Sewer Pipelines Research Articles

An automatic method of siltation depth detection and 3D modeling in water-filled sewer pipelines based on sonar point clouds

Underground sewer pipeline inspection is essential for ensuring the safety and sustainability of urban infrastructure and improving residents’ quality of life. Traditional sewer pipeline inspection methods incur high manual costs. Therefore, this paper proposes an automated siltation depth identification and three-dimensional pipeline model reconstruction using an iterative farthest point removal fitting process based on a sonar point cloud. This method includes interpreting raw sonar data into point cloud data, point cloud preprocessing, iterative farthest point removal fitting process to fit pipeline profile and reverse interpolation of pipeline slices for three-dimensional model reconstruction. This method is validated in real-world applications, successfully calculating and annotating silt depth for pipeline slice profiles. The quantitative analysis shows an error in the estimated siltation proportion fluctuating within approximately 1%. This method also reconstructs the three-dimensional spatial model of siltation in the sewer pipeline, thereby providing effective technical support for engineering applications and reducing manual effort.

Discrete element simulation of ground collapse induced by buried sewage pipeline breakage and soil leakage

The rupture of buried sewage pipelines constitutes a primary triggering factor of ground collapse. To investigate the phenomenon of ground collapse resulting from buried sewage pipeline breakage and soil particle leakage, numerical simulations employing the discrete element method (DEM) are utilized. The study focuses on examining the ground collapse mechanisms in both dry and water-rich sand stratum scenarios. A positive correlation is identified between the particle loss rate (PLR) and the magnitude of ground collapse, and a microscopic analysis of the mechanism of particle loss is carried out to visualize the soil arching process. The entire collapse process shows that the formation and destruction of soil arching are clearly accompanied by the particle loss phenomena. Additionally, the computational fluid dynamics (CFD) module is incorporated into the DEM to investigate the collapse of water-rich sand stratum. Parametric studies involving the burial depth of the pipeline, water pressure, the defect radius of the pipeline breakage, and the defect shapes are comprehensively conducted. It is found that the water pressure is the primary factor influencing the PLR, which progressively increases with higher water pressure, larger radius of pipe defect, and shallower burial depth.

Lightweight Sewer Pipe Crack Detection Method Based on Amphibious Robot and Improved YOLOv8n.

Aiming at the problem of difficult crack detection in underground urban sewage pipelines, a lightweight sewage pipeline crack detection method based on sewage pipeline robots and improved YOLOv8n is proposed. The method uses pipeline robots as the equipment carrier to move rapidly and collect high-definition data of apparent diseases in sewage pipelines with both water and sludge media. The lightweight RGCSPELAN module is introduced to reduce the number of parameters while ensuring the detection performance. First, we replaced the lightweight detection head Detect_LADH to reduce the number of parameters and improve the feature extraction of modeled cracks. Finally, we added the LSKA module to the SPPF module to improve the robustness of YOLOv8n. Compared with YOLOv5n, YOLOv6n, YOLOv8n, RT-DETRr18, YOLOv9t, and YOLOv10n, the improved YOLOv8n has a smaller number of parameters of only 1.6 M. The FPS index reaches 261, which is good for real-time detection, and at the same time, the model also has a good detection accuracy. The validation of sewage pipe crack detection through real scenarios proves the feasibility of the proposed method, which has good results in targeting both small and long cracks. It shows potential in improving the safety maintenance, detection efficiency, and cost-effectiveness of urban sewage pipes.

Field monitoring and numerical simulation for force characteristics of pipe jacking in deep buried moderately weathered slate

Understanding the force characteristics of pipe jacking in rock formations is crucial for ensuring the stability of the structure and construction safety during construction. Yet, very little is explored about its characteristics in rock formations of pipe jacking. This paper presents a case study of constructing a deeply buried moderately weathered slate sewage pipeline using pipe jacking method in Changsha, China. To this end, we propose a novel method to combine field monitoring and numerical simulation representation in an efficacious way. Field monitoring was conducted to investigate the spatial distribution characteristics of jacking force, axial stress, and hoop stress in moderately weathered slate. Numerical simulation methods were employed to discuss the influences of several factors on pipe stress: the pipe-rock contact area, contact relationships at pipe joint interfaces, the height of the jacking force position, and the depth of pipe burial. The results show that hand shield is influenced by the excavation technology, and the distribution of jacking force exhibits a stepped or oscillating upward pattern in moderately weathered slate. The maximum axial stress occurs at the mid-span position of the arched roof of pipe at 30 MPa. Hoop stresses are dominated by compressive stresses with the maximum at-8 MPa. As the pipe burial depth increases, so does the axial stress on the pipe. Lower positioning of the jacking force heightens this stress effect. A larger pipe-rock contact area correlates with reduced axial stress levels. The weakening of the contact interface between pipe joints minimally affects axial stress, as stress primarily transmits through non-weakened areas. To ensure the reliability of the data, automatic monitoring and measuring instruments calibrated for on-site monitoring are used. The results of this study can provide beneficial guidance for the design and construction of pipe jacking.

