Infrastructure Failure Research Articles

Impact of the 2021 north american winter storms on children with epilepsy.

In February 2021 a series of winter storms caused power outages for nearly 10 million people in the United States, Northern Mexico and Canada. In Texas,the storms caused the worst energy infrastructure failure in state history, leading to shortages of water, food and heat for nearly a week. Impacts on health and well-being from natural disasters are greater in vulnerable populations such as individuals with chronic illnesses, for example due to supply chain disruptions. We aimed to determine the impact of the winter storm on our patient population of children with epilepsy (CWE). We conducted a survey of families with CWE that are being followed at Dell Children's Medical Center in Austin, Texas. Of the 101 families who completed the survey, 62% were negatively affected by the storm. Twenty-five percent had to refill antiseizure medications during the week of disruptions, and of those needing refills, 68% had difficulties obtaining the medications, leading to nine patients-or 36% of those needing a refill-running out of medications and two emergency room visits because of seizures and lack of medications. Our results demonstrate that close to 10% of all patients included in the survey completely ran out of antiseizure medications, and many more were affected by lack of water, heat, power and food. This infrastructure failure emphasizes the need for adequate disaster preparation for vulnerable populations such as children with epilepsy for the future.

Multiscale Shared Learning for Fault Diagnosis of Rotating Machinery in Transportation Infrastructures

Rotating machinery is ubiquitous, and its failures constitute a major cause of the failures of transportation infrastructures. Most fault-diagnosis methods for rotating machinery are based on vibration-signal analysis because vibrations directly reflect the transient regime of machinery elements. This article proposes a novel multiscale shared-learning network (MSSLN) architecture to extract and classify the fault features inherent to multiscale factors of vibration signals. The architecture fuses layer-wise activations with multiscale flows, to enable the network to fully learn the shared representation with consistency across multiscale factors. This characteristic helps MSSLN provide more faithful diagnoses than existing single- and multiscale methods. Experiments on bearing and gearbox datasets are used to evaluate the fault-diagnosis performance of transportation infrastructures. Extensive experimental results and comprehensive analyses demonstrate the superiority of the proposed MSSLN in fault diagnosis for bearings and gearboxes, the two foundational elements in transportation infrastructures.

IMMUNER: Integrated Multimodal Mobility Under Network Disruptions

Targeting at improving door-to-door accessibility, efficiency and reducing environmental impact, recent decades have witnessed vigorous development of multimodal transport. Coupled through passenger transfer, the failure of one mode, however, is likely to cause cascading disruptions to the complete system. Therefore, to maintain safe and efficient operations, the integrated recovery of the multimodal transportation system becomes an important, timely topic, given increasingly complex schedule interactions. This study proposes a mathematical model to handle the integrated recovery problem of multimodal transportation network by minimizing the passenger travel cost and mode recovery cost under failures of multiple critical infrastructures. For a case study on air/HSR interaction in China, our model incorporates decisions in terms of aircraft recovery, railway timetable rescheduling, and passenger routing with realistic constraints (e.g. max travel time, max transfer time). A column-and-row generation based heuristic solution algorithm is developed in order to solve the complex model with given computational budgets. Results reveal that real-world test disruption scenarios can be solved within 20 minutes with a small relaxation gap. A significant amount of passenger traveling time can be reduced in contrast to a sequential recovery approach with independent reaction decisions for each transport mode. This work contributes towards the development of support tools for integrated disruption management of multimodal transportation.

Water Resources and Water Services Infrastructure and Its Vulnerability to Extreme Events—The Case of Jordan

This study discussed the water sector as a critical infrastructural element in Jordan where the sector is exposed to the extreme events. The exposure of the country to extreme events has initiated this study. Such events are Pollution accidents, flooding, draughts, overexploitation, failure in electricity supply, climate changes, earthquakes, landslides, failure of dams, failure of wastewater treatment plants, failure of desalination plants, sabotage, fire, water theft, migration and demographic changes (immigration and urban migration), relations to neighboring countries, epidemics, and others. These extreme events are discussed in this article and the results show that failures in the water infrastructure and water supply, in Jordan, with its water sector situation have rigorous percussions on the country’s health, food supply, economy, societal stability, the built environment, and on other water-related issues. The study concludes that developing national programs to protect the water infrastructure in the water-fragile country has become very crucial to reach a robust and resilient water sector which not only means providing the inhabitants with quantitatively sufficient and qualitatively healthy water but also aims to incorporate guaranteeing social, economic and political stability.

