Pub Date : 2026-03-05DOI: 10.1016/j.eng.2026.02.019
Jiarui Li, Ze Xiang, Yunyang Xu, Xingyu Luo, Yao Jiang, Yingchen Huang, Zhe Yang, Ronggao Chen, Xiao Xu
Cellular senescence, a state of irreversible cell cycle arrest, is increasingly recognized as a key pathological driver of the progression of chronic liver diseases from metabolic dysfunction and fibrosis to hepatocellular carcinoma. While initially acting as a tumor-suppressive mechanism to eliminate damaged cells, the chronic accumulation of senescent cells creates a proinflammatory, profibrotic microenvironment through the senescence-associated secretory phenotype (SASP), thereby promoting tissue damage. This review examines the context-dependent mechanisms of cellular senescence across a range of chronic liver diseases, including metabolic, immune-mediated, viral, and malignant conditions. Building on these mechanisms, we critically assess the therapeutic landscape—from the selective clearance of senescent cells to novel strategies that modulate the senescence program—highlighting both their promise and current limitations. Despite the generally promising preclinical results, the clinical translation of senotherapies faces significant hurdles, including the heterogeneity of senescence, a lack of specific biomarkers, and potential off-target effects. Overcoming these challenges through emerging technologies will be crucial to harnessing senescence as a new therapeutic axis for chronic liver disease.
{"title":"Targeting Cellular Senescence: A New Therapeutic Axis in Chronic Liver Disease","authors":"Jiarui Li, Ze Xiang, Yunyang Xu, Xingyu Luo, Yao Jiang, Yingchen Huang, Zhe Yang, Ronggao Chen, Xiao Xu","doi":"10.1016/j.eng.2026.02.019","DOIUrl":"https://doi.org/10.1016/j.eng.2026.02.019","url":null,"abstract":"Cellular senescence, a state of irreversible cell cycle arrest, is increasingly recognized as a key pathological driver of the progression of chronic liver diseases from metabolic dysfunction and fibrosis to hepatocellular carcinoma. While initially acting as a tumor-suppressive mechanism to eliminate damaged cells, the chronic accumulation of senescent cells creates a proinflammatory, profibrotic microenvironment through the senescence-associated secretory phenotype (SASP), thereby promoting tissue damage. This review examines the context-dependent mechanisms of cellular senescence across a range of chronic liver diseases, including metabolic, immune-mediated, viral, and malignant conditions. Building on these mechanisms, we critically assess the therapeutic landscape—from the selective clearance of senescent cells to novel strategies that modulate the senescence program—highlighting both their promise and current limitations. Despite the generally promising preclinical results, the clinical translation of senotherapies faces significant hurdles, including the heterogeneity of senescence, a lack of specific biomarkers, and potential off-target effects. Overcoming these challenges through emerging technologies will be crucial to harnessing senescence as a new therapeutic axis for chronic liver disease.","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"69 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2026-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147360278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2026-03-04DOI: 10.1016/j.eng.2026.01.025
Jianya Luo, Qingyan Lv, Mengping He, Zhiqiang Wang, Yuan Liu
Methicillin-resistant Staphylococcus aureus (MRSA) represents a significant global public health threat. Combination therapy, particularly the use of antibiotics in conjunction with non-antibiotic agents, has emerged as a promising strategy to address the growing crisis of antibiotic resistance. Fosfomycin (FOS), increasingly utilized in clinical practice for treating drug-resistant bacterial infections, exhibits limited efficacy as a monotherapy. Here, we find that 5-fluorouracil (5-FU), a Food and Drug Administration (FDA)-approved anticancer drug, effectively enhances the antibacterial activity of FOS against MRSA, including biofilm-embedded MRSA cells. Mechanistically, 5-FU targets cytidine triphosphate (CTP) synthase, a rate-limiting enzyme responsible for the adenosine triphosphate (ATP)-dependent conversion of uridine triphosphate (UTP) to CTP. Moreover, we demonstrate that the synergistic effect of 5-FU and FOS arises from the perturbation of pyrimidine metabolism, which induces membrane damage, dissipation of the proton motive force (PMF), enhanced ATP synthesis, and accumulation of reactive oxygen species, culminating in bacterial death. In both Galleria mellonella (G. mellonella) and murine infection models, the combination of 5-FU and FOS markedly improves survival and reduces bacterial burdens. Collectively, our work demonstrates the therapeutic potential of 5-FU combined with FOS for tackling MRSA infections and highlights the pivotal role of perturbing pyrimidine metabolism in restoring antibiotic susceptibility.