Enhancing Sewage Pipeline Project Cost Estimations in Iraq through Artificial Neural Network Models

Enhancing Sewage Pipeline Project Cost Estimations in Iraq through Artificial Neural Network Models

Investigation of an Acute Gastrointestinal Illness Outbreak Linked to Drinking Water in a Higher Educational Institute in East Sikkim, India.

Introduction An acute gastrointestinal illness outbreak was reported in a higher educational institution among students and faculties in East Sikkim, India, from January to February 2023. The investigation was conducted to identify the source of the infection and causative pathogens and prevent the spread of the outbreak. Methods We defined a case as three or more loose stools in 24 hours, abdominal pain, or vomiting with the onset of symptoms between January 16 and February 16, 2023. Active surveillance was conducted by reviewing the affected individuals at the campus and patient registers at the dispensary, where cases were treated. Stool samples, rectal swabs, water samples, and suspected food samples were collected for microbiological testing using conventional culture, multiplex polymerase chain reaction (PCR), and kit-based real-time PCR methods. Results Out of 1,850 residents, 106 (5.7%) were affected by gastrointestinal symptoms like diarrhea, vomiting, etc. The attack rate for females was 23 (1.24%) and for males was 83 (4.49%). The most affected individual median age was 21 years (range: 2-51 years). From the laboratory investigations, most of the cases demonstrated polymicrobial etiologies. Gastroenteritis pathogens like Campylobacter, astrovirus, androtavirusdiarrheagenic Escherichia coli (DEC) (EAEC, EIEC, ETEC, EPEC, and EAEC), Shigella,etc., were detected in the suspected samples. The environmental investigation indicated the presence of rusted and leaky water pipes and sewage pipelines, along with ineffective chlorination of the water plant. Conclusions Based on epidemiological and laboratory investigations, it is conjectured that sewage and fecal contamination of drinking water and poor maintenance of the water distribution system most likely caused the outbreak described in this study. Basic treatment modalities, adequate chlorination, and periodic inspection of the water system were suggested, which controlled the outbreak to a greater extent.

Biotransformation of minocycline by a novel strain Kluyvera sp. DM13 isolated from municipal sludge: Performances, removal pathways, and toxicity

Minocycline has been widely used in clinical treatment and its residues were considered to have environmental safety risks due to complex chemical structure. Therefore, it is necessary to find an efficient and environmentally friendly method to remove minocycline from the environment. This study screened and isolated a minocycline degrading strain DM13 from the activated sludge for municipal sewage pipeline, and optimized the biodegradation of minocycline by DM13 under various environmental conditions. The maximum biodegradation efficiency of 50 mg/L minocycline reached 93 % at 72 h with the temperature of 30 °C, the initial pH of 7.0, and the inoculation rate of 3 %. Two potential biotransformation pathways were proposed, including deamination, demethylation, and decarbonylation. The acute toxicity assessment showed that the biotransformation products of minocycline had lower toxicity than the parent compound. In addition, the first-generation tetracycline antibiotics could be removed, suggesting that strain DM13 has the potential for application in treating antibiotic wastewater.

