. LI , Z.L. Hong , C. Lian , C. WANG , Q.Q. Zou , B. Fu

With the increasingly widespread use of fiber-reinforced polymer (FRP) composites, the volume of non-biodegradable FRP solid waste is accumulating at a high rate, becoming a significant threat to environmental sustainability. To address this, the authors’ group has developed a novel mechanical recycling method to produce “macro fibers” for use as discrete reinforcement in concrete. In this study, an experimental program was conducted to explore the indirect tensile behavior of macro fiber reinforced concrete (MFRC) with an emphasis on the effect of fiber volume fractions and fiber length. Then, the finite element models (FEMs) of the splitting test and four-point bending test were established. The results reveal that the macro fibers have a limited influence of approximately ±5 % on the compressive strength, while increasing the splitting tensile strength and flexural tensile strength of concrete by 52.2 % and 125.0 % respectively. The comparison analysis indicates that the splitting tensile strength and flexural behavior of MFRC are effectively captured by the finite element models, although some discrepancies or overestimations are observed.

随着纤维增强聚合物（FRP）复合材料的日益广泛使用，不可生物降解的FRP固体废物的体积正在快速积累，成为对环境可持续性的重大威胁。为了解决这个问题，作者小组开发了一种新的机械回收方法来生产“宏观纤维”，用于混凝土中的离散钢筋。在这项研究中，进行了一个实验程序，探讨宏观纤维增强混凝土（MFRC）的间接拉伸行为，重点是纤维体积分数和纤维长度的影响。然后，建立了劈裂试验和四点弯曲试验的有限元模型。结果表明，宏观纤维对混凝土抗压强度的影响有限，约为±5 %，而对混凝土的劈裂抗拉强度和弯曲抗拉强度分别提高52.2% %和125.0 %。对比分析表明，有限元模型能较好地反映MFRC的劈裂抗拉强度和抗弯性能，但存在差异或高估。

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引用次数: 0

Calcium leaching resistance and mechanism of early-age mortar with combined incorporation of glass powder and nano-silica under leaching conditions 浸出条件下玻璃粉与纳米二氧化硅复合早龄砂浆抗钙浸出性能及机理研究

IF 6.6 2区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Case Studies in Construction Materials

Pub Date : 2025-12-10 DOI: 10.1016/j.cscm.2025.e05681

Yuyang Liu , Taifeng Zhong , Wei Li , Yeda Li , Liping Jin , Likang Qiu , Liangjun Gu , Yueyue Ling

After application, shotcrete is immediately subjected to erosion by surrounding rock water, and calcium leaching is prone to occur in early-age concrete under leaching conditions. The combined incorporation of glass powder (GP) and nano-silica (NS) is expected to enhance the calcium leaching resistance of shotcrete; however, the improvement effect and mechanism of GP-NS incorporation in early-age concrete under leaching conditions remain unclear. In this study, leaching tests were conducted on early-age mortar specimens with GP-NS incorporation. Calcium leaching resistance was analyzed through measurements of leached calcium, soluble calcium, and leaching depth, and its mechanism was elucidated using TGA, MIP, SEM, and EDS. Finally, compressive strength tests were conducted to determine the optimal mix ratio. Results show that GP-NS incorporation improves the calcium leaching resistance of early-age mortar throughout the 28d leaching process, with the concentration of leached calcium ions in the 40 % GP-9 % NS group being 30.97 % lower than that of the control group at 28d. The dilution and pozzolanic effects of GP and NS act synergistically to reduce soluble calcium sources. Compared with the control group, the 40 % GP-9 % NS group exhibited a 68.66 % reduction in soluble calcium per unit mass at 28d, and the 25 % GP-9 % NS group showed a 6.04 % lower total CH content percentage. The incorporation of NS mitigates the deterioration of pore structure in early-age mortar under leaching conditions induced by GP, thereby blocking calcium ion leaching pathways. The porosity of the 25 % GP-9 % NS group was 7.11 % lower than that of the 25 % GP group. The activity stages of the externally incorporated materials are effectively connected in sequence: NS's secondary hydration occurs from 7 to 14d, while the pozzolanic effect of GP develops from 14 to 28d. The incorporation of NS effectively compensates for the deficiency in early strength of GP alone. The 40 % GP-9 % NS group showed a 34.75 % improvement in compressive strength at 7 d compared to the 40 % GP group. Under leaching conditions, the optimal mix ratio for the GP-NS incorporation is 25 % GP-6 % NS. The calcium leaching process increases mortar porosity, with pore morphology evolution characterized by pore enlargement, pathway extension, and the formation of numerous ink-bottle pores due to local pitting corrosion. These findings provide a reference for mitigating calcium leaching in tunnel shotcrete and promoting the resource utilization of glass solid waste.