{"title":"Boosting Fosfomycin Efficacy Against Methicillin-Resistant Staphylococcus aureus Infections by Targeting Pyrimidine Metabolism","authors":"Jianya Luo, Qingyan Lv, Mengping He, Zhiqiang Wang, Yuan Liu","doi":"10.1016/j.eng.2026.01.025","DOIUrl":"https://doi.org/10.1016/j.eng.2026.01.025","url":null,"abstract":"Methicillin-resistant <em>Staphylococcus aureus</em> (MRSA) represents a significant global public health threat. Combination therapy, particularly the use of antibiotics in conjunction with non-antibiotic agents, has emerged as a promising strategy to address the growing crisis of antibiotic resistance. Fosfomycin (FOS), increasingly utilized in clinical practice for treating drug-resistant bacterial infections, exhibits limited efficacy as a monotherapy. Here, we find that 5-fluorouracil (5-FU), a Food and Drug Administration (FDA)-approved anticancer drug, effectively enhances the antibacterial activity of FOS against MRSA, including biofilm-embedded MRSA cells. Mechanistically, 5-FU targets cytidine triphosphate (CTP) synthase, a rate-limiting enzyme responsible for the adenosine triphosphate (ATP)-dependent conversion of uridine triphosphate (UTP) to CTP. Moreover, we demonstrate that the synergistic effect of 5-FU and FOS arises from the perturbation of pyrimidine metabolism, which induces membrane damage, dissipation of the proton motive force (PMF), enhanced ATP synthesis, and accumulation of reactive oxygen species, culminating in bacterial death. In both <em>Galleria mellonella</em> (<em>G. mellonella</em>) and murine infection models, the combination of 5-FU and FOS markedly improves survival and reduces bacterial burdens. Collectively, our work demonstrates the therapeutic potential of 5-FU combined with FOS for tackling MRSA infections and highlights the pivotal role of perturbing pyrimidine metabolism in restoring antibiotic susceptibility.","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"49 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2026-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147360279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2026-03-04DOI: 10.1016/j.eng.2025.03.042
Min Chen, Guifu Si, Changle Chen
Polyolefins were first commercialized in the 1930s by Imperial Chemical Industries (ICI) and were applied as underwater cable coatings. After almost a century of development, polyolefins have become one of the most widely used synthetic polymers, accounting for almost half of all global plastics production [1]. Due to their many superior properties, they have found many everyday applications, but their annual production and widespread usage in single-use packaging have made them a major component of post-consumer plastic waste [2]. Polyolefins are indispensable in the modern chemical industry and society, and their global market size is expected to grow rapidly in the foreseeable future. However, the current linear production and consumption of polyolefins is unsustainable and has created a serious environmental burden. Traditional recycling of polyolefins is performed via either mechanical recycling or pyrolysis. Mechanical recycling has been the dominant polyolefin recycling strategy, but side reactions including degradation and crosslinking seriously damage material properties, leading to products with lower quality compared with the original materials. Pyrolysis also faces many issues such as low energy efficiency, poor product selectivity, and environmental pollution. Due to the limitations of these two strategies, novel recycling strategies or the redesigning of polyolefins are highly desirable.