Attention‐optimized 3D segmentation and reconstruction system for sewer pipelines employing multi‐view images

AbstractExisting deep learning‐based defect inspection results on images lack depth information to fully demonstrate the sewer, despite their high accuracy. To address this limitation, a novel attention‐optimized three‐dimensional (3D) segmentation and reconstruction system for sewer pipelines is presented. First, a real‐time sewer segmentation method called AM‐Pipe‐SegNet is developed to inspect defects (i.e., misalignment, obstacle, and fracture) efficiently. Attention mechanisms (AMs) are introduced to improve the performance of segmentation. Second, an attention‐optimized and sparse‐initialized depth estimation network called AM‐Pipe‐DepNet is presented to generate depth maps from multi‐view images. Third, a 2D‐to‐3D mapping algorithm is proposed to remove noise and transform the sewer segmentation results into 3D spaces. Comparison experiments reveal that incorporating AMs into the network significantly enhances pipe segmentation and 3D reconstruction performance. Finally, two digital replicas of real sewer pipes are built based on photos taken by probes, providing valuable insights for sewer maintenance.

Crack monitoring for municipal sewer pipes using DFOST

The distributed fiber optic sensing technique (DFOST) provides an effective solution for long-term deformation monitoring of sewer pipelines under operation conditions due to occurrence of cracks. However, optical fibers attached on the pipeline wall might fail to capture all cracks as they are usually highly spatially scattered, resulting in misjudgment of pipe structural conditions. To address this challenge, this paper presents a novel deployment approach of distributed fiber optic monitoring system for pipeline cracks. First, the working principle of optical frequency domain reflectometry (OFDR) is explained. Second, the production of concrete pipe specimens with different optical fiber sensors deployment schemes is shown in detail. Finally, a comprehensive analyse of the results of visual inspection and fibre optic monitoring is completed. To evaluate the performance of this approach, experimental data from various cases were considered. The conclusion of this paper provides theoretical and empirical support for the application of distributed fiber optic sensors for pipeline cracks monitoring.

Effect of zeolite and bamboo biochar as CO2 absorbant in concrete

Carbon dioxide (CO2) is one of the major air pollutants that enter the atmosphere. There is a large release of carbon dioxide into the atmosphere as a result of burning fossil fuels in the cement manufacturing industries and many other industries, as well as emissions from gridlock. This increase in CO2 concentration in the atmosphere leads to various ill effects and global warming. To reduce the CO2 level in the atmosphere, efforts were made to prepare concrete that can absorb CO2 by addition of zeolite and bamboo biochar. These materials were chosen because zeolite and bamboo biochar have large pore volume and large specific surface area and so they can absorb more CO2. Zeolite is having more oxygen content and bamboo biochar is having more carbon content which helps in CO2 absorption. In this work, Zeolite is substituted for fine aggregate in the varying ratios of 25% and 50% and bamboo biochar is substituted for cement in the ratios of 0.5%, 1% and 1.5%. The strength properties and CO2 absorbing capacities of various zeolite and bamboo biochar concrete ratios were compared and it was found that concrete with 50% zeolite and 1% bamboo biochar (ZB5) was the optimal mix. The optimal mix was found based on compressive strength, split tensile strength, water absorption, impact strength, amount of CO2 absorption and depth of CO2 penetration in concrete. This optimal mix has a compressive strength of 38.49 MPa which is 7.48% higher than conventional concrete and also has a split tensile strength of 4.39 MPa which is 15% higher than conventional concrete. It was also found that the optimal mix absorbed 1.2 g of CO2 per day and that the depth of CO2 penetration was 15 mm when the concrete cube was kept in the carbonation chamber for 7 days. This study provided necessary information on the addition of zeolite and bamboo biochar in the concrete which enhances both strength properties and CO2 absorption. This study is important because now-a-days the current CO2 emission in the atmosphere is mainly due to several man-made activities. This ZB concrete provides a solution to reduce the amount of CO2 in the atmosphere and can be used in the concrete pavements, sewer pipelines, parapet walls and the environments with higher CO2 concentration and emission.Graphical

PipeTransUNet: CNN and Transformer fusion network for semantic segmentation and severity quantification of multiple sewer pipe defects

