Generally, the amount of wastewater in sewerage pipes is measured using sensor-based devices such as submerged area velocity flow meters or non-contact flow meters. However, these flow meters do not provide accurate measurements because of impurities, corrosion, and measurement instability due to high turbidity. However, cameras have advantages such as their low cost, easy service, and convenient operation compared to the sensors. Therefore, in this study, we examined the following three methods for measuring the flow rate by capturing images inside of a sewer pipe using a camera and analyzing the images to calculate the water level: direct visual inspection and recording, image processing, and deep learning. The MATLAB image processing toolbox was used for analysis. The image processing found the boundary line by adjusting the contrast of the image or removing noise; a network to find the boundary line between wastewater and sewer pipe was created after training the image segmentation results and placing them into three categories using deep learning. From the recognized water levels, geometrical features were used to identify the boundary lines, and flow velocities and flow rates were calculated from Manning’s equation. Using direct inspection and image-processing techniques, boundary lines in images were detected at rates of 12% and 53%, respectively. Although the deep-learning model required training, it demonstrated 100% water-level detection, thereby proving to be the most advantageous method. Moreover, there is enough potential to increase the accuracy of deep learning, and it can be a possible replacement for existing flow measurement sensors.

Most urban areas in the world have water infrastructure systems, including the buried sewer and water pipelines, which are assessed as in need of extensive rehabilitation. Deterioration by many other factors affects structural integrity. Trenchless technologies such as Cured-in-Place Pipe (CIPP) are now applied in numerous projects while minimizing disturbance in an urban environment. The main purpose of this study is to develop a high strength CIPP material using various composite materials (e.g., glass fiber, carbon fiber, polyester felt, unsaturated polyester resin, and others). Composite samples were made of the materials and tested using three-point bend apparatus to find mechanical properties, which include the flexural modulus, strength, and deflection. A composite combination with glass fibers with thin felt layers shows the best results in mechanical properties. Flexural modulus is a key factor for CIPP liner thickness design. Glass fiber composite yields between four and nine times higher values than the minimum value specified in the American Society for Testing and Materials (ASTM) F1216. This study provides a fundamental baseline for high strength CIPP liners that are capable of using conventional curing technologies.

Cured-in-place pipe (CIPP), as a kind of trenchless sewer rehabilitation technology, is a method to repair sewer pipe using unsaturated polyester resin. This study develops a CIPP liner using hot water or steam curing as well as glass fiber, in contrast to traditional methods, which use nonwoven fabric. Composite material samples were fabricated by combining liner materials using various methods, and the structural characteristics of the liners were compared and analyzed through short- and long-term flexural strength tests. A long-term test was conducted for 10,000 h, and the results revealed 13.3 times higher flexural strength and 8 times higher flexural modulus than the American Society for Testing Materials minimum criteria for CIPP short-term properties. The maximum creep retention factor was 0.64, thereby reducing the design thickness of the CIPP by up to 54%. The structural characteristics also improved when glass fibers were mixed with traditional CIPP liner, making it possible to reduce the thickness by 30%. Glass fibers result in high structural strength when combined with unsaturated polyester resin. Structural strength increased, even when glass fibers were mixed with traditional CIPP liner. The main contribution of this research is the development of a high strength CIPP liner and improvement of the structural properties of CIPP lining without using the specially formulated resin or lining materials.

Asset owners and engineers throughout the U.S. and the world are in search of cost-effective and environmentally friendly solutionsthat serve infrastructure issues. One of the most critical areas of concern is wastewater piping and structures. It is well known that corrugated metal pipes used in storm-water structures are corroding and microbial induced corrosion of sanitary sewers of various materials results in structural concern.Geopolymers have long been known to provide enhanced physical performance to traditional cementitious binders with the addedadvantages of significantly reduced greenhouse emissions and superior chemical resistance. Geopolymers are ceramic polymertechnology that creates a chemical material similar to natural stone that is superior to traditional Portland cement and shotcretematerials. However, they have not generally been contractor-friendly. This paper reviews a geopolymer mortar system that has been used in the U.S. since 2011 and is becoming a preferred solution fortrenchless rehabilitation. The system is spray cast either by rotary nozzle or via traditional shotcrete delivery systems inside ofexisting structures to create whole new structures which do not depend on the existing structure, just using it as formwork. This paperdiscusses competitive advantages over other trenchless repair solutions such as spiral wound, slip-lining and CIPP through specificcase studies including a corrugated metal storm drain rehabilitation in Hidalgo, Texas along with the repair of a stone sewer system inCincinnati, Ohio.

Historically the U.S. transportation system has been continuously improved to adopt new policies and technologies. The transportation vehicle energy transition from fossil fuels to electricity is promoted by policymakers and major automakers with an expectation of enhancing sustainability aspects such as fossil fuel consumption reduction, carbon emissions reduction, and lower operations and maintenance costs. However, the electrification of the existing transportation infrastructure system requires substantial upgrades to overcome two major concerns from ordinary drivers and the public. One is the driver’s range anxiety based on the current capability of the electric vehicle (EV) technologies. The other is the availability of EV charging stations near the planned route. To address these two issues, we introduce an agent-based simulation model to project the consequences of electrification in the Indiana state highway system. Specifically, the model is developed to monitor the status of long-distance EV trips between different regions. The multi-agent engine method guarantees the model can adapt to diverse scenarios and complex environments. The simulation experiment verifies that the proposed model can provide the expected outcomes, including the numerical data of electric energy demand and the geospatial information (as location coordinates) of failed trips. By performing a GIS-based analysis of the results, the derived geospatial data can help state transportation agencies determine where to deploy the charging facilities to satisfy the overall charging demand. The proposed simulation framework offers a novel and strategic way to resolve the challenges for EV charging-related research and projects.

The aging water infrastructure is one ofthe main causes of the increasing water pipe breaks nationwide, resulting in significant social and economic costs. To sustain water infrastructure assets in good condition, it requires a proactive decision making process for capital improvement project planning, maintenance, rehabilitation and replacement. Because physical inspections involve extensive labor and financial resources, it is not always a feasible option to cover entire water pipe inspection and condition assessment. This paper presents two statistical water pipe failure prediction models developed forwater system managers to assisttheir long-term decision making process using Weibull distribution and binary logistic regression. The models presented are developed based on water pipe failure data from approximately 100 years old cast iron pipesfroma municipality in the Midwest, United States.