Manhole Cover Research Articles

Development of Sustainable and Innovative Manhole Covers in Fibre-Reinforced Concrete and GFRP Grating

In several countries, manhole covers made of steel are being stolen, with significant economic losses for private and public entities, and even causing accidents. In this work, a new manhole cover is developed using fibre-reinforced cementitious (FRC) materials and glass fibre-reinforced polymer (GFRP) gratings. Since the GFRP gratings are immune to corrosion, and FRC is a relatively low-cost material, manhole covers in FRC reinforced with GFRP gratings are durable and not so appealing to be stolen as those made from steel. An experimental program with manhole cover specimens made with two types of FRC and two types of GFRP gratings was executed by investigating the strength, stiffness and post-cracking tensile capacity of the FRCs and the stiffness and flexural capacity of the two GFRP gratings. It was demonstrated that the developed manhole cover concept can be of class A15 up to D400 according to the recommendations of BS EN 124:1994.

New approach for zero energy industries via integrating energy management and renewable energy system: Enviro-economic investigation

The building sector stands as the world’s predominant energy consumer and major contributor of CO2 emissions. In the recent years, the Zero Energy Building (ZEB) concept has been gaining traction as a solution to this challenge. However, while the concept has been primarily used for residential buildings where it has shown promising results, its application in the industrial sector has not been extensively explored. So, the current study proposes a sustainable approach to achieve zero energy for an industrial site using PV system. The approach is applied on a manhole cover manufacturing company located in Alexandria, Egypt. Firstly, an energy audit is conducted to identify the energy consumption patterns and energy waste sources in the facility. Next, an energy management where various energy saving measures are implemented to optimize energy use in the facility follows. Then, an 850kWp grid connected PV array is designed and sized using PVsyst simulation tool to supply the remaining net annual energy consumption of the industrial site. The results show that the energy management reduces the entire facility’s energy consumption by about 20 % with a yearly saving of energy, cost, and CO2 emission reduction of 425.3MWh, 22,536$, and 850.4 tons, respectively. Moreover, the proposed PV system proves its reliability and efficiency to supply slightly more than the remaining facility energy needs of 1257.74 MWh/year thus achieving zero energy status. With an investment of 211,480 USD, a payback period of about 4 years is estimated with a Life Cycle Emission of 18,444 tons CO2 reduction. The study shows that achieving Zero Energy Industries (ZEI) is technically and economically feasible by combining energy management and Renewable Energy Systems (RES). Besides its contribution to SDG’s 7 and 13, the study elaborates the significance of implementing ISO 50001 towards energy efficient practices in industrial sites thus providing valuable insights and recommendations to various stakeholders and policy makers.

Improved YOLOX-based detection of condition of road manhole covers

Manhole cover damage poses significant threats to road safety and infrastructure integrity, necessitating timely detection and repair. To address this, we introduce an enhanced YOLOX model integrated with ECA (High Efficiency Channel Attention) modules for real-time monitoring using car recorder footage. Our method categorizes manhole cover conditions into three distinct states: normal, broken, and down. By in-corporating ECA-Net before the decoupling head of the YOLOX model, we significantly boost its channel feature extraction abilities, critical for distinguishing subtle changes in cover conditions. Experimental results reveal a substantial increase in mean Average Precision (mAP) to 93.91%, with a notable AP of 92.2% achieved in the detection of the ‘down’ state, historically the most challenging category. Despite the en-hancements, our model maintains a high detection speed, processing at an average rate only five images per second slower than the original YOLOX model. Comparative analyses against leading detection models, in-cluding Faster R-CNN, SSD, and CenterNet, underscore the superiority of our approach in terms of both accuracy and speed, particularly in accurately recognizing the ‘down’ condition of manhole covers. This in-novative model provides a reliable tool for swiftly identifying damaged manhole covers and their precise lo-cations, enabling prompt maintenance actions. By improving the monitoring efficiency of urban infrastruc-ture, our solution contributes to enhanced road safety and the advancement of smart city technologies.