With the continuous development of urbanization, the service life of sewer pipes is gradually approaching a critical threshold. Defects within pipe networks can significantly affect the municipality operations and residents' quality of life. Towards efficient and automatic sewer pipeline inspection, an integrated framework was proposed for semantic segmentation and severity quantification of multiple sewer pipe defects using the PipeTransUNet model that fuses convolutional neural networks with Transformer. The capability of the fusion network to extract and localize sewer defects was further enhanced by incorporating the convolutional block attention module and improving the activation function. In extensive experiments, PipeTransUNet shows enough competitiveness after hyperparameter selection, architecture tweaks, and comparison with other state-of-the-art models. Specifically, PipeTransUNet outperformed other models in terms of both quantitative and visual evaluations, with mean intersection over union, the mean of pixel accuracy, mean pixel accuracy, mean recall, mean F1-score, mean specificity, mean Kappa, and frequency-weighted intersection over union values reaching 71.92%, 84.90%, 80.74%, 85.93%, 83.05%, 84.44%, 55.55%, and 91.32%, respectively. A severity level assessment method for different sewer defects was developed based on PipeTransUNet and compared with expert reviews' results to demonstrate its feasibility and effectiveness. Moreover, the in-depth image features extracted from the segmentation head of our proposed model were visually evaluated and interpreted using pixel-level gradient-weighted class activation mapping. In summary, PipeTransUNet canrecognize complex defects well and provide a solid foundation for inspecting and maintaining sewer pipe networks.

High caffeine levels in old sewer system waters reveal domestic wastewater leakage

Infrastructure deterioration is a threat to developed countries, emphasizing the need for effective management techniques. In particular, the leakage of aged domestic sewer pipeline is a major health issue, yet there is a lack of markers to identify domestic leakage. We studied the pollution in urban waters resulting from domestic sewage leakage into storm drainages. We monitored caffeine, fragrance substances and polycyclic aromatic hydrocarbons (PAHs) in the storm discharge points in five urban districts having separate sewer systems aged from 10 to over 40 years. Results show that caffeine and fragrance concentrations tended to increase with sewer system age. For instance, caffeine concentrations in the areas of sewer systems over 40 years old were at least two orders of magnitude higher than in 10-year-old sewer systems, and were as high as 1–10% of domestic sewage, strongly suggesting the leakage of domestic sewer pipelines. PAHs exhibited consistent patterns across the districts. Overall, we observe that sewer leaking processes can be distinguished by analyzing the levels of organic pollutants.

Estimation of possible locations for green roofs and bioswales and analysis of the effect of their implementation on stormwater runoff control

IntroductionThe objectives of this study are twofold. The first is to identify potential green infrastructure construction sites by building rooftops and sidewalks. The second is to analyze internal flooding for a wide range of drainage areas and to quantitatively evaluate the effectiveness of stormwater runoff control.MethodsThe target area has approximately 600 ha with a runoff coefficient of 0.71. Using Arc GIS Desktop 10.8.1, this study has identified green roofs and bioswales that would be highly beneficial in capturing large amounts of rainfall. In addition, Info Works ICM was used for the inundation analysis, which can simultaneously calculate the flow in sewer pipelines and above-ground inundation flow. Runoff coefficients were calculated for each land use using the urban land use subdivision mesh data with 100 m unit. This study targeted a 10-year probability rainfall (total rainfall: 86.3 mm, maximum hourly rainfall: 52.3 mm/h, duration: 3 h) with a middle concentrated rainfall waveform obtained from past experiments in the d4PDF database of ensemble climate prediction contributing to global warming.ResultsThe amount of land availability for green roofs and bioswales was about 1 and 0.1% of the drainage area, respectively. The runoff coefficients for green roofs only, bioswales only, with and without introduction of both green roofs and bioswales were 70.34, 70.87, 70.28, and 70.93%, respectively. The difference in runoff coefficients was about 0.65 percentage points even when both were constructed. As a result of inundation analysis, the reduction was 2.5% for the maximum waterlogged area, 1.5% for the flooded area, and 0.7% for the average depth of waterlogging divided by the maximum waterlogged area. The construction of green roofs and bioswales in the same area or downstream of the area shows little mitigation effect when flooding occurs in an area near the downstream end of the sewer network.DiscussionAlthough this study has mainly discussed the stormwater runoff control aspect, the most important feature of green infrastructure is its multifunctionality. In terms of utilizing and promoting green infrastructure, it is important to visualize its multifaceted effects and share them with many stakeholders.