喷施后，喷射混凝土立即受到围岩水的侵蚀，在浸出条件下，早期混凝土容易发生钙浸出。玻璃粉（GP）与纳米二氧化硅（NS）的联合掺入有望提高喷射混凝土的抗钙浸出性能；然而，在浸出条件下，GP-NS掺入对早期混凝土的改善效果和机制尚不清楚。本研究对掺入GP-NS的早期砂浆试样进行浸出试验。通过对浸出钙、可溶性钙、浸出深度的测定分析了钙的抗浸出性，并通过热重分析、能谱分析、扫描电镜和能谱分析等手段阐明了其机理。最后进行抗压强度试验，确定最佳配合比。结果表明：在28d的浸出过程中，掺入GP-NS提高了早期砂浆的抗钙浸出能力，40 % GP-9 % NS组在28d时的浸出钙离子浓度比对照组低30.97 %。GP和NS的稀释和火山灰效应协同作用，减少可溶性钙源。与对照组相比，40 % GP-9 % NS组在28d时每单位质量的可溶性钙减少68.66 %，25 % GP-9 % NS组总CH含量百分比降低6.04 %。NS的掺入减轻了GP诱导的早期砂浆在浸出条件下孔隙结构的恶化，从而阻断了钙离子的浸出途径。25 % GP-9 % NS组孔隙率比25 % GP组低7.11 %。外部掺入材料的活性阶段顺序有效连接：NS的二次水化发生在7 ~ 14d， GP的火山灰效应发生在14 ~ 28d。NS的合并有效地弥补了单独GP早期强度的不足。与40 % GP组相比，40 % GP-9 % NS组在第7天的抗压强度提高了34.75 %。在浸出条件下，GP-NS掺入的最佳配比为25 % GP-6 % NS。钙浸出过程使砂浆孔隙度增加，孔隙形态演化为孔道扩大、路径延伸，局部点蚀形成大量墨水瓶孔。研究结果可为缓解隧道喷射混凝土中钙的浸出，促进玻璃固体废弃物资源化利用提供参考。

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引用次数: 0

Hyperspectral remote sensing for characterizing asphalt binders, mastics, and mixtures under aging conditions 老化条件下表征沥青粘结剂、胶料和混合物的高光谱遥感

IF 6.6 2区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Case Studies in Construction Materials

Pub Date : 2025-12-10 DOI: 10.1016/j.cscm.2025.e05683

Vatsal Dharmeshkumar Patel , Ankush Kumar , Rishikesh Bharti , Rajan Choudhary

Asphalt aging is a complex process that involves rheological, chemical, and mechanical changes affecting pavement durability. This research examines the aging behavior of asphalt binders, mastics, and mixtures using Viscosity Grade 30 (VG30), polymer-modified (PMB), and crumb rubber-modified (CRMB) binders. A multi-technique approach integrated rheological (MSCR, LAS), chemical (FTIR), and mechanical (IDEAL-CT) analyses with the hyperspectral remote sensing, under unaged (UA), short-term (STA), and long-term (LTA) aging conditions. RTFO/TFO and PAV were used to simulate STA and LTA for binders and mastics, and a forced-draft oven for mixtures. Results showed a 35–45 % increase in aging degree from short-term to long-term exposure across all materials, with VG30-based samples being the most vulnerable, while PMB and CRMB demonstrated enhanced oxidative stability. Hyperspectral similarity scores exhibited a strong negative correlation with both the rheological indices (R = −0.77 to −0.81) and chemical indices (R = −0.72 to −0.79), as well as a strong positive correlation (R = 0.77) with mechanical indices. These findings demonstrate the potential of hyperspectral sensing as a rapid, non-destructive tool for asphalt aging assessment. This integrated assessment furthers understanding of material behavior and advanced pavement performance monitoring.

