{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T01:02:05Z","timestamp":1778634125966,"version":"3.51.4"},"reference-count":32,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T00:00:00Z","timestamp":1770249600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:p>To enable rapid, accurate grading of tunnel surrounding rock during construction, we propose a real-time grading method that integrates image processing with lightweight deep learning. We developed an automated pipeline that combines image-processing techniques and machine-learning algorithms to extract and classify characteristic parameters of tunnel surrounding rock, enabling real-time monitoring and classification at the tunnel palm surface. The study demonstrates that: (1) Following the proposed image-acquisition standards for rock and tunnel palm surfaces, images are converted to grayscale, denoised, enhanced, and normalized, which facilitates efficient and accurate extraction of structural features and improves the precision of classification parameters; (2) An optimized lithology identification and classification model was built, and a rock-hardness, strength, and integrity sensing approach based on the ShuffleNetV2 convolutional neural network was introduced to achieve real-time surrounding-rock grading. On an engineering site, the method attains 85% accuracy for lithology classification, 75% for rock-mass integrity, and 80% for overall surrounding-rock grade, confirming its feasibility and practical value. These results offer theoretical insight and engineering utility for the scientific evaluation of tunnel surrounding-rock grade.<\/jats:p>","DOI":"10.3389\/frai.2026.1766828","type":"journal-article","created":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T06:44:27Z","timestamp":1770273867000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Real-time grading method of tunnel surrounding rock based on image recognition"],"prefix":"10.3389","volume":"9","author":[{"given":"Yihuan","family":"Xiao","sequence":"first","affiliation":[{"name":"School of Civil Engineering, Southwest Jiaotong University","place":["Chengdu, Sichuan, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Civil Engineering, Southwest Jiaotong University","place":["Chengdu, Sichuan, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingye","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Civil Engineering, Southwest Jiaotong University","place":["Chengdu, Sichuan, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zemin","family":"Qiu","sequence":"additional","affiliation":[{"name":"School of Civil Engineering, Southwest Jiaotong University","place":["Chengdu, Sichuan, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liao","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Civil Engineering, Southwest Jiaotong University","place":["Chengdu, Sichuan, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yihua","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Civil Engineering, Southwest Jiaotong University","place":["Chengdu, Sichuan, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yabin","family":"Li","sequence":"additional","affiliation":[{"name":"School of Civil Engineering, Southwest Jiaotong University","place":["Chengdu, Sichuan, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yin","family":"Pan","sequence":"additional","affiliation":[{"name":"Sichuan Electric Power Design and Consulting Co., Ltd.","place":["Chengdu, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qinghua","family":"Xiao","sequence":"additional","affiliation":[{"name":"School of Civil Engineering, Southwest Jiaotong University","place":["Chengdu, Sichuan, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,2,5]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"406","DOI":"10.19713\/j.cnki.43-1423\/u.T20230214","article-title":"Intelligent identification of classification features of tunnel surrounding rock and visualization","volume":"21","author":"Chen","year":"2024","journal-title":"J. Railway Sci. Eng."},{"key":"ref2","author":"Gui","year":"2024"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"1528","DOI":"10.1080\/19475705.2020.1803996","article-title":"Reliable stability analysis of surrounding rock for super section tunnel based on digital characteristics of joint information","volume":"11","author":"He","year":"2020","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"1382","DOI":"10.16058\/j.issn.1005-0930.2023.06.003","article-title":"Research on intelligent identification and classification of surrounding rock in tunnels and research on excavation safety risk based on machine vision","volume":"31","author":"Huang","year":"2023","journal-title":"J. Basic Sci. Eng."