Deep learning-based predictive models for pavement patching and manholes evaluation

ABSTRACT Deep Learning (DL) techniques have been applied to the processing and evaluation of pavement conditions. DL-based predictive models are rapid and have higher accuracy compared to traditional methods. In this research, some models were developed to appraise pavement patching by considering the severity and the manhole cover utilising images collected by the Road Surface Profiler (RSP) from Bus Rapid Transit (BRT) lines in Tehran, Iran. Two cases (scenarios) were regarded in this research: 1) Patching without considering the severity, and 2) Patching by considering the severity. YOLOv5, YOLOv6, and YOLOv7 as one-stage object detection algorithms, and Faster R-CNN (ResNet-50) and Faster R-CNN (MobileNet_v3) as two-stage object detection algorithms were evaluated to choose the desired algorithm. The results exhibited that the trained model on YOLOv5 has better performance and is faster than the rest of the algorithms. In addition, the influence of the classes’ combination on model performance was scrutinised. Based on the results of the other classes, the outcomes of the study suggested that the model can detect and train better when the model is trained by a dataset with all classes. Furthermore, the performance of the model was also investigated by examining the effect of image size and YOLOv5's architectures. Based on the obtained results, YOLOv5 and an image size of 640*640 outperformed the rest. In Case 1, the precision, recall, F1-score, and mAP were 75.8%, 67.4%, 71.35%, and 77.4%, respectively. Moreover, the values of these metrics in Case 2, were 70%, 61.2%, 65.3%, and 64.4%, respectively. Hence, the results demonstrated that the developed model can be used with high performance in the detection and evaluation of pavement patching by severity, and manholes in Pavement Management Systems (PMS) and maintenance and rehabilitation plans.

A field study of manhole cover leakage.

Leaking manhole covers add inflow to sanitary sewer systems. These are the most accessible components of the sewer system, and so potentially the cheapest to repair or modify, to reduce inflow. There is, however, very little data regarding manhole cover leakage available to evaluate the cost-benefit of such an approach, and there is no field data. So, a multi-year field study of inflow was conducted. Two-hundred and fifty manholes in New York City and surrounding towns were tested. A key field observation in this study was that a dust and grit seal typically built up between the cover and the rim. This seal had a big impact on leakage rates. This made testing in-situ a necessity, and the test method had to preserve this seal. Due to the presence of the dust and grit seal, approximately 80% of the manholes tested showed no leakage. A further 15% had slow leaks. Only 5% had significant leaks.

Manhole Cover Classification Based on Super-Resolution Reconstruction of Unmanned Aerial Vehicle Aerial Imagery

Urban underground pipeline networks are a key component of urban infrastructure, and a large number of older urban areas lack information about their underground pipelines. In addition, survey methods for underground pipelines are often time-consuming and labor-intensive. While the manhole cover serves as the hub connecting the underground pipe network with the ground, the generation of underground pipe network can be realized by obtaining the location and category information of the manhole cover. Therefore, this paper proposed a manhole cover detection method based on UAV aerial photography to obtain ground images, using image super-resolution reconstruction and image positioning and classification. Firstly, the urban image was obtained by UAV aerial photography, and then the YOLOv8 object detection technology was used to accurately locate the manhole cover. Next, the SRGAN network was used to perform super-resolution processing on the manhole cover text to improve the clarity of the recognition image. Finally, the clear manhole cover text image was input into the VGG16_BN network to realize the manhole cover classification. The experimental results showed that the manhole cover classification accuracy of this paper’s method reached 97.62%, which verified its effectiveness in manhole cover detection. The method significantly reduces the time and labor cost and provides a new method for manhole cover information acquisition.

Research on Point Cloud Structure Detection of Manhole Cover Based on Structured Light Camera

This study introduced an innovative approach for detecting structural anomalies in road manhole covers using structured light cameras. Efforts have been dedicated to enhancing data quality by commencing with the acquisition and preprocessing of point cloud data from real-world manhole cover scenes. The RANSAC algorithm is subsequently employed to extract the road plane and determine the height of the point cloud structure. In the presence of non-planar point cloud exhibiting abnormal heights, the DBSCAN algorithm is harnessed for cluster segmentation, aiding in the identification of individual objects. The method culminates with the introduction of a sector fitting detection model, adept at effectively discerning manhole cover features within the point cloud and delivering comprehensive height and structural information. Experimental findings underscore the method’s efficacy in accurately gauging the degree of subsidence in manhole cover structures, with data errors consistently maintained within an acceptable range of 8 percent. Notably, the measurement speed surpasses that of traditional methods, presenting a notably efficient and dependable technical solution for road maintenance.