沥青老化是一个复杂的过程，涉及影响路面耐久性的流变学、化学和机械变化。本研究考察了沥青粘合剂、胶粘剂以及使用粘度等级30 （VG30）、聚合物改性（PMB）和橡胶屑改性（CRMB）粘合剂的混合物的老化行为。该方法将流变学（MSCR， LAS）、化学（FTIR）和力学（IDEAL-CT）分析与高光谱遥感相结合，在未老化（UA）、短期（STA）和长期（LTA）老化条件下进行。使用RTFO/TFO和PAV模拟粘合剂和胶粘剂的STA和LTA，并使用强制通风烤箱模拟混合物。结果表明，从短期到长期暴露，所有材料的老化程度增加了35 - 45% %，其中vg30基样品最脆弱，而PMB和CRMB则表现出增强的氧化稳定性。高光谱相似度得分与流变性指标（R = - 0.77 ~ - 0.81）和化学指标（R = - 0.72 ~ - 0.79）呈强负相关，与力学指标呈强正相关（R = 0.77）。这些发现证明了高光谱传感作为一种快速、无损的沥青老化评估工具的潜力。这种综合评估进一步加深了对材料性能和先进路面性能监测的理解。

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引用次数: 0

A deep learning approach for predicting steel rebar corrosion in concrete bridge columns from two-year noisy GPR B-scan images 基于两年噪声GPR b扫描图像预测混凝土桥柱钢筋腐蚀的深度学习方法

IF 6.6 2区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Case Studies in Construction Materials

Pub Date : 2025-12-10 DOI: 10.1016/j.cscm.2025.e05671

Maryam Abazarsa, Tzuyang Yu

Steel corrosion is the main cause responsible for the premature failures of reinforced and prestressed concrete structures (e.g., bridges) around the world. Corrosion detection of steel rebars and tendons using nondestructive testing/evaluation (NDT/E) techniques such as ground-penetrating radar (GPR) have demonstrated to be an effective approach for early warning, while various technical challenges remain unsolved in the data interpretation. This is mainly due to the environmental variation in the field and various corrosion levels and concrete properties in noisy GPR data, making the prediction of steel rebar corrosion very difficult in the field. The objective of this paper is to present our approach on analyzing long-term noisy GPR data to extract subsurface steel rebar’s condition without monitoring environmental variation. Our deep learning approach utilizes a convolutional neural network (CNN) AlexNet model and a proposed Power2Net model to predict the corrosion level of steel rebars in concrete bridge columns from 3834 GPR B-scan images on 186 days over a two-year period. The novelty of our approach is the ability to correlate surface visual images with subsurface GPR B-scan images for subsurface steel rebar corrosion prediction. Seven concrete bridge columns at different corrosion levels (from intact to corroded) were scanned in each inspection. In our approach, AlexNet is used for extracting multi-scale features from the images, while Power2Net is used to predict corrosion levels of steel rebars inside concrete. Three laboratory reinforced concrete specimens with known corrosion levels were used to verify our model. From our parametric study, it is found that an inverse power-law pattern between the size of a filter and the number of filters as a function of neural network layer is the key to efficiently extract essential information from noisy radar images and robustly predict steel rebar corrosion in the long-term. From our results, it is found that our proposed DL approach (AlexNet-Power2Net) can predict the corrosion level of different concrete columns under the influence of long-term environmental variation without any environmental data, demonstrating the consistency and robustness of our approach. The environmental effect on B-scan images was amplified by the corrosion level and manifested by false alarms in our predicted level curves. Optimal initial learning rate and optimal number of epochs were found to be 0.001 and 73, respectively, in our case study. We also found that fine-tuning of weights (or model pretraining) can improve model convergence.

钢材腐蚀是世界各地钢筋和预应力混凝土结构（如桥梁）过早失效的主要原因。使用无损检测/评估（NDT/E）技术（如探地雷达（GPR））对钢筋和肌腱进行腐蚀检测已被证明是一种有效的早期预警方法，但在数据解释中仍存在各种技术挑战有待解决。这主要是由于现场环境的变化以及噪声GPR数据中不同的腐蚀水平和混凝土性能，使得现场钢筋腐蚀预测非常困难。本文的目的是介绍在不监测环境变化的情况下，分析长期有噪声的探地雷达数据提取地下钢筋状况的方法。我们的深度学习方法利用卷积神经网络（CNN） AlexNet模型和拟议的Power2Net模型，从两年期间186天的3834张GPR b扫描图像中预测混凝土桥柱中钢筋的腐蚀水平。该方法的新颖之处在于能够将表面视觉图像与地下GPR b扫描图像相关联，用于地下钢筋腐蚀预测。在每次检查中扫描了七个不同腐蚀程度（从完整到腐蚀）的混凝土桥柱。在我们的方法中，AlexNet用于从图像中提取多尺度特征，而Power2Net用于预测混凝土内部钢筋的腐蚀水平。三个已知腐蚀水平的实验室钢筋混凝土样本被用来验证我们的模型。从参数化研究中发现，滤波器的大小与滤波器的数量之间的逆幂律模式作为神经网络层的函数是有效地从噪声雷达图像中提取基本信息并对钢筋腐蚀进行长期稳健预测的关键。从我们的结果中发现，我们提出的深度学习方法（AlexNet-Power2Net）可以在没有任何环境数据的情况下预测长期环境变化影响下不同混凝土柱的腐蚀水平，证明了我们方法的一致性和鲁棒性。环境对b扫描图像的影响被腐蚀程度放大，并在我们预测的水平曲线中表现为假警报。在我们的案例研究中，发现最优初始学习率和最优epoch数分别为0.001和73。我们还发现权值的微调（或模型预训练）可以提高模型的收敛性。

