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.
{"title":"Optimizing cement-treated recycled concrete aggregates for road bases using secondary additive","authors":"Zainul Abedin Khan , Umashankar Balunaini , Nhu H.T. Nguyen , Susanga Costa","doi":"10.1016/j.cscm.2025.e05687","DOIUrl":"10.1016/j.cscm.2025.e05687","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":9641,"journal":{"name":"Case Studies in Construction Materials","volume":"24 ","pages":"Article e05687"},"PeriodicalIF":6.6,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145788615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"Clay activation through CO2-derived oxalic acid for advancing its reactivity and strength of limestone calcined clay cement (LC3)","authors":"Miral Fatima , Mounir Ltifi , Khuram Rashid , Idrees Zafar","doi":"10.1016/j.cscm.2025.e05684","DOIUrl":"10.1016/j.cscm.2025.e05684","url":null,"abstract":"<div><div>The widespread deployment of limestone calcined clay cement (LC<sup>3</sup>) 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 CO<sub>2</sub> reduction utilizing a waste carbon stream, for the development of LC<sup>3</sup>. 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, R<sup>3</sup>) reveals that the CT method uniquely enhances reactivity by promoting selective leaching of Fe<sub>2</sub>O<sub>3</sub> and enriching Al<sub>2</sub>O<sub>3</sub> content, while also inducing mineralogical transitions from quartz to more reactive phases like cristobalite. The R<sup>3</sup> 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 LC<sup>3</sup> binders, CT-activated clay yielded a more balanced hydration phase assemblage, accelerating early-age hydration. This translated directly to superior mechanical performance; LC<sup>3</sup>-CT blends nearly met the ASTM strength criterion (i.e., 42.5 MPa) benchmark at 28 days (within 1 % deviation), significantly outperforming LC<sup>3</sup>-TH blends (10 % deficit). Despite the added acid, the LC<sup>3</sup>-CT system maintains a compelling environmental advantage, achieving 21–23 % reductions in CO<sub>2</sub> 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 CO<sub>2</sub> utilization, this approach strengthens the foundation for next-generation, low-carbon cement technologies.</div></div>","PeriodicalId":9641,"journal":{"name":"Case Studies in Construction Materials","volume":"24 ","pages":"Article e05684"},"PeriodicalIF":6.6,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145718706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-11DOI: 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.
{"title":"Strength characterisation of fly ash blended 3D printed concrete enhanced with explainable machine learning","authors":"Imtiaz Iqbal , Waleed Bin Inqiad , Tala Kasim , Svetlana Besklubova , Melak Mohammad Adil , Mujib Rahman","doi":"10.1016/j.cscm.2025.e05682","DOIUrl":"10.1016/j.cscm.2025.e05682","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":9641,"journal":{"name":"Case Studies in Construction Materials","volume":"24 ","pages":"Article e05682"},"PeriodicalIF":6.6,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145788493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-11DOI: 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。
{"title":"A deep learning framework for microstructural analysis of nano-modified cementitious composites using metal intrusion and BSE imaging","authors":"Zixuan Yu , Shuai Liang , Tong Liu , Yifei Ma , Yining Lin , Yuan Gao , Yanming Liu , Jinpeng Wang","doi":"10.1016/j.cscm.2025.e05694","DOIUrl":"10.1016/j.cscm.2025.e05694","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":9641,"journal":{"name":"Case Studies in Construction Materials","volume":"24 ","pages":"Article e05694"},"PeriodicalIF":6.6,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145718804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-11DOI: 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.
{"title":"Sustainable cementitious materials from bamboo leaf-derived hydrochar: Process optimization and mechanical performance","authors":"Lafiya S.L , Kavitha M.Sambasivam","doi":"10.1016/j.cscm.2025.e05677","DOIUrl":"10.1016/j.cscm.2025.e05677","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":9641,"journal":{"name":"Case Studies in Construction Materials","volume":"24 ","pages":"Article e05677"},"PeriodicalIF":6.6,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145735776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-11DOI: 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.