},{"key":"ref5","first-page":"105","article-title":"Advances in the study of rock discontinuity net work modeling technique","volume-title":"Bull. Geol. Sci. Technol.","author":"Jia","year":"2001"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"11601","DOI":"10.1007\/s11227-022-04330-9","article-title":"Application of canny operator threshold adaptive segmentation algorithm combined with digital image processing in tunnel face crevice extraction","volume":"78","author":"Jiang","year":"2022","journal-title":"J. Supercomput."},{"key":"ref7","doi-asserted-by":"publisher","first-page":"103810","DOI":"10.1016\/j.tust.2021.103810","article-title":"Rock mass trace line identification incorporated with grouping algorithm at tunnel faces","volume":"110","author":"Leng","year":"2021","journal-title":"Tunn. Undergr. Space Technol."},{"key":"ref8","doi-asserted-by":"publisher","first-page":"750","DOI":"10.1016\/j.autcon.2005.02.004","article-title":"Digital image processing based approach for tunnel excavation faces","volume":"14","author":"Leu","year":"2005","journal-title":"Autom. Constr."},{"key":"ref9","doi-asserted-by":"publisher","first-page":"967","DOI":"10.16285\/j.rsm.2016.2785.","article-title":"Reliability analysis method of sub-classification of tunnel rock mass and its engineering application","volume":"39","author":"Li","year":"2018","journal-title":"Rock Soil Mech."},{"key":"ref10","doi-asserted-by":"publisher","first-page":"139340","DOI":"10.1016\/j.conbuildmat.2024.139340","article-title":"Intelligent identification of rock mass structural based on point cloud deep learning technology","volume":"456","author":"Li","year":"2024","journal-title":"Constr. Build. Mater."},{"key":"ref9001","doi-asserted-by":"publisher","first-page":"1809","DOI":"10.11779\/CJGE201810007","article-title":"Method for surrounding rock mass classification of highway tunnels based on deep learning technology[J]","volume":"40","author":"Liu","year":"2018","journal-title":"Chin. J. Geotech. Eng."},{"key":"ref11","doi-asserted-by":"publisher","first-page":"437","DOI":"10.20174\/j.juse.2023.02.010.","article-title":"Lithology identification method of tunnel surrounding rock based on transfer learning technology","volume":"19","author":"Liu","year":"2023","journal-title":"Chin. J. Undergr. Space Eng."},{"key":"ref12","doi-asserted-by":"publisher","first-page":"103595","DOI":"10.1016\/j.tust.2020.103595","article-title":"Prediction model of rock mass class using classification and regression tree integrated AdaBoost algorithm based on TBM driving data","volume":"106","author":"Liu","year":"2020","journal-title":"Tunn. Undergr. Space Technol."},{"key":"ref13","doi-asserted-by":"publisher","first-page":"19846","DOI":"10.1038\/s41598-022-19301-6","article-title":"A probability prediction method for the classification of surrounding rock quality of tunnels with incomplete data using Bayesian networks","volume":"12","author":"Ma","year":"2022","journal-title":"Sci. Rep."},{"key":"ref14","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1080\/17499518.2023.2182891","article-title":"A real-time intelligent classification model using machine learning for tunnel surrounding rock and its application","volume":"17","author":"Ma","year":"2023","journal-title":"Georisk Assess. Manag. Risk Eng. Syst. Geohazards"},{"key":"ref15","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1007\/s10064-023-03097-4","article-title":"Classification for tunnel surrounding rock based on multiple geological methods and extension model","volume":"82","author":"Ma","year":"2023","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref16","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1007\/s100640000090","article-title":"Estimating the geotechnical properties of heterogeneous rock masses such as flysch","volume":"60","author":"Marinos","year":"2001","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref17","doi-asserted-by":"publisher","first-page":"1073","DOI":"10.1016\/S1365-1609(00)00041-1","article-title":"A semi-automated methodology for discontinuity trace detection in digital images of rock mass exposures","volume":"37","author":"Reid","year":"2000","journal-title":"Int. J. Rock Mech. Min. Sci."