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引用次数: 0

Mix design optimisation for concrete with alternative binders and aggregates incorporating environmental, mechanical and durability performance 混合设计优化混凝土与替代粘合剂和集料结合环境，机械和耐久性性能

IF 6.6 2区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Case Studies in Construction Materials

Pub Date : 2025-12-09 DOI: 10.1016/j.cscm.2025.e05638

A. Razmi , T. Bennett , T. Xie , P. Visintin

The design of environmentally efficient concretes remains challenging due to the conflicting requirements of reducing embodied carbon while maintaining durability and mechanical performance, particularly when recycled aggregates and supplementary cementitious materials (SCMs) are used. This study presents a performance-based optimisation framework that integrates mix design variables, service-life prediction, and life-cycle assessment (LCA) to minimise global warming potential (GWP) while meeting durability requirements. The framework combines artificial neural networks (trained on 4828 experimental mixes), phenomenological chloride diffusion modelling, and a cradle-to-gate life-cycle assessment, optimised using genetic algorithms to minimise global warming potential and natural aggregate usage while meeting chloride diffusion requirements. Results show that switching from GWP minimisation to natural aggregate conservation requires a reduction in water-to-binder ratio (w/b) by 8–30 % and an increase in binder-to-aggregate ratio (b/a) by 40–114 %, which consequently raises GWP. Among SCMs, GGBFS achieves up to 48 % lower GWP, followed by silica fume (47 %) and fly ash (35 %). Multi-objective analysis indicated that incorporating recycled aggregate at approximately 30 % balances durability, resource efficiency, and emissions, whereas full replacement significantly increases GWP unless offset by the use of large volumes of SCMs. Service-life modelling revealed that high-diffusivity concretes required up to 58

kg {CO}_{2 - eq} / m^{2}

additional emissions through increased cover depths, while SCM-enhanced mixes consistently achieved target service-lives with minimal cover penalties. By combining material optimisation with performance-based cover design, the framework identifies mix designs that balance durability, environmental efficiency, and resource conservation, supporting long-lasting, low-carbon concrete elements.

环保型混凝土的设计仍然具有挑战性，因为在保持耐久性和机械性能的同时减少隐含碳的要求相互冲突，特别是当使用回收骨料和补充胶凝材料（scm）时。本研究提出了一个基于性能的优化框架，该框架集成了混合设计变量、使用寿命预测和生命周期评估（LCA），以最大限度地降低全球变暖潜能值（GWP），同时满足耐久性要求。该框架结合了人工神经网络（在4828种实验混合物上进行了训练）、现象学氯化物扩散建模和从摇篮到gate的生命周期评估，并使用遗传算法进行了优化，以最大限度地减少全球变暖潜势和自然总量的使用，同时满足氯化物扩散要求。结果表明，从GWP最小化转变为自然骨料保护需要将水胶比（w/b）降低8-30 %，并将粘结剂与骨料比（b/a）提高40-114 %，从而提高GWP。在SCMs中，GGBFS的GWP降低率高达48% %，其次是硅灰（47% %）和粉煤灰（35% %）。多目标分析表明，采用约30% %的再生骨料可平衡耐久性、资源效率和排放，而完全替代可显著增加全球变暖潜能值，除非通过大量使用SCMs来抵消。使用寿命模型显示，通过增加覆盖深度，高扩散系数混凝土需要高达58 kgCO2−eq/m2的额外排放，而scm增强混合物始终以最小的覆盖损失实现目标使用寿命。通过将材料优化与基于性能的盖板设计相结合，框架确定了平衡耐用性、环境效率和资源节约的混合设计，支持持久、低碳的混凝土元素。

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引用次数: 0