{"title":"Experimental and numerical evaluation of indirect tensile properties of concrete containing macro fibers processed from waste GFRP composites","authors":"M. Qin , P.L. LI , Z.L. Hong , C. Lian , C. WANG , Q.Q. Zou , B. Fu","doi":"10.1016/j.cscm.2025.e05686","DOIUrl":"10.1016/j.cscm.2025.e05686","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":9641,"journal":{"name":"Case Studies in Construction Materials","volume":"24 ","pages":"Article e05686"},"PeriodicalIF":6.6,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145788422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-10DOI: 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.
{"title":"Calcium leaching resistance and mechanism of early-age mortar with combined incorporation of glass powder and nano-silica under leaching conditions","authors":"Yuyang Liu , Taifeng Zhong , Wei Li , Yeda Li , Liping Jin , Likang Qiu , Liangjun Gu , Yueyue Ling","doi":"10.1016/j.cscm.2025.e05681","DOIUrl":"10.1016/j.cscm.2025.e05681","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":9641,"journal":{"name":"Case Studies in Construction Materials","volume":"24 ","pages":"Article e05681"},"PeriodicalIF":6.6,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145788587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"Hyperspectral remote sensing for characterizing asphalt binders, mastics, and mixtures under aging conditions","authors":"Vatsal Dharmeshkumar Patel , Ankush Kumar , Rishikesh Bharti , Rajan Choudhary","doi":"10.1016/j.cscm.2025.e05683","DOIUrl":"10.1016/j.cscm.2025.e05683","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":9641,"journal":{"name":"Case Studies in Construction Materials","volume":"24 ","pages":"Article e05683"},"PeriodicalIF":6.6,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145735771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-10DOI: 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.
{"title":"A deep learning approach for predicting steel rebar corrosion in concrete bridge columns from two-year noisy GPR B-scan images","authors":"Maryam Abazarsa, Tzuyang Yu","doi":"10.1016/j.cscm.2025.e05671","DOIUrl":"10.1016/j.cscm.2025.e05671","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":9641,"journal":{"name":"Case Studies in Construction Materials","volume":"24 ","pages":"Article e05671"},"PeriodicalIF":6.6,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145788591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-09DOI: 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 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.
{"title":"Mix design optimisation for concrete with alternative binders and aggregates incorporating environmental, mechanical and durability performance","authors":"A. Razmi , T. Bennett , T. Xie , P. Visintin","doi":"10.1016/j.cscm.2025.e05638","DOIUrl":"10.1016/j.cscm.2025.e05638","url":null,"abstract":"<div><div>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 (<em>SCMs</em>) are used. This study presents a performance-based optimisation framework that integrates mix design variables, service-life prediction, and life-cycle assessment (<em>LCA</em>) to minimise global warming potential (<em>GWP</em>) 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 <em>GWP</em> minimisation to natural aggregate conservation requires a reduction in water-to-binder ratio (<em>w/b</em>) by 8–30 % and an increase in binder-to-aggregate ratio (<em>b/a</em>) by 40–114 %, which consequently raises <em>GWP</em>. Among <em>SCMs</em>, <em>GGBFS</em> achieves up to 48 % lower <em>GWP</em>, 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 <em>GWP</em> unless offset by the use of large volumes of <em>SCMs</em>. Service-life modelling revealed that high-diffusivity concretes required up to 58 <span><math><mrow><mi>kg</mi><msub><mrow><mi>CO</mi></mrow><mrow><mn>2</mn><mo>−</mo><mi>eq</mi></mrow></msub><mo>/</mo><msup><mrow><mi>m</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></math></span> additional emissions through increased cover depths, while <em>SCM</em>-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.</div></div>","PeriodicalId":9641,"journal":{"name":"Case Studies in Construction Materials","volume":"24 ","pages":"Article e05638"},"PeriodicalIF":6.6,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145735779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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