Reliability-based life prediction method for concrete sewage pipelines under microbial attack

The durability of concrete sewage pipelines decreased for microbial attack. In this study, a method for life prediction of concrete sewage pipes under microbial-induced attack was developed by combining numerical simulation with theoretical analysis. A mathematical model of the corrosion depth of microbial erosion of concrete was constructed based on the cumulative model of sulfate concentration on the surface of sewage pipes and verified the applicability by comparing it with the experimental results. The Monte Carlo (MC) life prediction method and Fuzzy- Monte Carlo (F-MC) life prediction method for concrete sewage pipes was established based on the reliability theory and fuzzy methodology. Several factors are considered as random variables and analyzed parametrically, including hydrogen sulfide concentration (SH2S), diffusion coefficient (D), and thickness of the concrete cover (CC). In addition failure event was consider as a fuzzey event. It was found that: The increasing SH2S and D result in higher failure rates and lower service life, however as the increase of the thickness of CC, there is the opposite effect. The results of the numerical simulations revealed a NR between the three kinds of parameters and the probability of failure of sewer pipes. Compared with the MC model, the F-Mc model produces more conservative but not minimal different results, which confirms the applicability of the F-MC model.

A Case Study of Pavement Foundation Support and Drainage Evaluations of Damaged Urban Cement Concrete Roads

Surface cracks and joint deteriorations are typical premature failures of urban cement concrete pavement. However, traffic loads on the urban pavement are much lower than those on highways. Limited research has been conducted to investigate the causes of accelerated damage in urban cement concrete roads. To investigate the foundation issues that may cause the accelerated damage of urban cement concrete pavements, in this study, field evaluations were conducted to assess pavement foundation support and drainage conditions. Field visual inspections, Ground Penetrating Radar (GPR) survey, Dynamic Cone Penetrometer (DCP) test, and the Core-Hole Permeameter (CHP) test were performed. In urban residential areas with inadequate subgrade bearing capacity, cement concrete pavements are prone to early damage. Foundations with a higher content of coarse particles exhibit a higher CBR value, which can extend the service life of the pavement. The compaction of foundation materials near sewer pipelines and manholes is insufficient, leading to non-uniform support conditions. Moreover, the permeability of the foundation material can influence the service life of pavement surface structures. Foundation materials with fewer fine particles enhance drainage performance, contributing to a longer service life for PCC pavements. In areas with inadequate drainage, water accumulation reduces the bearing capacity of the foundation, thereby accelerating pavement deterioration. The poor bearing capacity and drainage conditions of the foundation lead to cavities between the surface layer and foundation material thus yielding stress concentrations on the pavement surface, which cause the formation of pavement surface cracks.

Predicting Microbiologically Influenced Concrete Corrosion in Self-Cleansing Sewers Using Meta-Learning Techniques

Sewer networks are not only necessary as an infrastructure for human societies, but they can also help humans achieve a stable situation with the surrounding natural environment by controlling and preventing the spread of pollution in the environment. As a result, concrete sewer maintenance and analysis of their damaging elements are critical. In this regard, modeling microbiologically influenced corrosion (MIC) is a challenging phenomenon. Due to the complicated aspects related to the interaction of microorganisms and concrete degradation, this research suggests several machine-learning models as well as traditional multiple linear regression model to predict the MIC in sewer pipelines. The models can be categorized into three sections: (i) stand-alone models (group method of data handling, generalized regression neural network, radial basis function neural network, multilayer perceptron neural network, chi-square automatic interaction detection, and classification and regression tree); (ii) integrative models (adaptive neuro-fuzzy inference system and support vector regression with particle swarm optimization, artificial bee colony, and firefly algorithm); and (iii) ensemble meta-learner stepwise regression (SR) model. After implementing the models, statistical measures, including root mean square error, mean absolute error, mean bias error, Pearson correlation coefficient, and Nash-Sutcliffe model efficiency are considered for evaluating models’ performances. The results indicate that the ensemble meta-learner-SR model is significantly more precise than other models. They also demonstrate that using an integrative model can improve the accuracy of stand-alone models by at least up to 42%. The durability and lifespan of the sewer system are also estimated with the aid of the best predictive model (meta-learner-SR) for two scenario cases of (i) gas phase and (ii) submerged conditions. It is concluded that the sewer systems have a considerably lower life span (24 y less) exposed to submerged sewage than the gas phase with 56 y of durability.

