基桩完整性等级AI自动判别训练数据集

采集的数据一方面主要来源于公司在各类公路、铁路、建筑等工程项目中开展的基桩完整性检测工作，利用自研仪器采集的声波透射法原始波形数据，数据真实可靠且具备工程代表性。另一方面来源于与科研院所、检测机构的合作，通过共享或联合研究获得典型桩身缺陷波形。两类数据结合，既能保证工程现场的实用性，又能补充实验条件下的典型样本，确保数据多样性和全面性，为算法模型的训练和验证提供坚实基础。

The collected dataset primarily comes from two sources: on one hand, it originates from pile integrity testing conducted by the company on various engineering projects including highways, railways and construction works, with raw waveform data acquired via the cross-hole sonic logging method using self-developed instruments being authentic, reliable and representative of actual engineering practice; on the other hand, it includes typical waveforms of defective piles obtained through data sharing or joint research with research institutes and testing and inspection institutions. By integrating these two types of data, the dataset not only ensures the practicality of on-site engineering applications, but also supplements typical samples collected under controlled laboratory conditions, thereby guaranteeing the diversity and comprehensiveness of the dataset and providing a robust foundation for the training and validation of algorithmic models.

搜集汇总

背景与挑战

背景概述

该数据集是一个用于训练AI模型自动判别混凝土基桩完整性等级的训练数据集，包含194条声波透射法波形数据，来源于工程现场检测和典型缺陷样本，确保数据真实且多样。它适用于公路、铁路及建筑工程桩基质量智能评定，旨在通过自动化判别正常、轻微畸变、明显畸变、严重畸变四类波形，提升检测效率与准确性，解决传统人工判定工作量大和主观性强的问题。