},{"key":"ref18","first-page":"4510","article-title":"Mobilenetv2: inverted residuals and linear bottlenecks","author":"Sandler","year":"2018"},{"key":"ref19","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1016\/j.autcon.2013.08.019","article-title":"Advance optimized classification and application of surrounding rock based on fuzzy analytic hierarchy process and tunnel seismic prediction","volume":"37","author":"Shi","year":"2014","journal-title":"Autom. Constr."},{"key":"ref20","doi-asserted-by":"publisher","first-page":"2347","DOI":"10.3390\/app14062347","article-title":"A novel identification approach using RFECV\u2013Optuna\u2013XGBoost for assessing surrounding rock grade of tunnel boring machine based on tunneling parameters","volume":"14","author":"Shi","year":"2024","journal-title":"Appl. Sci."},{"key":"ref21","doi-asserted-by":"publisher","first-page":"1052117","DOI":"10.3389\/feart.2022.1052117","article-title":"Classifying the surrounding rock of tunnel face using machine learning","volume":"10","author":"Song","year":"2023","journal-title":"Front. Earth Sci."},{"key":"ref22","doi-asserted-by":"publisher","first-page":"6964","DOI":"10.3390\/app13126964","article-title":"Accurate identification of broken rock mass structure and its application in stability analysis of underground caverns surrounding rock","volume":"13","author":"Sun","year":"2023","journal-title":"Appl. Sci."},{"key":"ref23","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1007\/s00603-021-02659-w","article-title":"Simulation of rock-breaking process by drilling machine and dynamic classification of surrounding rocks","volume":"55","author":"Tan","year":"2022","journal-title":"Rock Mech. Rock. Eng."},{"key":"ref24","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1016\/0148-9062(79)91204-x","article-title":"Handbook on mechanical properties of rocks","volume":"16","author":"Vutukuri","year":"1974","journal-title":"Int. J. Rock Mech. Min. Sci."},{"key":"ref25","doi-asserted-by":"publisher","first-page":"69","DOI":"10.11988\/ckyyb.20191526","article-title":"Preliminary discussion on classifying surrounding rockmass considering influence of high ground temperature and geothermal gradient","volume":"37","author":"Wang","year":"2020","journal-title":"J. Changjiang River Sci. Res. Inst."},{"key":"ref26","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1016\/0148-9062(85)93356-x","article-title":"Unified rock classification system","volume":"21","author":"Williamson","year":"1984","journal-title":"Bull. Assoc. Eng. Geol."},{"key":"ref27","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1007\/s12517-020-05320-y","article-title":"Study on stability classification of underground engineering surrounding rock based on concept lattice\u2014TOPSIS","volume":"13","author":"Wu","year":"2020","journal-title":"Arab. J. Geosci."},{"key":"ref28","doi-asserted-by":"publisher","first-page":"3627","DOI":"10.1007\/s10064-018-1368-5","article-title":"Classification model for surrounding rock based on the PCA-ideal point method: an engineering application","volume":"78","author":"Xue","year":"2019","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref29","doi-asserted-by":"publisher","first-page":"200","DOI":"10.13347\/j.cnki.mkaq.2017.06.053","article-title":"Prediction model for stability classification of roadway surrounding rock based on grid search method and support vector machine","volume":"48","author":"Yuan","year":"2017","journal-title":"Saf. Coal Mines"},{"key":"ref30","doi-asserted-by":"publisher","first-page":"2656","DOI":"10.3390\/app12052656","article-title":"Intelligent classification of surrounding rock of tunnel based on 10 machine learning algorithms","volume":"12","author":"Zhao","year":"2022","journal-title":"Appl. Sci."},{"key":"ref31","doi-asserted-by":"publisher","first-page":"1311","DOI":"10.1007\/s11709-024-1134-7","article-title":"Surrounding rock classification from onsite images with deep transfer learning based on EfficientNet","volume":"18","author":"Zhuang","year":"2024","journal-title":"Front. Struct. Civ. Eng."}],"container-title":["Frontiers in Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2026.1766828\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T09:56:59Z","timestamp":1771927019000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2026.1766828\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,5]]},"references-count":32,"alternative-id":["10.3389\/frai.2026.1766828"],"URL":"https:\/\/doi.org\/10.3389\/frai.2026.1766828","relation":{},"ISSN":["2624-8212"],"issn-type":[{"value":"2624-8212","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,5]]},"article-number":"1766828"}}