Self-Reported Consumption of Bottled Water v. Tap Water in Appalachian and Non-Appalachian Kentucky.

Quantitative studies on drinking water perceptions in Appalachia are limited. High-profile water infrastructure failures in the U.S. and Eastern Kentucky, coupled with human-made and natural disasters in the Appalachian Region, have likely impacted opinions regarding tap water. To use existing unexplored data to describe baseline tap water v. bottled water consumption in Kentucky. Telephone-based cross-sectional data were obtained from the 2013 Kentucky Health Issues Poll (KHIP) directed by the Foundation for a Healthy Kentucky. Among many items in KHIP, self-reported consumption of bottled water over tap water, reasons for bottled water use, and demographic data were obtained. Among Appalachian (n=356) and non-Appalachian (n=1,125) Kentucky respondents, a significantly higher frequency of Appalachian Kentuckians reported drinking bottled water more often than tap water relative to non-Appalachian Kentuckians (57% v. 34%; X2 p < 0.001). Appalachian residency significantly predicted bottled water consumption in simple and multivariable logistic regression adjusted for significant covariates (i.e., age, sex, and race). Among persons consuming bottled water more than tap water, Appalachian Kentuckians reported significantly more concerns regarding tap water taste or smell (p = 0.005) and safety (p = 0.008) than non-Appalachians. These results from 2013 data pre-date headline news items related to public water and likely underestimate current bottled water preferences. New data are needed, and these results warrant further investigation into tap water aesthetics in Appalachia, bottled water consumption impacts on personal finances, and approaches to build public trust for public drinking water among multiple populations including Appalachian Kentuckians.

How much water supply resilience is enough? – A discussion paper

Resilience for water supply systems is the ability to maintain supply when the system is impacted by a disruption from the norm. It is not just about source reliability. It includes all manner of infrastructure failures and natural disasters, and even terrorist and war events. There is a spectrum of failure scenarios ranging from relatively short local supply area outages through to city wide total losses of supply, and the associated consequences range from inconvenience to catastrophic. Whatever the nature of a supply disruption, there will be a resilience measure which could be taken to reduce its impact, but could it be justified particularly when there is a small probability of it ever being needed. It is proposed guidance about how much resilience is needed could be obtained from existing level of service standards, even though those standards might not be used locally or be directly related to water supply. The paper investigated two ends of the resilience spectrum, frequently and exceptionally infrequently resilience needed events. It was found that most Australian water supply authorities are providing a similar level of service, and consistent with a 10 to 15-year standard for frequently occurring resilience events. Storage tanks are the main source of resilience for many water supply areas for such events and have historically been sized using qualitative methods. This paper discusses the more effective use of quantitative methods. At the other end of the spectrum resilience is capable of maintaining supply in events with average recurrence intervals of 1,000 to &lt;10,000 years is specified by published emergency risk guidelines. A high-level review of Queensland urban centres found resilience to meet all types of failure scenarios, not just droughts, approaching a 10,000-year standard could be provided in conjunction with high level of water restrictions if independent water source and treatment capabilities from adjoining supply areas were interconnected.

Estimation of the capacity for self-purification of transformed rivers of Khanty-Mansi Autonomous Okrug-Yugra (Russia).

This article presents an estimation of the self-purification capacity of rivers flowing through an area of the Ob River basin at Khanty-Mansi Autonomous Okrug-Yugra, which has been significantly transformed by the oil and gas industry. The research was done on the rivers of the Ob River basin in oil fields in 2003-2006 and 2018-2019. The coefficient of mineralization of organic matter has been used as a criterion of the rivers' water self-purification levels. The mineralization coefficient was based on the ratio of numbers of bacteria consuming ammonium nitrogen and polymer substances as well as saprophytic heterotrophic bacteria. According to the results obtained, most rivers draining the oil field area (75% in 2003-2006 and 79% in 2018-2019) were characterized by a low potential for self-purification. The highest level of self-purification criteria was obtained for two rivers: the Ai-Pim River in the Surgut region and the Ovyn'yegan River in the Oktyabrsky region. The high level of self-purification detected with the coeficient of mineralization ensures the maintenance of a certain trophical level and environmental balance of the aquatic ecosystem. Complicated technological processes applied in the oil and gas industry, as well as outdated equipment and poor environmental management, often lead to pipeline accidents and other infrastructure failures. Hazardous pollutants flow into rivers from the watershed area and further into the Gulf of Ob of the Kara Sea. Continuous monitoring of water quality and water resource management based on the results obtained will constitute serious measures to prevent deterioration of aquatic ecosystems. It is necessary to include microbiological parameters in monitoring programs, as this helps to identify the processes of transformation of chemicals found in the river. The use of self-purification assessment methodology for rivers in oil field areas yields effective results in the decision-making process in the sphere of water resource management, significantly reducing ecological risks in the Ob River basin and, as a consequence, in the Arctic region. Integr Environ Assess Manag 2023;19:988-993. © 2022 SETAC.

