摘要
由一名新闻记者兼机器人与机器学习的新闻编辑每日新闻-调查人员发布了关于马学习的新报告。根据NewsRx记者来自密歇根州卡拉马祖的新闻报道，研究表明，“混凝土桥面是关键的交通基础设施部件，退化可能危及结构完整性和公共安全。本研究利用国家桥梁清单(NBI)开发了机器学习(ML)m odel，以分类桥面状况并预测退化轨迹。”我们的新闻编辑从西密歇根大学的研究中获得了一句话：“从1992年到2015年，在密歇根州超过28786个BRI DGE的检查记录上对模型进行了测试和培训。在基于10倍交叉验证的超参数优化后，评估了11种方法，包括逻辑回归、梯度提升、AdaBoost、随机森林、额外树、Kneest neighbors、Naive Bay结果表明，优化后的CatBoost分类器在甲板条件下的测试准确率达到96.66%，超参数优化的引入显著提高了模型的整体预测性能，保证了模型的鲁棒性和可靠性。有助于更全面地了解影响桥面状况等级的因素。这些见解为延长使用寿命的预防性维护计划提供了依据。这项工作开创了一个数据驱动的框架，以预测混凝土劣化，摘要:利用机器学习技术对混凝土桥面板进行预测,以评估桥梁的剩余使用寿命,从而为基础设施的可持续发展提供决策支持。这些模型将有助于提高桥梁的安全性、效率和使用寿命。并通过为桥梁维护和管理提供及时的信息和基于证据的决策来维持桥梁基础设施的能力。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on Ma chine Learning. According to news reporting originating from Kalamazoo, Michigan , by NewsRx correspondents, research stated, “Concrete bridge decks are critical transportation infrastructure components where deterioration can compromise str uctural integrity and public safety. This study develops machine learning (ML) m odels using the National Bridge Inventory (NBI) to classify deck conditions and predict deterioration trajectories.” Our news editors obtained a quote from the research from Western Michigan Univer sity, “Models were tested and trained on inspection records from over 28,786 bri dges in Michigan over 23 years, from 1992 to 2015. Eleven approaches were evalua ted after hyperparameter optimization, based on 10-fold cross-validation, includ ing logistic regression, gradient boosting, AdaBoost, random forest, extra trees , Knearest neighbors, naive Bayes, decision tree, LightGBM, CatBoost, and baggi ng. Model effectiveness was assessed using accuracy, recall, F1-score, and area under the curve. Results indicate the optimized CatBoost classifier achieved 96. 66% testing accuracy in rating deck conditions. The incorporation of hyperparameter optimization has significantly enhanced the overall predictive performance of the models, ensuring robust and reliable deterioration forecasti ng. The research sheds light on crucial factors such as deck age, area, and aver age daily traffic, contributing to a more comprehensive understanding of the fac tors influencing bridge deck condition ratings. These insights inform preventati ve maintenance planning to extend service life. This work pioneers a data-driven framework to forecast concrete deterioration, empowering officials with precise predictions to optimize infrastructure management under budget constraints. The approach provides a promising decision-support tool for sustainable infrastruct ure. This paper explores the use of machine learning techniques for the deterior ation prediction of concrete bridge decks to estimate the remaining service life of bridges. These models will contribute to the safety, efficiency, and sustain ability of bridge infrastructure by providing timely information and evidence-ba sed decision making for bridge maintenance and management.”
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