Utilizing waste manhole covers and fibreboard as reinforcing fillers for thermoplastic composites

This study investigated the potential of using recycled manhole cover flour (CMC) and medium density fibreboard (MDF) sawdust as low cost fillers for recycled polypropylene (PP) composites. Various compositions of CMC and MDF flour, up to 40 wt% (20 wt% CMC and 20 wt% MDF), were compounded with recycled PP using maleic anhydride grafted PP. The results showed that increasing the filler content improved the bending strength and modulus, reaching 46.4 MPa and 3399 MPa, respectively, at 40 wt% loading. However, the tensile strength decreased slightly with the addition of 10 wt% filler and showed a slight decrease with the increase in filler content from 20 to 40 wt%. The water absorption of the composites showed only a slight increase from 0% for pure PP to 0.007% for composites with 40 wt% filler. Thermogravimetric analysis confirmed the thermal stability of the composites up to about 400°C. This study demonstrated the potential of using a blend of waste CMC and MDF flour up to 40 wt% to improve the strength and water resistance of recycled PP composites, offering a promising and sustainable approach to cost-effective composite production.

Manhole cover design based on eco-community

By conducting a thorough review of manhole cover development in more advanced regions, conducting extensive field research, and utilizing comparative analysis of typical cases, we have successfully formulated innovative and culturally-rooted manhole cover design schemes. These designs are not solely focused on functionality, but also strive to enhance the overall landscape atmosphere and amplify the exhibition of unique eco-community cultural elements. Our primary objective is to provide a concrete and invaluable reference for future landscape design projects, encouraging the seamless integration of cultural aesthetics into practical eco-community infrastructure. By doing so, we aim to foster a more harmonious and culturally vibrant urban environment that not only meets the functional needs of its inhabitants but also reflects and celebrates the rich tapestry of eco-community culture. Ultimately, our goal is to contribute to the creation of cities that are not only functional and efficient but also visually stunning and deeply connected to their cultural heritage.

A Progressive Feature Fusion-Based Manhole Cover Defect Recognition Method

A Progressive Feature Fusion-Based Manhole Cover Defect Recognition Method

Automated detection and quantification of pavement cracking around manhole

Damage detection plays an important role in pavement health monitoring and inspection. Unfortunately, research about damage detection of the special component of pavement structures, such as pavement manhole covers, is relatively few. A new pipeline for the detection and quantification of damage around the pavement manhole covers is proposed in this research. In this pipeline, the Attention-enhanced Manhole Detection Model (AMDM) is proposed to detect manhole covers. AMDM achieves an ideal balance between accuracy and speed by eliminating redundant structures and incorporating an attention mechanism. The BCSM (Boundary-enhanced Crack Segmentation Model) is proposed to segment the damage around the manhole cover, and the boundary loss function is used to enhance the fine segmentation ability of the model on the boundary. The MAP (Mean Average Precision) of the manhole covers detection model is 96.68%, and the MIOU (Mean Intersection Over Union) of the crack segmentation model is 89.73%. Attributing to this efficient and accurate pipeline, a reasonable damage evaluation method is proposed in the end, which is based on statistical data and engineering experience. Overall, not only this research will contribute an automatic and cost-effective method to the detection and evaluation of damage around the manhole cover, but also inspire the detection and evaluation of other special components of civil engineering structures.

Real-time detection of road manhole covers with a deep learning model

Road manhole covers are crucial components of urban infrastructure; however, inadequate maintenance or poor marking can pose safety risks to vehicular traffic. This paper presents a method for detecting road manhole covers using a stereo depth camera and the MGB-YOLO model. We curated a robust image dataset and performed image enhancement and annotation. The MGB-YOLO model was developed by optimizing the YOLOv5s network with MobileNet-V3, Global Attention Mechanism (GAM), and BottleneckCSP, striking a balance between detection accuracy and model efficiency. Our method achieved an impressive accuracy of 96.6%, surpassing the performance of Faster RCNN, SSD, YOLOv5s, YOLOv7 and YOLOv8s models with an increased mean average precision (mAP) of 15.6%, 6.9%, 0.7%, 0.5% and 0.5%, respectively. Additionally, we have reduced the model's size and the number of parameters, making it highly suitable for deployment on in-vehicle embedded devices. These results underscore the effectiveness of our approach in detecting road manhole covers, offering valuable insights for vehicle-based manhole cover detection and contributing to the reduction of accidents and enhanced driving comfort.