Urban Resilience

This article discusses a methodological proposal for the elaboration of urban resilience plans, which is one of the most important issues involving cities nowadays. The etymological concept of “resilience” needs to be rescued because Urban Resilience is the exact application of the original concept applied to cities, regardless of their scale. Bausista-Puig (2022, p.1) had examined 1014 publications during the period 1998–2020 and concluded that urban resilience literature has grown since 2009 until today and this reveals the great international interest in this topic during the last decade.Cities, due to their complexity and locus of living for a large part of the global population and with a rising tendency, need to guarantee their conditions of habitability. Considering the improvement of extreme weather events and the consequences for cities, resilience plans have become urgent and fundamental elements in this whole process. As demonstrated in this article, it is not only climate change that impacts urban life, but there are other elements such as infrastructure failures, strikes by essential professional services and the issue of refugees, which can also destabilize the urban balance. On the other hand, resilience plans that aim to return cities to their initial operational conditions need to contain all the elements that guarantee their execution. Plans with only diagnoses and actions of what should be done, will not guarantee their execution. Another important issue discussed in this article is the structuring axes which means, what are the priority areas and the themes that should be present in an urban resilience plan. Are they just those related to climate change or are there others with a high potential to destabilize our cities around the globe?

Reliability Analysis and Life Prediction of Aging LNG Unloading Arms Based on Non-Destructive Test Data

Unloading arms (ULAs) among seaport infrastructures are susceptible to deterioration posed by the effects of harsh marine environmental conditions. During infrastructure’s service life, the deterioration of structural integrity may increase the risk of failure of infrastructure, and should be taken into account during structural reliability assessment. In this study, a simple non-destructive test (NDT) was employed to examine the structural deterioration of ULAs which were installed over 30 years ago. Then, these aging ULAs were modeled by the finite-element program, using non-destructive test data to update the thickness dimensions of structural members. Next, a reliability assessment was conducted based on the stress distribution of the main structural components under external loads, which are calculated by their relation to wind speed. Moreover, the time-dependent reliability index curve was also built by considering the deterioration function to predict the failure probability of the particular components during the remaining lifetime. The study revealed that the present condition of the ULA system was satisfactory for current loading conditions. A reliability index predicted with deteriorations factors may be a rational and appropriate approach for the assessment of aging structures needed for efficient infrastructure management.

Evaluating the Interdependencies of Infrastructure Critical Systems during Earthquake Event: A Case Study for Padang City

Our modern society is becoming increasingly reliant on transportation networks, as well as the interdependent infrastructures and technologies that interact with them. The increasing complexity and interconnectedness of infrastructure networks make them susceptible to impact not only directly from external shocks but also indirectly from the failure of dependent infrastructures. This research study was conducted in Padang city, one of the most disaster-prone areas in Indonesia. Based on the literature review, it is no doubt that research study on seismic risk assessment is insufficient and outdated. In fact, a study about the interdependency between Critical Infrastructures (CIs) is yet to be done in this region. In this study, there are two approaches used for data gathering which is by surveying existing CIs using Google Earth and by an online questionnaire survey via Google Form. Based on the qualitative survey, a functionality rating method is done to obtain the level of outage/loss functionality which is an indicator for the damage occurred to the structure and infrastructure. Following that, a seismic risk analysis was conducted to assess the interdependency between investigated CIs and facilities. Respondents’ judgement from the questionnaire were used to identify the base criticality of each critical infrastructure. Based on the qualitative survey, the level of loss in functionality for the substation and the telecommunication tower is rated as “High”, but the loss in functionality for the water supply system is rated as “Moderate”. Moreover, the findings used from the respondents’ judgements were used to establish the initial level of criticality for each vital infrastructure. According to the findings, hospitals, power substations, and communication towers all have a criticality level of “5-Vital”, while police stations and fire stations both have a “3-medium” criticality rating. Eventually, the results of this assessment of interdependence are displayed in a criticality map, which shows how the interdependency relationship affects the initial criticality of a certain upstream infrastructure. Understanding the potential consequences of infrastructure failure, especially in regard to dependent infrastructures, can help emergency response teams formulate more targeted strategies for managing risks. As a consequence of this, the resilience of the wider community is improved, which contributes toward the implementation of Sustainable Development Goal (SDG) 11: Sustainable cities and communities particularly in reducing disasters and people in vulnerable situation.

