桥(图论)
克里金
结算(财务)
土木工程
可靠性(半导体)
压实
岩土工程
工程类
结构工程
下沉
环境科学
计算机科学
地质学
医学
内科学
古生物学
功率(物理)
物理
量子力学
构造盆地
机器学习
万维网
付款
标识
DOI:10.2478/amns-2024-0932
摘要
Abstract This study addresses quality control challenges in municipal road and bridge construction by introducing an intelligent monitoring approach. Utilizing three-dimensional laser scanning, we monitor roadbed settlement and deformation accurately. Compaction quality is assessed through vibration acceleration metrics from milling operations, applying a compaction monitoring value. Furthermore, a combination of regression models and stochastic processes in a Kriging function model evaluates the reliability of detecting bridge steel corrosion. In J city’s political road bridge analysis, we observed a differential settlement with the least affected areas showing subsidence within 250mm. In contrast, the most impacted point, B1, recorded a settlement of 2597mm in December. Compaction quality monitoring revealed that control error margins for E and CV indicators lie between −2.65% to 2.35% and −2.7% to 2.6%, respectively, demonstrating a narrower error range for E compared to CV.
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