IoT-based Manhole Management System for Open Manhole Covers

The IoT based manhole management system for open covers is a system aimed to improve the management of manhole covers in urban infrastructure. The system uses IoT technology to monitor the status of manhole covers in real-time and sends alerts to the authorities if a manhole cover is open or missing or stolen. The system also includes an Android Application to notify the people if they are near an open manhole cover and to ensure their safety. The system was developed using NodeMCU as an instance of Arduino, and for the web application the programming languages used were HTML, CSS, JavaScript, PHP and API. The system provides an efficient and effective solution for the management of manhole covers, reducing the overall cost of maintenance and ensuring public safety.

Strength analysis of flat manhole cover in case of change of location and inclination angle of two mutually intersecting scratches

Mechanical damages of the main structural elements of the process equipment of hazardous production facilities are quite often detected during the revision process after repairs when assessing the quality of the work performed. One of the most common mechanical damages on the surface of parts and assemblies of technical devices includes scratches and risks. Significant scratches and risks that have a relatively large area and depth and limit the commissioning of the facility are subject to sampling and removal during re-repair. Superficial and non-extended scratches and risks do not belong to unacceptable defects, the technical device should not be taken into account, and further operation occurs with their presence. During further operation of the facility, such defects may not manifest themselves in any way, but may be high-voltage concentrators, which in the vast majority of cases are not critical. However, the presence of two or more scratches on the surface of the structural elements, their different location relative to each other, and therefore different mutual influence, can have a significant effect on the redistribution of zones and values of increased stresses on the surfaces where they are located. In the current regulatory and technical documents for diagnostics, these points are not considered, and accordingly are not taken into account. Therefore, the actual work is to perform a strength analysis of a flat manhole cover with two scratches, which have a different location on the surface, and a different angle of intersection relative to each other.

Monitoring and control of smart urban drainage systems using NB-IoT cellular sensor networks.

Narrow-Band-Internet of Things (NB-IoT) is a promising wireless communication technology for harsh signal propagation conditions, including underground connectivity. The purpose of this study is to evaluate the real-life signal quality of an NB-IoT cellular sensor network for the monitoring and control of urban drainage systems. The implementation of the control systems toward smart infrastructures increase the need for low-cost and low-energy-consumption underground sensing devices. A measurement campaign was conducted in Rakvere, Estonia, to analyze the NB-IoT network's coverage and underground signal quality. The results were used to analyze the flood risk reduction potential in the pilot catchment, considering the quality of NB-IoT cellular network coverage. The signal measurements showed that in the case of plastic manholes, the best cellular transmitter placement is 0.5 m below the manhole cover, and at the ground level NB-RSRP reference signal strength of -104 dBm or better must be ensured to have a link budget with optimal energy consumption for communication. The results of flood risk reduction showed that excluding control locations with insufficient signal strength increased the number of major flooding nodes by 3.9% and mean flooding duration by 4.2% which is a relatively small change and characterizes the flexibility of the developed control system.

ANALYSIS OF OPTIMIZED MANHOLE COVERS MADE OF GLASS-REINFORCED POLYESTER (SMC) THROUGH BENDING TESTS AND FINITE ELEMENT SIMULATIONS

This scientific article is devoted to the study and modeling of composite manhole covers made of polyester fiberglass, which are an alternative to traditional metal covers. These substitutes were introduced due to the problems associated with corrosion and theft of steel and cast iron manhole covers, as well as their insufficient resistance to chemicals. Manhole covers made from fiber reinforced plastic have a number of advantages over traditional metal covers. They provide a high strength-to-weight ratio, making them easy and comfortable to install. This material also has a high resistance to corrosion, which greatly extends its service life. Despite the advantages of composite manhole covers, studies related to their mechanical testing and simulations remain insufficient. This article presents the results of a study in which a manhole cover was made from two layers of polyester sheet press material using an additional stiffener. Then, bending tests were carried out, and the results were analyzed using finite element method (FEM) simulation. The purpose of this study was to confirm the validity of the model built for composite manhole covers, as well as to compare the results with experimental data on deformations. The results obtained are of great practical importance for the further development and optimization of the design of composite manhole covers, as well as for improving their reliability and durability.