Experience in construction and operation of sewerage networks in areas with deep seasonal ground freezing across different regions of the Russian Federation

Reviewing the experience in construction and operation of sewerage networks in areas with deep seasonal ground freezing across different regions of the Russian Federation. Methods: analyzing the sewer pipeline laying, in-situ observations of wastewater temperature regime at sewer outlets from residential buildings. Results: designs of sewer pipelines laid in different regions are presented, as well as their dependence on the depth of seasonal ground freezing in different regions of the Russian Federation. The actual values of sewage water temperature at sewage outlets from residential buildings are established. Practical significance: In a number of regions the laying of sewerage pipes at a depth of 1.5-1.8 m led to normal and stable operation of sewerage pipelines. In terms of capital expenditures, sewer pipelines account for more than 50% of the estimated cost of the entire wastewater disposal system. Their operation claims a significant share of the costs.

Effects of flow rate and wastewater concentration on the transformation of nitrogen in sediment-water system of sewage pipelines.

In this work, the transformation law of nitrogen in sediment-water system under different flow rates and wastewater concentrations were investigated in a simulated sewage pipeline system. Results showed that the different flow rates and wastewater concentrations in the pipeline caused differences in microbial community in sediments and nitrogen transformation. When the flow rate increased from 0.05 to 0.2 m/s, the scouring effect was enhanced, resulting in higher concentrations of NH4 + -N and NO3 - -N in the overlying water. At 0.2 m/s, the relative abundance of Clostridium_sensu_stricto_1 in sediments was higher, resulting in a greater conversion of amino acid nitrogen (AAN) to NH4 + -N. Meanwhile, many denitrifying bacteria (Trichococcus, Dechloromonas, norank_f__norank_o__Gaiellales, Thiobacillus) had high relative abundance in the sediments, and the denitrification process was common. When the wastewater concentration was high, the nitrification reaction was great in overlying and interstitial water. Moreover, the ammoniation process was great in the sediments, and the variation flux of AAN was large (remarkably reduced). PRACTITIONER POINTS: AAN transformed to NH4 + -N in sediment under different flow rate and concentration. Scouring was enhanced at 0.2 m/s, increasing nitrogen contents in overlying water. Difference in microbial community led to more AAN conversion to NH4 + -N at 0.2 m/s. The ammoniation process was greater in sediment at a high concentration of sewage. NH4 + -N migrated from overlying water to sediment at a high concentration of sewage.

Proactive exfiltration severity management in sewer networks: A hyperparameter optimization for two-tiered machine learning prediction

Effective management of aging sewer pipelines requires accurate analysis of sewer pipeline exfiltration. Previous research studies have not paid attention to proposing an index to represent the exfiltration severity. To this end, this study introduces a novel Exfiltration Severity Index (ESI) by considering the frequency and severity of defects captured from Closed-Circuit Television (CCTV) reports. To address the need for a proactive tool that automates the exfiltration estimation and reduces dependence on CCTV reports, we leverage Machine Learning (ML) and Deep Learning (DL) models to predict sewer exfiltration occurrence and severity. In this regard, our proposed methodology comprises a series of steps that start by computing the ESI of pipeline segments considering the frequency, type, and severity of defects. After that, physical, environmental, and climatic factors influencing pipeline exfiltration are gathered and aggregated to build predictive models. We compare the performance of six ML models and two DL models, developed in two tiers to predict exfiltration occurrence and severity, respectively. The hyperparameters of each model are optimized using GridSearchCV to enhance prediction accuracy. Among the eight algorithms, the light gradient-boosting machine performs best, with 71% and 85% accuracy in the first and second tiers, respectively. Furthermore, our study investigates the influence of various factors on pipeline exfiltration and reveals that pipe diameter and population have the most significant impact on exfiltration occurrence and severity. Our method provides a valuable tool for managing sewer pipeline exfiltration and can be utilized to prioritize sewer network maintenance and repair efforts.

SewerOD: A visual sewer disease detection dataset for machine learning

The underground sewer pipeline is an essential urban infrastructure that undertakes the vital responsibility of discharging sewage. The location and type of diseases in the pipeline often need to be checked manually by inspectors, which cannot be completed efficiently due to labor costs and time requirements. With the development of computer vision, the use of detection technology to maintain sewer pipelines has extremely high research value. However, image data of pipelines are often regarded as commercial secrets, and these studies are greatly limited due to the scarcity of open-source pipeline disease datasets. To solve this problem, we present a public large-scale object detection dataset for sewer disease detection named SewerOD in this work. The dataset contains about 47K images, annotated by professional researchers, and includes two of the most widespread structural disease types: Corrosion and Crack. Our dataset is available at https://github.com/SewerOD.