Smart structures require novel, efficient, and effective technologies for their safe operation and serviceability. This paper presents a novel, practical, cost-effective, and field test-based methodology using portable cameras and computer vision technologies to identify the lateral live load distribution factors for the existing highway bridges to perform load rating. By using a computer vision-based measurement method and traffic recognition, the girder deflection under live load can be monitored in a noncontact way and can be utilized to derive the load distribution. To verify the feasibility of the proposed approach, a comparative experimental study is conducted on a real-life pre-stressed concrete bridge with a set of conventional load tests and experiments in normal traffic. The results are compared with the conventional approach, such as simplified formulations recommended by AASHTO specifications, and the experimental method using the data from strain gauges and a calibrated finite element model (FEM). The comparative results show that the proposed approach can obtain very similar load distribution factors and bridge load rating factors both in a conventional load test and normal traffic. In comparison to the simplified formulation recommended by AASHTO specifications, the proposed approach can reflect the real-life structural properties and improve the load rating factor of AASHTO specifications by around 12%. In addition, as compared to the load-test-based approaches, such as using strain data and calibrated FEM, the proposed approach does not require traffic closure and a large amount of effort to deal with the load test and model updating. The bridge studied in this paper represents a very typical one from a large population of bridges that are part of the smart infrastructure. Such a practical approach will be practical and cost-effective for bridge load rating in smart cities.

智能结构需要新颖，高效，有效的技术来确保其安全运行和可维护性。本文介绍了一种新颖，实用，具有成本效益且基于现场测试的方法，该方法使用便携式摄像机和计算机视觉技术来识别现有公路桥梁的横向活荷载分布因子，以进行额定荷载。通过使用基于计算机视觉的测量方法和交通流量识别，可以以非接触方式监视在活荷载下的梁变形，并可以用来得出荷载分布。为了验证所提方法的可行性，在真实生活中的预应力混凝土桥梁上进行了对比实验研究，其中包括一组常规载荷测试和正常交通中的实验。将结果与常规方法进行比较，例如AASHTO规范推荐的简化配方，以及使用应变计和校准有限元模型（FEM）数据的实验方法。对比结果表明，该方法在常规的载荷测试和正常交通中均可获得非常相似的载荷分配因子和桥梁载荷额定因子。与AASHTO规范推荐的简化公式相比，所提出的方法可以反映现实生活中的结构特性，并将AASHTO规范的额定载荷系数提高约12％。此外，与基于负载测试的方法（例如使用应变数据和校准的有限元法）相比，该方法不需要流量封闭，也无需花费大量精力来处理负载测试和模型更新。本文研究的桥梁代表了作为智能基础设施一部分的大量桥梁中的一个非常典型的桥梁。对于智慧城市中的桥梁额定载荷而言，这种实用的方法将是实用且具有成本效益的。