[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-450261-105":59,"doc-detail-450261-en":122},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":115,"head_meta":117,"extra_data":119,"updated_unix":121},105,"en","prediction-of-crack-repair-percentage-in-self-healing-concrete-using-machine-learning","Prediction of crack repair percentage in self-healing concrete using machine learning","","Concrete’s widespread use is constrained by micro-crack formation that undermines durability and structural performance, while self-healing effectiveness is often assessed through costly, time-consuming experiments without a universally accepted standard. This study builds three hybrid machine-learning models—ANN optimized with GA, PSO, and the Levenberg–Marquardt (LM) algorithm—to predict crack healing percentage. Evaluation uses multiple statistical indices, showing all three hybrids outperform prior work, with ANN–LM achieving the highest accuracy, supporting design of durable cement-based materials.",{"@graph":69,"@context":114},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/prediction-of-crack-repair-percentage-in-self-healing-concrete-using-machine-learning/450261/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/prediction-of-crack-repair-percentage-in-self-healing-concrete-using-machine-learning/450261.png","ImageObject",300,407,{"name":92,"@type":93},"Miles","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-06","2026-09-30",true,{"@type":102,"interactionType":103,"userInteractionCount":19},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108],{"name":109,"@type":110,"acceptedAnswer":111},"Which model achieves the highest prediction accuracy and how was performance evaluated?","Question",{"text":112,"@type":113},"The ANN–LM hybrid model yields the highest prediction accuracy. Performance is assessed using multiple statistical indices.","Answer","https://schema.org",{"og:url":83,"og:type":116,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":118,"canonical":83},"index,follow",{"doc_id":120,"site_id":62},450261,1790945817,{"code":4,"msg":5,"data":123},{"doc_id":120,"user_id":124,"nickname":92,"user_avatar":125,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":126,"file_id":127,"file_url":128,"file_type":129,"file_size":130,"view_count":19,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":131,"language":132,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":133,"faqs":134,"seo_title":135,"seo_description":67,"update_tm":136,"read_time":137},13056703019404,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nPrediction of crack repair percentage in self-healing concrete using machine learning  \nHossein Khosravi􀀍, Peyman Kiani, Mohammad Bahram & Mojtaba Lezgy-Nazargah  \nConcrete is the most widely used construction material, yet it is highly susceptible to micro-crack formation, which can critically reduce its durability and structural performance. This challenge has drawn significant research interest toward the self-healing capability of concrete as a sustainable solution. However, the effectiveness of self-healing in concrete is still mainly evaluated through time-consuming and costly experimental procedures, with no universally accepted standard methods, limiting its practical implementation. To address this problem and harness the potential of artificial intelligence for modeling complex nonlinear behaviors, this study develops three hybrid predictive models: artificial neural network (ANN) optimized with genetic algorithm (GA), particleswarm optimization (PSO), and the Levenberg–Marquardt (LM) algorithm. Model performance was assessed using multiple statistical indices, and the results demonstrated that all three hybrid models outperformed the reference study by Zhuang et al., with the ANN–LM model yielding the highest prediction accuracy. The findings highlight that integrating optimization algorithms with ANN provides a robust and reliable framework for predicting the crack healing percentage in self-healing concrete, offering valuable insights for the design and improvement of durable and sustainable cement-based materials.  \nKeywords Self-healing concrete, Artificial neural network, Genetic algorithm, Particle swarm optimization, Levenberg–Marquardt algorithm  \nToday, concrete and steel are considered the two dominant construction materials used in structural systems, with most structures being built using one or a combination of both materials1. After water, concrete ranks as the second most consumed substance globally, with an average of three tons used per person annually. The consumption of concrete in construction is nearly double the total usage of all other building materials combined, and it is expected to remain a primary material in the future. Due to this extensive usage, any issues or deficiencies related to concrete and reinforced concrete structures—particularly in terms of safety and maintenance costs—have become a significant public concern2. Concrete is inherently prone to cracking, which decreases durability and increases maintenance costs3. Its low tensile strength contributes to the formation of cracks, which can occur at any stage of its service life. These cracks may result from various factors such as thermal stresses, plastic shrinkage, settlement, drying shrinkage, weather conditions, different loading scenarios, or combinations thereof, whether the concrete is in a plastic or hardened state4. Initial micro-cracks are often undetectable due to their small size, but they can propagate under environmental exposure and mechanical loading5. One of the major durability issues in concrete is its permeability6, which allows the ingress of harmful gases and liquids through cracks7. This exposes the reinforcing steel to oxygen and moisture, leading to corrosion and increasing the risk of structural deterioration8.  \nTherefore, the concept of concrete healing has gained substantial attention over the past two decades, as evidenced by the number of publications on the topic increasing from 10 in 2002 to 147 in 20179. The idea of selfhealing concrete dates back to ancient Roman times, where a special lime-based mortar was used that exhibited self-healing characteristics10. In recent years, several studies have applied machine learning approaches to evaluate and predict the self-healing behavior of cement-based materials. Jakubowski and Tomczak have made significant contributions in this area. They developed a deep learning metasen","cbCaifzSxZRWc4wB","https://ap.wps.com/l/cbCaifzSxZRWc4wB","pdf",4231449,21,"English","# Background and problem statement\n# Self-healing concrete and prior work\n# Proposed hybrid machine-learning models\n## ANN with GA\n## ANN with PSO\n## ANN with LM\n# Model evaluation and results\n# Implications for durable cement-based materials","[{\"question\":\"Which model achieves the highest prediction accuracy and how was performance evaluated?\",\"answer\":\"The ANN–LM hybrid model yields the highest prediction accuracy. Performance is assessed using multiple statistical indices.\"}]","Prediction of crack repair percentage in self-healing concrete using machine learning | PDF",1790732659,53]