{"title":"Upcycling and Redesigning of Polyolefins","authors":"Min Chen, Guifu Si, Changle Chen","doi":"10.1016/j.eng.2025.03.042","DOIUrl":"https://doi.org/10.1016/j.eng.2025.03.042","url":null,"abstract":"Polyolefins were first commercialized in the 1930s by Imperial Chemical Industries (ICI) and were applied as underwater cable coatings. After almost a century of development, polyolefins have become one of the most widely used synthetic polymers, accounting for almost half of all global plastics production <span><span>[1]</span></span>. Due to their many superior properties, they have found many everyday applications, but their annual production and widespread usage in single-use packaging have made them a major component of post-consumer plastic waste <span><span>[2]</span></span>. Polyolefins are indispensable in the modern chemical industry and society, and their global market size is expected to grow rapidly in the foreseeable future. However, the current linear production and consumption of polyolefins is unsustainable and has created a serious environmental burden. Traditional recycling of polyolefins is performed via either mechanical recycling or pyrolysis. Mechanical recycling has been the dominant polyolefin recycling strategy, but side reactions including degradation and crosslinking seriously damage material properties, leading to products with lower quality compared with the original materials. Pyrolysis also faces many issues such as low energy efficiency, poor product selectivity, and environmental pollution. Due to the limitations of these two strategies, novel recycling strategies or the redesigning of polyolefins are highly desirable.","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"23 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2026-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147360280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This review is the first to systematically propose and construct XiangShu (image–number) omics (XSO) that inherits the ecological values, epistemology, and methodology of traditional Chinese medicine (TCM), and absorbs modern mathematical sciences. We reposition XSO as a “promising research paradigm” rather than a definitive solution, serving as an exploratory computational bridge to address the epistemological chasm. By integrating the four diagnostic methods of TCM with cutting-edge multimodal technologies spanning acoustics, optics, thermodynamics, mechanics, electromagnetism, and magnetism, Xiang omics is established based on multiscale phenotypic data ranging from astronomical and geographical parameters to human macro-, meso-, and micro-level physiological characteristics. The Xiang data are then computed into Shu (number) Omics using classical TCM numerology and modern artificial intelligence. Shu omics contains two tiers of models. The small model aims to elucidate Yin–Yang dynamics conceptualized as a mathematical classification model; the progression from pre-disease to disease to recovery modeled via Shannon entropy-based predictive frameworks; the five-element relationships represented through complex functional equations. The large model integrates large language models (LLMs) based on the classical therapeutic logic of principle–method–formula–medicine, such as the Shu-Zhi Qihuang large model. Both models work together to support intelligent reasoning and system-level knowledge synthesis. Driven by the dual engines of data-intensive and experience-driven “blind computation” and “directed inference,” XSO enables a panoramic decoding of human life rhythms across temporal, spatial, and field dimensions, thereby inform clinical decision-making and precision-oriented interventions in TCM.
{"title":"XiangShu Omics: A Framework Based on the Spatio–Temporal Field Rhythms of Human Life and Systems Intelligence Methodology","authors":"Hongyu Wang, Chunchun Yuan, Haitao Zhang, Xiaoyun Wang, Haifeng Jia, Furui Fu, Xichen Tang, Jiarui Cui, Jiangxun Ji, Senjie Shi, Hongbin Xu, Jinni Hong, Tianpeng Liu, Junhao Liang, Mengting Yuan, Xiaomei Liu, Qianqian Liang, Dezhi Tang, Qi Shi, Yongjun Wang","doi":"10.1016/j.eng.2026.02.018","DOIUrl":"https://doi.org/10.1016/j.eng.2026.02.018","url":null,"abstract":"This review is the first to systematically propose and construct XiangShu (image–number) omics (XSO) that inherits the ecological values, epistemology, and methodology of traditional Chinese medicine (TCM), and absorbs modern mathematical sciences. We reposition XSO as a “promising research paradigm” rather than a definitive solution, serving as an exploratory computational bridge to address the epistemological chasm. By integrating the four diagnostic methods of TCM with cutting-edge multimodal technologies spanning acoustics, optics, thermodynamics, mechanics, electromagnetism, and magnetism, Xiang omics is established based on multiscale phenotypic data ranging from astronomical and geographical parameters to human macro-, meso-, and micro-level physiological characteristics. The Xiang data are then computed into Shu (number) Omics using classical TCM numerology and modern artificial intelligence. Shu omics contains two tiers of models. The small model aims to elucidate Yin–Yang dynamics conceptualized as a mathematical classification model; the progression from pre-disease to disease to recovery modeled via Shannon entropy-based predictive frameworks; the five-element relationships represented through complex functional equations. The large model integrates large language models (LLMs) based on the classical therapeutic logic of principle–method–formula–medicine, such as the Shu-Zhi Qihuang large model. Both models work together to support intelligent reasoning and system-level knowledge synthesis. Driven by the dual engines of data-intensive and experience-driven “blind computation” and “directed inference,” XSO enables a panoramic decoding of human life rhythms across temporal, spatial, and field dimensions, thereby inform clinical decision-making and precision-oriented interventions in TCM.","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"5 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147368175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2026-03-03DOI: 10.1016/j.eng.2025.12.041
Ruoxi Zhang, Aaron D. Sadow, Wenyu Huang
Global plastic production has reached 413.8 million metric tons in 2024 [1] and is forecasted to surpass 1.2 billion metric tons by 2050 [2], [3]. Polyolefins, mainly polyethylene (PE) and polypropylene (PP), dominate single-use packaging and account for approximately 55% of global plastic waste [2]. The chemical inertness that makes these materials desirable for commercial applications also renders them persistent in the environment [3].
