Wastewater infrastructure systems deteriorate over time due to a combination of physical and chemical factors. Failure of these critical structures can cause major social, environmental, and economic impacts. To avoid such problems, several researchers attempted to develop infrastructure condition assessment methodologies to maintain sewer pipe networks at desired condition. Sewer condition prediction models are developed to provide a framework to forecast future conditions of pipes and to schedule inspection frequencies. Yet, utility managers and other authorities are often challenged with identifying the optimal timeline for inspection of sewer pipelines. Frequent inspection of sewer networks is not cost-effective due to limited time, expensive assessment technologies, and large inventories of pipes. Therefore, the objective of this state-of-the-art review is to study progress over the years in developing condition prediction models and investigating the potential factors affecting the condition of sewer pipes. Published papers for prediction models from 2001 through 2019 were identified and analyzed. Also, this study conducts a comparative analysis of the most common condition prediction models such as artificial intelligence (AI) and statistical models. The literature review suggests that, out of 20 independent variables studied, pipe age, diameter, and length are the most significant contributors to the deterioration of sewer systems. In addition, it can be concluded that AI models reduce uncertainty in current condition prediction models. Furthermore, the most appropriate prediction models for development are those that are capable of accurately finding nonlinear and complex relationships among variables. This study recommends the use of more environmental and operational factors—e.g., soil type, bedding material, flow rate, and soil corrosivity—and advanced data mining techniques to develop comprehensive and accurate condition prediction models. The findings of this study are intended to guide practitioners in developing customized condition assessment models for their agencies that can save millions of dollars through optimized inspection timelines and fewer incidents.

Wastewater infrastructure systems deteriorate over time due to a combination of aging, physical, and chemical factors, among others. Failure of these critical structures cause social, environmental, and economic impacts. To avoid such problems, infrastructure condition assessment methodologies are developing to maintain sewer pipe network at desired condition. However, currently utility managers and other authorities have challenges when addressing appropriate intervals for inspection of sewer pipelines. Frequent inspection of sewer network is not cost-effective due to limited time and high cost of assessment technologies and large inventory of pipes. Therefore, it would be more beneficial to first predict critical sewers most likely to fail and then perform inspection to maximize rehabilitation or renewal projects. Sewer condition prediction models are developed to provide a framework to forecast future condition of pipes and to schedule inspection frequencies. The objective of this study is to present a state-of-the-art review on progress acquired over years in development of statistical condition prediction models for sewer pipes. Published papers for prediction models over a period from 2001 through 2019 are identified. The literature review suggests that deterioration models are capable to predict future condition of sewer pipes and they can be used in industry to improve the inspection timeline and maintenance planning. A comparison between logistic regression models, Markov Chain models, and linear regression models are provided in this paper. Artificial intelligence techniques can further improve higher accuracy and reduce uncertainty in current condition prediction models.

With growing concerns about global warming and greenhouse gases (GHG), there is an urgent need to quantify and reduce the environmental impact (EI) of pipeline installation (PI). The most common and popular criterion used to describe sustainability efforts from the environmental perspective is the concept of carbon footprint (CF). A CF is the total set of GHG emissions caused by an organization, event, or product. It is expressed in terms of the amount of carbon dioxide (${\mathrm{CO}}_2$ ), or its equivalent of other emitted GHGs. The open-cut pipeline installation (OCPI) method involves digging a trench along the length of the proposed pipeline, placing the pipe in the trench on suitable bedding materials and then embedding and backfilling. The trenchless method (TM) involves methods, materials, and equipment capable of the installation of new, replacing old, or renewing existing pipelines with minimal disruption to surface traffic, business, and subsurface. Choosing an appropriate low-carbon emission method to install or renew pipelines is an important task and may consider environmental concerns due to several energy-consuming activities, including material manufacturing, transportation, and installation. Because essentially, every PI and renewal project impacts the environment, it is of utmost importance for the pipeline project owner and the design engineer to evaluate this impact and take necessary actions to minimize any negative consequence. The objective of this paper is to present a literature review on progress acquired over the years in understanding GHG emissions from OCPI and TMs and to discuss a framework for CF estimation. Published papers were identified that reported GHG emissions and CF of the open-cut (OC) method and TM over a period from 1989 through 2019. This literature review suggests that for most of these PI and renewal projects, the material production phase consumes a large amount of energy and is a major contributor of GHG emissions. Higher GHG emissions in the OCPI method is a result of longer project durations and more equipment requirements compared with TMs where a smaller footprint or excavation area is used. Early phases of project planning should include appropriate ecological decisions consistent with the life cycle cost (LCC) and CF considerations. Pipe material and outside diameter should be considered during the installation phase by OC and TM to allow a detailed evaluation and comparison of their sustainability impacts. Incorporation of additional factors, such as cost and duration of the project, into the environmental analysis is recommended for the development of a comprehensive decision-making model to select the most environment-friendly PI method.

