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.

This paper analyzes the evolution over the past three decades of seismic damage estimation for buried pipelines and identifies some challenges for future research studies on the subject. The first section of this paper presents a chronological description of the evolution since the mid-1970s of pipeline fragility relations—the most common tool for pipeline damage estimation—and follows with a careful analysis of the use of several ground motion parameters as pipeline damage indicators. In the second section of the paper, four gaps on the subject are identified and proposed as challenges for future research studies. The main conclusion of this work is that enhanced fragility relations must be developed for improving pipeline damage estimation, which must consider relevant parameters that could influence the seismic response of pipelines.

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.

Underground freight transportation (UFT) is a class of automated transportation systems in which vehicles carry freight through tunnels or pipelines under roadways between intermodal terminals. The UFT systems increase the current freight movement capacity and alleviate the problems associated with the fast growing need for freight transportation. Despite the inherent UFT benefits to alleviate traffic congestion and enhance safety and security of freight transportation while improving sustainability, a detailed lifecycle benefit-cost analysis is yet required to explicitly show planners and decision makers the economic feasibility of such systems. The primary objective of this paper is to conduct a comprehensive lifecycle benefit-cost analysis of linear induction motor-based UFT systems for five different scenarios. This methodology includes the following steps: (1) developing UFT scenarios with different sizes (small, medium, and large) and routes (short and long) in collaboration with a stakeholder committee; (2) determining lifecycle cash flows; (3) calculating net present values, benefit-cost ratios, and internal rates of returns; (4) conducting sensitivity analysis; and (5) performing breakeven analysis. The results of this research show that the highest benefits of the UFT systems are revenue from shipment and reduction in air pollution. The internal rates of return, benefit-cost ratios, and net present values of the UFT scenarios indicate that the benefits offset the costs of the system. Results of sensitivity analysis show that the economic feasibility of the large and medium-size UFTs is more sensitive to tunnel construction cost and revenue from shipments. They also indicate that the economic feasibility of the UFT system with a short route and small size is more sensitive to higher discount rates and the policies concerning the shipment pricing. Results of breakeven analysis highlight a shorter payback period for the large and medium-size UFTs compared with small-size scenarios. The results show that the price of shipment for all the UFT scenarios is highly competitive compared with the current price of shipment by trucks.

Sanitary sewer pipes infrastructure system being in good condition is essential for providing safe conveyance of the wastewater from homes, businesses, and industries to the wastewater treatment plants. For sanitary sewer pipes to deliver the wastewater to the treatment plants, they must be in good condition. Most of the water utilities have aged sanitary sewer pipes. Water utilities inspect sewer pipes to decide which segments of the sanitary sewer pipes need rehabilitation or replacement. The process of inspecting the sewer pipes is described as condition assessment. This condition assessment process is costly and necessitates developing a model that predicts the condition rating of sanitary sewer pipes. The objective of this study is to develop Multinomial Logistic Regression (MLR) and Artificial Neural Network (ANN) models to predict sanitary sewer pipes condition rating using inspection and condition assessment data. MLR and ANN models are developed from the City of Dallas’s data. The MLR model is built using 80% of randomly selected data and validated using the remaining 20% of data. The ANN model is trained, validated, and tested. The significant physical factors influencing sanitary pipes condition rating include diameter, age, pipe material, and length. Soil type is the environmental factor that influences sanitary sewer pipes condition rating. The accuracy of the performance of the MLR and ANN is found to be 75% and 85%, respectively. This study contributes to the body of knowledge by developing models to predict sanitary sewer pipes condition rating that enables policymakers and sanitary sewer utilities managers to prioritize the sanitary sewer pipes to be rehabilitated and/or replaced.