{"title":"Hydrogenolysis Versus Hydrocracking for Polyolefin Upcycling","authors":"Ruoxi Zhang, Aaron D. Sadow, Wenyu Huang","doi":"10.1016/j.eng.2025.12.041","DOIUrl":"https://doi.org/10.1016/j.eng.2025.12.041","url":null,"abstract":"Global plastic production has reached 413.8 million metric tons in 2024 <span><span>[1]</span></span> and is forecasted to surpass 1.2 billion metric tons by 2050 <span><span>[2]</span></span>, <span><span>[3]</span></span>. Polyolefins, mainly polyethylene (PE) and polypropylene (PP), dominate single-use packaging and account for approximately 55% of global plastic waste <span><span>[2]</span></span>. The chemical inertness that makes these materials desirable for commercial applications also renders them persistent in the environment <span><span>[3]</span></span>.","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"2 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147360281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2026-03-03DOI: 10.1016/j.eng.2025.12.042
Xiao Chen, Shan Lou, Wenhan Zeng, Paul Scott, Xiangqian Jiang, Wenjuan Sun
Ensuring reliable measurement quality in X-ray computed tomography (XCT) dimensional metrology remains challenging, because the complete causal chain—from XCT influencing factors, through image quality, to final surface quality—has not yet been established. A key gap is the absence of a surface quality evaluation method with metrics directly linked to image quality metrics. This paper addresses that gap by introducing such a method which determines the metrological structural resolution (MSR), a critical resolution metric for XCT dimensional metrology. The method computes the two-dimensional surface amplitude transfer function (SATF) to evaluate XCT systematic and random errors and also to identify the measurable scale limit. The MSR can then be determined by either the permissible systematic error or the measurable scale limit. By providing metrics that map surface systematic error, random error and MSR onto image blur, noise and resolution, the proposed method effectively completes the XCT causal chain and enhances the understanding of how XCT influencing factors affect measurement quality.
{"title":"A Surface Quality Evaluation Method for Establishing Image-Surface Causal Chain in X-ray Computed Tomography Measurement","authors":"Xiao Chen, Shan Lou, Wenhan Zeng, Paul Scott, Xiangqian Jiang, Wenjuan Sun","doi":"10.1016/j.eng.2025.12.042","DOIUrl":"https://doi.org/10.1016/j.eng.2025.12.042","url":null,"abstract":"Ensuring reliable measurement quality in X-ray computed tomography (XCT) dimensional metrology remains challenging, because the complete causal chain—from XCT influencing factors, through image quality, to final surface quality—has not yet been established. A key gap is the absence of a surface quality evaluation method with metrics directly linked to image quality metrics. This paper addresses that gap by introducing such a method which determines the metrological structural resolution (MSR), a critical resolution metric for XCT dimensional metrology. The method computes the two-dimensional surface amplitude transfer function (SATF) to evaluate XCT systematic and random errors and also to identify the measurable scale limit. The MSR can then be determined by either the permissible systematic error or the measurable scale limit. By providing metrics that map surface systematic error, random error and MSR onto image blur, noise and resolution, the proposed method effectively completes the XCT causal chain and enhances the understanding of how XCT influencing factors affect measurement quality.","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"15 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147368174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2026-02-18DOI: 10.1016/j.eng.2025.11.036
Yanying Wang, Ying Cheng, Qinglin Qi, Zhiheng Zhao, George Q. Huang, Stefan Pickl, Fei Tao
Recommending maintenance plans presents significant challenges due to the low standardization of maintenance records and unclear pathways for identifying appropriate plans. While knowledge graphs have been extensively researched for integrating and evolving maintenance data, these issues hinder the accurate recommendation of maintenance solutions within large-scale maintenance knowledge systems. This paper proposes a causality and equipment structure enhanced maintenance plan matching and recommendation (CEE-MPMR) method to address these challenges. The method leverages an unsupervised SimCSE model to normalize domain vocabulary in the absence of domain lexicon, and proposes a maintenance plan reasoning method based on RotatE cc. The proposed method achieves a maintenance plan matching accuracy of 90.80%, effectively improving the precision of maintenance plan recommendations. Finally, we applied and validated the approach on real-world data from a nuclear power enterprise and integrated the algorithm into a maintenance plan recommendation system, supporting intelligent analysis and decision-making for nuclear complex equipment maintenance.
