Review Reports

Round 1

Reviewer 1 Report

The paper is well-written and includes interesting findings for the engineers. The authors proposed a new scheme for damage detection of bridges and create the framework for image-based SHM. The overall quality of the paper is OK to be accepted in this journal 

Author Response

We thank the reviewer for your recognition of our work.

Reviewer 2 Report

The article concerns the application of deep learning to damage detection and the development of a BIM-based framework for bridge damage management. The authors fully explain the objective of their study, being the article well organized and written. Notwithstanding, this reviewer believes the introduction should deal from a more general point of view the necessity of management of existing bridges. Indeed, it is well known the backbone of road infrastructures worldwide has reached its design life and the recently observed collapse worldwide, e.g. [1] and [2], indicate the need of structural assessment and optimization of their management (including damage detection). In addition, the described framework could be in an appropriate manner employed for rapid assessment to perform an initial screening of existing bridges, followed by the application of more refined numerical-based approaches (with a proportional acquisition of more data) as e.g. in [3], to a limited number of cases and possibly the implementation of effective and well-focused monitoring systems in specific situations. 

 

References:

[1] Mitchell, D., J. Marchand, P. Croteau, and W. D. Cook. 2011. “Concorde Overpass Collapse: Structural Aspects.” Journal of Performance of Constructed Facilities, 25 (6): 545–553. American Society of Civil Engineers. https://doi.org/10.1061/(ASCE)CF.1943-5509.0000183.

[2] Scattarreggia, N., R. Salomone, M. Moratti, D. Malomo, R. Pinho, and G. M. Calvi. 2022a. “Collapse analysis of the multi-span reinforced concrete arch bridge of Caprigliola, Italy.” Eng Struct, 251: 113375. Elsevier. https://doi.org/10.1016/J.ENGSTRUCT.2021.113375

[3] Scattarreggia, N., W. Galik, P. M. Calvi, M. Moratti, A. Orgnoni, and R. Pinho. 2022b. “Analytical and Numerical Analysis of the Torsional Response of the Multi-Cell Deck of a Collapsed Cable-Stayed Bridge.” Eng Struct, 265 (114412). https://doi.org/10.1016/j.engstruct.2022.114412.

Author Response

According to the requirements of the reviewers, we have revised the introduction and added the guidance to the introduction. The revised part of the introduction is as follows.

Transportation is an essential service industry and an important part of the modern economic system, and bridges, as the leading engineering of transportation infrastructure, play a vital role in the transportation system. In recent years, bridge accidents caused by the aging of bridges and improper maintenance management have been frequent, and bridge maintenance management has attracted the attention of various countries[1-4]. The U.S. Federal Highway Administration statistical standards show that the total number of bridges built in the United States is about 600,000, of which the proportion of defective bridges is about 25.8%[5]. To store a large amount of archival data on the nation's bridges, the U.S. researched bridge management systems (BMS) earlier[6-8]. Some other regions, such as China, UK, and Germany, are also gradually researching bridge management systems to achieve rapid inspection and quantitative assessment of structural damage of bridges in service[9-15]. Bridge management systems can improve the management efficiency of bridges. However, traditional management systems usually store bridge inspection results separately, and bridge inspection information is separated from bridge entities, lacking accurate and practical assessment and maintenance recommendations.

Construction industry analyst Jerry Laiserin first coined Building Information Modelling (BIM) in 2002. Since then, scholars worldwide have extensively researched BIM's information integration and engineering applications[16-21]. The core of BIM technology is the establishment of virtual 3D models of construction projects. It uses digital technology to build a complete engineering information base to help realise the information integration of building information in the whole life cycle. In the study of bridge health and maintenance, many scholars have considered combining BIM technology with bridge health monitoring and management based on the concept of whole-life bridge construction and proposed constructing a BIM-based bridge management system. For example, Davila Delgado modelled a structural performance inspection system in a building information modelling environment, thus allowing sensor data to be visualised directly on the BIM model[22]; Brendan et al. built a BIM model to connect and analyze data related to bridge inspection, assessment and management to help decision makers better manage bridge health information [23]; Zhou marked the damage information of a steel arch bridge in the 3D model drawn by Revit to achieve rapid automatic assessment of the overall technical condition of the bridge and significantly speed up the bridge inspection[24]. BIM technology, with its convenient interoperability, data information integration and imageability, has greatly improved the efficiency of bridge inspection and structural assessment. However, in the process of system construction, the bridge management system is often set up in a comprehensive way for each structure of the bridge, without considering the complex environment that distinguishes underwater structures from above-water structures, such as sewage environment, sediment, special damages, and others[25-30]. Underwater structures are an essential part of bridges, and their condition significantly affects the safety of their use. As a result of long-term service and external environmental factors, damage to underwater structures has gradually emerged, such as the uneven settlement of foundations due to water scour, cracking and spalling of concrete, and others[31-33]. The New York State Department of Transportation's underwater inspection of local bridges in service revealed that more than half of the bridge collapses were due to large structural damage to the substructure[34]. Underwater structural damage poses a significant threat to bridges' overall structural safety and durability, and scientific monitoring and assessment of the structural health of bridges are urgently needed to ensure the safe operation of bridges. Therefore, it is necessary to develop a bridge underwater structural health management system for the particular characteristics of underwater structures.

Reviewer 3 Report

This paper proposes an identification technique of underwater structural damage and develops a bridge structure damage management system based on BIM technology, among which identifying the underwater structural damage images is the dominant innovation. Thus, I suggest:

(1) Camera underwater calibration experiments are carried out in laboratory environment, and its effectiveness need to verify in underwater structure on-site;

(2) The more distinct images are needed for estimating the effectiveness of calibration, due to the obscure graphic and meaning showed in picture1c;

(3) The verification of lite‑YOLO‑v4 algorithm’s effectiveness should be improved by comparing with other image identifications.

Author Response

1.Response to comment: Camera underwater calibration experiments are carried out in laboratory environment, and its effectiveness need to verify in underwater structure on-site.

Response: According to the principle of camera calibration, the camera is suitable for various scenes after calibration and does not need to be verified in underwater structures.

2. Response to comment: The more distinct images are needed for estimating the effectiveness of calibration, due to the obscure graphic and meaning showed in picture1c.

Response: We have modified the clarity of the image and given the corresponding explanation. (Figure 1. Calibration plate design and results in experimental use of calibration plates: (a)Calibration Plate, (b) Before the picture was corrected, the calibration plate was recessed inward in a curved shape all around, (c) After picture correction, the calibration plate is straight all around.)

3.Response to comment: The verification of lite‑YOLO‑v4 algorithm’s effectiveness should be improved by comparing with other image identifications.

Response: We have already discussed the superiority of our image processing approach in another published paper, which has been cited in this paper.

Thank you for your comments on our paper