The development of underground infrastructure, environmental concerns, and economic trends is influencing society, resulting in the advancement of technology for more efficient, environment-friendly, and cost-effective pipeline installation and renewal. A comparison of environmental and social (E and S) costs of a pipeline renewal and replacement (R and R) is an essential element when considering sustainable development of underground infrastructure. Project owners, decision-makers, design consultants, and contractors commonly take into consideration the construction costs only and overlook the E and S cost aspects while making a choice between trenchless renewal and open-cut pipeline replacement (OCPR). Trenchless cured-in-place pipes (CIPP) involve a liquid thermoset resin saturated material that is inserted into the existing pipeline by hydrostatic or air inversion or by mechanically pulling-in and inflating. The liner material is cured-in-place using hot water, steam, or light cured using ultraviolet (UV) light, resulting in the CIPP product. The objective of this paper is to provide a comparative analysis of E and S costs of a trenchless CIPP renewal method (CIPPRM) with OCPR for small diameter sanitary sewers (SDSS) and to identify influencing factors impacting costs. An actual case study based on a river basin in Pasadena, CA, was used for this research to evaluate the E and S costs implication of a small-diameter CIPPRM and OCPR. The results show that the total E and S costs of a trenchless CIPP method is 90% less as compared to OCPR for SDSS, such as $203–305\mathrm{mm}$ (8–12 in.) diameters. It was determined that the environmental impacts of the CIPP will be more than its social impacts. For open-cut, the social impacts are found to be more than environmental impacts. CIPP renewal caused less ozone depletion, global warming, smog, acidification, eutrophication, noncarcinogenics, respiratory effects, ecotoxicity effects, and fossil fuel depletion. The liner, felt, and resin influenced the environmental cost the most for CIPP compared to open-cut in which power consumption of construction equipment and pipe material drove the environmental cost. The cost of fuel for detour roads, detour delays, and pavement restoration were negligible for CIPPRM as compared with OCPR, which contributed a major social cost factor (approximately 75%). A similar approach can be applied for larger pipe diameters and other locations to develop a decision tool.

Clays generally have a low strength and capacity, and additives are usually used to stabilize them. In recent years, using fly ash to stabilize soil has decreased environmental pollution while also having an economic benefit. The objective of this study is to perform a comparative investigation on the effect of class C and class F fly ashes on geotechnical properties of high-plasticity clay using the Atterberg’s limit, compaction, California Bearing Ratio (CBR), and unconfined compressive strength tests. The results showed that with an increase in the amount of fly ash, there was a decrease in the maximum dry density and an increase in the optimum moisture content. Moreover, an addition of fly ashes of up to 25% caused a reduction of the liquid limit and plasticity index, and an increase in the maximum unconfined compressive strength and CBR. Lengthening the curing time had a positive impact on the unconfined compressive strength of the soil. The soil samples with class C fly ash were seen to possess more efficient geotechnical properties as compared to class F fly ash.

The production of concrete and the manufacturing process of cement result in a significant carbon footprint, contributing to a large portion of global emissions in structures such as buildings, bridges, roads, and tunnels. Although concrete is an ideal building material that is durable and long-lasting, it can be susceptible to micro-cracks. These micro-cracks in concrete can allow water and chlorine ions to penetrate the structure, leading to the degradation of the concrete and corrosion of the reinforcement, posing an unacceptable level of structural risk. Self-healing concrete is not a new material in the construction industry but can be characterized by the capability of concrete to repair its cracks autogenously or autonomously. Recent advancements in concrete research and technology have given us a better understanding of concrete’s healing properties. Self-healing concrete combines durability with sustainability while offsetting the high carbon output of concrete manufacturing and production and associated life-cycle costs. Technologies such as microbially induced calcite (calcium carbonate) precipitation, shape-memory polymers, encapsulation methods, hydration, and swelling agents can potentially reduce carbon emissions while enhancing resilience and longevity. This paper examines these technologies and their applications in the construction industry by comprehensively reviewing the literature and available case studies. This study concluded that there are promising advancements and innovations in concrete, particularly when improving upon its autogenous healing properties. The recommendations for future research include exploring more ways to bring the concrete industry and cement manufacturing toward net-zero carbon emissions.