Prototyping an integrated IoT-based real-time sewer monitoring system using low-power sensors

Improper monitoring of sewage networks may raise various issues such as overflows, pipe blockages, theft of manhole covers, leading to flooding and pollution, infrastructure damage, vehicles accidents, injury, and even death from falling into open manholes. The key objective of this research was to examine different elements and create a prototype architecture for a real-time sewer monitoring system. Implementation of the architecture involved constructing a data gathering station and experimenting with various wireless sensing devices to assess the precision of the sensors. In addition, the study sought to design a geographic information system that integrates algorithms capable of identifying sewer overflow, blocked pipes, and the presence of manhole covers. The performance of Sharp GP2Y0A41SK0F infrared, TF-Luna Benewake LiDar, TOF400 VL53L1X laser, JSN-SR04T ultrasonic distance sensors was tested in terms of their ability to monitor water level and manhole cover. Tests revealed the most favorable results in TOF400 VL53L1X at distances between 0.2 and 1.0 m (presumed distance to the manhole cover) with a standard deviation of 0.13–0.24, and in TF-Luna Benewake at distances between 1.0 and 5.0 m (presumed distance to the chamber bottom) with a standard deviation of 0.44–1.15. The deviation analysis has yielded equations that can be utilized to provide rough estimates of the accuracy levels of the aforementioned sensors, based on the measured distance. Additionally, the FC-28 analog and YL-63 infrared sensors were evaluated for detecting pipe blockages, with the YL-63 being more suitable. The outcomes of this study furnish valuable insights that can aid in achieving sustainable resolutions for issues related to sewer monitoring

A Smart Manhole Monitoring and Detection System

Abstract: An intelligent manhole cover management system is a crucial foundation in a smart city to effectively prevent frequent accidents related to manhole covers. Proper management of the drainage system is essential for ensuring the safety of residents. Regular inspections alone can lead to flooding and blockages in the drainage system. The displacement, relocation, and deterioration of manhole covers pose serious risks to personal safety, which contradicts the goals of smart city development. The system incorporates multiple sensors to monitor the status of manholes, including detecting lid openings, cover theft, and temperature variations. Additionally, this paper aims to analyze real-time levels of toxic gases. If the gas levels exceed the predetermined threshold, alerts will be sent to the authorized individuals through their connected mobile devices, even if they are remotely located.

Manhole Detection and Monitoring System using IoT

Nowadays, manholes and its maintenance are the main problem in the metropolitan smart cities. A drainage monitoring system plays a significant role in keeping the towns and cities healthy and clean. The major challenge is to further investigate the condition of manholes on the road. In observation, most of the manhole’s lids were not in the settled emplacement and are in damaged condition. Because of these damaged manholes, there are chances of occurrence of accidents on the road. These damaged manholes will be hazardous to personal safety. If the sewage maintenance is not proper, ground water gets contaminated causing infectious diseases. Blockages in drains during monsoon season causes problems in the routine of the public. Hence, there should be a facility in which it alerts the officials about blockages in sewers, their exact location and about the gas explosion, increase in the water level and temperature level. The goal of this project is to create an effective accident-avoidance system by avoiding open manholes in large cities. This system includes an array of sensors for complete monitoring of the manhole cover such that many accidents can be prevented. This system reduces the work of manpower and increases the safety and speed of work. The working and implementation of this project will be very useful to take necessary actions and maintain the regularity of the municipal society

Research and Implementation of Low-Power Anomaly Recognition Method for Intelligent Manhole Covers

This paper addresses the difficulty of balancing a real-time response and low power consumption in intelligent manhole cover application scenarios. It proposes a method to distinguish normal and abnormal events by segmenting the boundary at which the acceleration of the intelligent manhole cover deviates from a set threshold and lasts for a certain period, based on the difference in the intelligent manhole cover’s vibration patterns when a normal event and an abnormal event occur. This paper uses the autonomous data fusion of digital output motion sensor data to implement a pattern recognition algorithm for the above-mentioned pattern, which reduces the MCU computing and working time and the overall power consumption of the system while meeting real-time response requirements. The test results demonstrate that the method has a high rate of anomaly recognition accuracy. The method ensures the system’s real-time response capability, and the actual low power consumption test demonstrates that the device can operate continuously for 9.5 years. The low power consumption index exceeds the requirements of the existing national standard, thereby resolving the issue that it is challenging to balance intelligent manhole cover abnormality recognition and low power consumption.