Methodological Guide to Forensic Hydrology

In Latin America and the Caribbean (LAC) region, geophysical, meteorological and hydrological disasters are increasing every year. With significantly limited resources, these countries are naturally forced to absorb lessons from these disasters. One of the fundamental activities during this learning task remains the need to standardize the forensic reporting process. Like all academic disciplines, engineering is exceptional in its application to the forensic field. This feature makes it a unique input to the investigation of hydrological and environmental catastrophes. Based on the fundamental concepts of forensic investigation, ten principles for properly conducting forensic hydrology studies are proposed. The ten principles proposed are: (i) Principle of use, (ii) production, (iii) principle of exchange, (iv) recognition, (v) correspondence, (vi) reconstruction, (vii) principle of probability, (viii) uncertainty, (ix) principle of certainty, and (x) conclusion principle. A hypothetical case of urban infrastructure failure is used to explain, in detail, each of the proposed principles. This paper proposes a methodology to be considered as a reference point for a forensic hydrological analysis to be used at the LAC region.

A clustering-classification approach in categorizing vulnerability of roads and bridges using public assistance big data

In the US, the state, local, territorial, and tribal governments seek funding from the federal government to repair, restore, reconstruct, or replace the disaster damaged infrastructures through Public Assistance program. This research has utilized historical public assistance funded projects’ data in an unsupervised machine learning algorithm: agglomerative hierarchical clustering and uncovered three distinct levels of vulnerability among the roads and bridges in different counties under different natural hazard conditions. The paper shows the spatial distribution of the different levels of vulnerability. The difference in the extent and severity of infrastructure failure among different vulnerability levels was found to be statistically significant. In the second part of the research, a random forest classification model is utilized to explore whether the number of disasters, the area and population of a county, and the existing condition of roads and bridges in a county can predict the vulnerability level of the county as derived from the clustering model to cross-validate them. It has been found that the classification model can predict the vulnerability levels with 85% accuracy. It was also found that the differences in the condition of roads and bridges among the risk profiles are statistically significant and higher risk profile counties have worse roads and bridges. The segmentation of roads and bridges into different vulnerability levels can help the decision makers in planning for asymmetric initiatives for roads and bridges in different counties. Moreover, it has been established that improving the existing conditions of infrastructures can help in reducing the cost of public assistance in a county.

Towards rainstorm event identification: A transfer learning framework using citizen-report texts and multi-source spatial data

Urban rainstorms are often accompanied by various hazardous events (e.g., water inundation and small-scale debris flow) and subsequent consequential events (e.g., infrastructure failure and trapped citizens). It is imperative to achieve rapid identification of such events for timely and effective rainstorm disaster management. However, due to small-sample event classes, it becomes challenging to achieve the desired identification results. In this paper, a cross-domain transfer learning scheme is proposed to identify rainstorm events by combining citizen-report texts with multi-source spatial data. In the proposed framework, we recommend joint distribution adaptation (JDA) as the key to improving the identification of target events with inadequate samples by transferring knowledge from similar event domains. Meanwhile, multi-source spatial features are extracted and embedded in the text with linear discriminant analysis (LDA) to overcome the feature incompleteness that is caused by short text length. The proposed approach is verified based on the real data in Wuhan, China. Experimental results show that the knowledge transfer between different event classes helps cities to improve the performance of rainstorm event identification, with the accuracy increasing from 81% to 92%. Under data imbalance scenarios, the proposed scheme outperforms the state-of-the-art methods, such as sampling-based methods, ERNIE, and TCA. At the same time, the combination of textual and spatial features results in significant improvement in event identification, as evident by an 8% increase in accuracy. This framework can be followed to build a rainstorm event warning and assistance system to rapidly adapt management responses.