{"title":"Causality and Equipment Structure Enhanced Maintenance Plan Recommendation with Knowledge Graph Integration","authors":"Yanying Wang, Ying Cheng, Qinglin Qi, Zhiheng Zhao, George Q. Huang, Stefan Pickl, Fei Tao","doi":"10.1016/j.eng.2025.11.036","DOIUrl":"https://doi.org/10.1016/j.eng.2025.11.036","url":null,"abstract":"Recommending maintenance plans presents significant challenges due to the low standardization of maintenance records and unclear pathways for identifying appropriate plans. While knowledge graphs have been extensively researched for integrating and evolving maintenance data, these issues hinder the accurate recommendation of maintenance solutions within large-scale maintenance knowledge systems. This paper proposes a causality and equipment structure enhanced maintenance plan matching and recommendation (CEE-MPMR) method to address these challenges. The method leverages an unsupervised SimCSE model to normalize domain vocabulary in the absence of domain lexicon, and proposes a maintenance plan reasoning method based on RotatE cc. The proposed method achieves a maintenance plan matching accuracy of 90.80%, effectively improving the precision of maintenance plan recommendations. Finally, we applied and validated the approach on real-world data from a nuclear power enterprise and integrated the algorithm into a maintenance plan recommendation system, supporting intelligent analysis and decision-making for nuclear complex equipment maintenance.","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"49 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2026-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146231336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2026-02-18DOI: 10.1016/j.eng.2026.02.014
No Abstract
没有抽象的
{"title":"Gas Turbine Shortage Could Derail Data Center Expansion","authors":"","doi":"10.1016/j.eng.2026.02.014","DOIUrl":"https://doi.org/10.1016/j.eng.2026.02.014","url":null,"abstract":"No Abstract","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"4 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2026-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146223287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2026-02-17DOI: 10.1016/j.eng.2026.02.012
Jinyang Jiang, Junlin Lin, Lin Jin, Fengjuan Wang, Zhiyong Liu, Yingze Li, Zeyu Lu
As fundamental construction materials, cementitious composites face significant challenges under conventional development approaches, including carbon-intensive production, resource-intensive experimentation, and inefficient design processes. With the emergence of machine learning (ML) as a transformative solution to these limitations, this study presents a state-of-the-art review of existing research to highlight its potential in advancing the development of cementitious composites with intelligent and green lifecycles. The review first provides a foundational introduction to ML concepts and then proposes a novel four-quadrant classification framework to systematically organize current ML applications in the field. The ML-driven innovations integrate the component–structure–process–performance relationships of cementitious composites through sustainable material selection, effective characterization, accurate performance prediction, and optimized inverse design, collectively promoting a paradigm shift toward intelligent and green lifecycles. Furthermore, critical implementation challenges are examined across technical, methodological, and operational dimensions, together with corresponding solution strategies. This review ultimately offers both a conceptual framework and practical implementation guidelines for the development of next-generation sustainable construction materials.
{"title":"Advancements in Machine Learning for the Development of Cementitious Composites Toward an Intelligent and Green Lifecycle: A State-of-the-Art Review","authors":"Jinyang Jiang, Junlin Lin, Lin Jin, Fengjuan Wang, Zhiyong Liu, Yingze Li, Zeyu Lu","doi":"10.1016/j.eng.2026.02.012","DOIUrl":"https://doi.org/10.1016/j.eng.2026.02.012","url":null,"abstract":"As fundamental construction materials, cementitious composites face significant challenges under conventional development approaches, including carbon-intensive production, resource-intensive experimentation, and inefficient design processes. With the emergence of machine learning (ML) as a transformative solution to these limitations, this study presents a state-of-the-art review of existing research to highlight its potential in advancing the development of cementitious composites with intelligent and green lifecycles. The review first provides a foundational introduction to ML concepts and then proposes a novel four-quadrant classification framework to systematically organize current ML applications in the field. The ML-driven innovations integrate the component–structure–process–performance relationships of cementitious composites through sustainable material selection, effective characterization, accurate performance prediction, and optimized inverse design, collectively promoting a paradigm shift toward intelligent and green lifecycles. Furthermore, critical implementation challenges are examined across technical, methodological, and operational dimensions, together with corresponding solution strategies. This review ultimately offers both a conceptual framework and practical implementation guidelines for the development of next-generation sustainable construction materials.","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"11 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2026-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146208969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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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