Building resilience into smart mobility for urban cities: an emerging economy perspective

Population growth in urban cities has created transport problems such as traffic congestion, pollution, accidents, and infrastructure failures. These problems often lead to disruption of mobility, especially in emerging economies. The goal of any city is to have a resilient transport system that is free from frequent and severe disruptions. Resilience can be incorporated into smart mobility systems to create a robust, integrated and intelligent transport system. Therefore, this study sought to determine resilience factors required for a resilient smart mobility system in emerging economies. The study used interpretive structural modelling (ISM) to establish the causal interrelationships among the factors drawing from data from experts in smart cities, city transport and academia. It was found that the most critical factor to drive resilience is foresight – the ability to anticipate disruptions. Three other resilience factor categories identified were dynamism, certainty and optimisation, which all point to the importance of building dynamic capabilities. The key implication for practice highlights the role that human and artificial intelligence can play in anticipating natural, social and infrastructure disruptions. ISM offered a mechanism to determine the critical causal resilience factors to be prioritised amongst the competing demands on the limited resources of emerging economies. Apart from foresight, which was found to be a driving factor, the other resilience factors are equally strong linkage factors (influence each other), thus further revealing the complexities that decision-makers in urban cities of emerging economies constantly face. A research agenda for resilience in smart mobility in emerging economies was developed.

A Vine Copula‐Based Ensemble Projection of Precipitation Intensity–Duration–Frequency Curves at Sub‐Daily to Multi‐Day Time Scales

AbstractPrecipitation intensity–duration–frequency (IDF) curves play a crucial role in the design and planning of urban infrastructure to reduce the risk of urban flooding and rainfall‐triggered landslides. However, changing rainfall characteristics in a warming climate render conventional IDF curves inappropriate due to the statistical assumption of stationarity. In this study, we develop vine copula‐based projections of future IDF curves at sub‐daily to multi‐day time scales with a multi‐model ensemble of five regional climate simulations over China. Stochastic spatiotemporal downscaling of precipitation is achieved to generate extreme precipitation simulations at a high spatial (0.1°) and temporal (3 hourly) resolution. These downscaled simulations are combined by the vine copula to improve the reliability and accuracy of climate‐model‐based IDF curves relative to historical observations. Our findings reveal that climate‐model‐based stochastic downscaling of precipitation reproduces the IDF curves well based on historical observations in China. The vine copula multi‐model ensemble approach outperforms Bayesian model averaging by generating more accurate and reliable IDF curves. The urban areas of 196 Chinese cities are projected to experience an increase in extreme precipitation of up to 30% in intensity and nearly two times the frequency of historical events under a high emission scenario (RCP8.5). The current urban infrastructure of more than half of the 196 cities would thus be inadequate to prevent losses caused by rainfall‐triggered hazards if designed solely based on historical precipitation observations. Compared to the climate‐model‐based IDF curves, we find that statistical IDF curves based on a nonstationary time covariate (i.e., extrapolating historical trends) are likely to underestimate the risk of urban infrastructure failures under a warming climate. This work highlights that urban infrastructure design guidelines in China should be upgraded to adapt existing IDF curves to the changing climate.

Local News and the Electoral Incentive to Invest in Infrastructure

Despite broad public support, investment in US infrastructure has not kept pace with growth, population shifts, and rising exposure to climate change risks. One explanation lies in politicians’ electoral incentives: because, in the short term, voters see only the costs of investment and not its benefits, politicians have incentive to pander and spend less than what they or their fully informed constituents would prefer. Local newspapers could help reduce this constraint by increasing politicians’ confidence that voters will receive information that justifies higher spending. In a survey experiment, we found that informing US city and county elected officials about news coverage of infrastructure failures increased support for a costly investment for those in competitive electoral settings. When motivated by reelection, politicians need the benefits of investment to be visible in order to justify its costs. Our results demonstrate the political importance of the nonpolitical news covered in local newspapers.

Performance assessment of the operational management of urban drainage systems: a case study of Bejaia City, Algeria.

Over the recent years, managers of the Algerian National Sanitation Office (ONA) have been worrying about the degraded state of their Urban Drainage Systems (UDS). This infrastructure failure is due essentially to the lack of funding and absence of adequate structured methodology for diagnosis and maintenance. As a result, ONA's managers found it very interesting to get a tool that allows them to assess their management of UDS operation. The aim of this paper is therefore to provide an assessment tool to managers for a good network operation. The adopted approach is participative and takes into account the specific local context. Six performance indicators, grouped into two criteria, have been constructed to achieve this objective. These indicators have been chosen for their effectiveness; they are scaled to get their performance scores according to a scaling constructed here based on standards when available and to the ONA expert's recommendations. The developed tool is applied to the UDS of Bejaia City, in north-eastern Algeria. The assessment and the analysis of the performance evolution of these indicators for the period 2017-2021 are carried out. The results highlighted successes that can be maintained as well as weaknesses that need to be improved.

AUTOMATIC DETECTION AND DIMENSIONAL MEASUREMENT OF MINOR CONCRETE CRACKS WITH CONVOLUTIONAL NEURAL NETWORK

Abstract. The increasing number of aging infrastructures has drawn attention among the industry as the results caused by critical infrastructure failure could be destructive. It is essential to monitor the infrastructure assets and provide timely maintenance. However, one of the crucial problems is that the budget allocated to the maintenance stage is much less than that for the designing and construction stages. The cost of labor, equipment, and vehicles are significant. Therefore, it is impossible to perform a thorough inspection by human inspectors over each asset. A more efficient method will be needed to solve this problem. This paper aims to provide an automatic approach to detecting and measuring the dimensions of minor cracks that appear on concrete structures with a noisy background. This research also investigates the relationship between image pixel size, accuracy, detection rate of cracks, and shooting distance of images. The proposed method will be able to reduce the cost and increase accuracy. A case study was performed on a concrete sewer with cracks distributed on the surface in Sydney, New South Wales, Australia.

TOWARDS MORE RESILIENT SMART CITIES: MT-InSAR MONITORING OF URBAN INFRASTRUCTURE USING MACHINE LEARNING TECHNIQUES

Abstract. Global climate change makes the maintenance and resilience of cities one of the greatest challenges facing civilization. Constant monitoring is essential work to determine if the city and its infrastructure are well preserved. This monitoring has become increasingly simple and affordable, thanks to the advancement of technology, so the current trend is to systematically monitor the entire city (this concept is the paradigm of the smart city). There are several methods for infrastructure monitoring including GPS, mobile-mapping, video-surveillance, etc. However, this type of method has a series of disadvantages, such as the impossibility of obtaining large-scale data or the unavailability of information of the previous, current or after state that an event has occurred in the study-area. This can be solved with monitoring based on satellite images, since these have historical and constant coverage over time, with good resolution to identify urban structures and cover large study areas. The use of satellite radar images through MT-InSAR is booming because it is a powerful remote sensing technique capable of detecting displacements on the earth's surface. This technique can be combined with Machine-Learning to perform predictive analysis in urban environments and detect infrastructure failures. This predictive monitoring capable of anticipating risks is one of the objectives of the new urbanism. For this reason, this work analyzes the collapse of a pier, which occurred in Vigo, a city in NW Spain, through radar satellite images (Sentinel-1), MT-InSAR and Machine-Learning. The result is the possibility of anticipating structural failures thanks to the predictive monitoring.

Structural Performance Assessment of Geothermal Asphalt Pavements: A Comparative Experimental Study

This paper introduces shallow geothermal systems as a potential solution for improving the thermo-mechanical performance of asphalt under extreme climate events. With the recent changes experienced in the climate, earlier infrastructure failure can be expected, predominantly for temperature-sensitive flexible pavements. With that in mind, the efficiency of geothermal systems in terms of heating and cooling was comprehensively argued in many studies. However, very limited studies discussed the structural performance of geothermal pavements. This study conducted a comparative experimental study to assess the change in the compressive and flexural strengths of asphalt under extreme heating and cooling conditions and to evaluate the change in asphalt structural performance due to integrating different types of geothermal pipes into the asphalt structure. This comparative analysis employed thirty-three asphalt specimens with and without copper and polyvinyl chloride (PVC) geothermal pipes. The results of this study show that the geothermal pipes negatively affected the compressive strength of the asphalt at a normal average temperature. However, their effect was relatively minimal on the asphalt (AC) compressive strength under extreme heating and cooling conditions. In contrast, under three thermal conditions—normal, heating, and cooling temperatures—the flexure strength of the AC was significantly improved by 14.3%, 85%, and 70%, respectively, due to the copper pipe integration into the AC. The study concluded that copper pipes were superior to PVC ones in terms of enhancing the AC structural performance.