Performance Of Infrastructure Research Articles

Sustainable Operations: A Systematic Operational Performance Evaluation Framework for Public–Private Partnership Transportation Infrastructure Projects

With the application of public–private partnership (PPP) model in urban transportation infrastructure projects, various participants have put forward multi-dimensional demands to the operation and maintenance of infrastructures. This study aims to establish a systematic operational performance evaluation framework for PPP transportation infrastructure projects. Based on a literature review, the balanced scorecard was improved, and a conceptual model of multidimensional performance assessment was constructed. The structure of the qualitative performance assessment system was quantified and analyzed by combining structural equation modeling with questionnaires to obtain causal relationships among the indicators. Subsequently, a system dynamics model was constructed to assess the performance dynamically, and a validation analysis was conducted. It finds that maintaining a low level of operational quality over an extended period can significantly reduce stakeholder satisfaction, consequently exacerbating the decline in project performance. In contrast, an improvement in the level of informatization is found to positively contribute to enhancing operational quality and facilitating the long-term sustainability of project operations. It innovatively integrates four dimensions of financial, multi-stakeholder satisfaction, operation and maintenance quality, and sustainability performance to enrich the theoretical system of PPP transportation infrastructure performance assessment. At the same time, it analyzes the influence mechanism among the indicators and its long-term dynamic performance, which provides an effective decision-making tool for operational performance management.

Spatial analysis of future climate risk to stormwater infrastructure.

Climate change is expected to result in more intense precipitation events that will affect the performance and design requirements of stormwater infrastructure. Such changes will vary spatially, and climate models provide a range of estimates of the effects on events of different intensities and recurrence. Infrastructure performance should be evaluated against the expected range of events, not just rare extremes. We present a national-scale, spatially detailed screening assessment of the potential effects of climatic change on precipitation, stormwater runoff, and associated design requirements. This is accomplished through adjustment relative to multiple future climate scenarios of precipitation intensity-duration-frequency analyses presented in NOAA Atlas 14, which are commonly used in infrastructure design. Future precipitation results are estimated for each Atlas 14 station (these currently omit the Pacific Northwest). Results are interpolated using a geographically conditioned regression kriging approach to provide information about potential climate change impacts in a format more directly useful to local stormwater managers. The intensity of 24-h events with 2-year or greater recurrence is likely to increase in most areas of the United States leading to increased runoff and potential need for increased storage volumes. Changes in more frequent events (e.g., the 90th percentile event) commonly used in design of green infrastructure are relatively less.

Neural network inspired differential evolution based task scheduling for cloud infrastructure

In recent years, cloud computing has become an essential technology for businesses and individuals alike. Task scheduling is a critical aspect of cloud computing that affects the performance and efficiency of cloud infrastructure. During this pandemic where most of the healthcare services like COVID-19 sampling, vaccination process, patient management and other services are dependent on cloud infrastructure. These services come with huge clients and server load in a small instance of time. These task loads can only be managed at cloud infrastructure where an efficient resource management algorithm plays an important role. The optimal utilization of cloud infrastructure and optimization algorithms plays a vital role. The cloud resources rely on the allocation policy of the tasks on cloud resources. Simple static, dynamic, and meta-heuristic techniques provide a solution but not the optimal solution. In such a scenario machine learning and evolutionary algorithms are only the solution. In this work, a hybrid model based on meta-heuristic technique and neural network is proposed. The presented neural network inspired differential evolution hybrid technique provides an optimal assignment of the tasks on cloud infrastructure. The performance of the DE-ANN hybrid approach is performed using performance metrics, average start time(ms), average finish time(ms), average execution time(ms), total completion time(ms), simulation time(ms), and average resource utilization respectively. The proposed DE-ANN approach is validated against BB-BC, and Genetic approaches. It outperforms the existing meta-heuristic techniques i.e. Genetic approach, and Big-Bang Big-Crunch. The performance is evaluated using two configuration scenarios using 5 virtual machines and 10 virtual machines with varying tasks from 1000 to 4500. Experimental results show that the DE-ANN technique significantly improves task scheduling performance compared to other traditional techniques. The technique achieves an average improvement of 19.15% in total completion time(ms), 32.23% in average finish time(ms), 51.95% in average execution time(ms), and 33.24% in average resource utilization respectively. The DE-ANN technique is also effective in handling dynamic and uncertain environments, making it suitable for real-world cloud infrastructures.

Impact of bioretention cells in cities with a cold climate: modeling snow management based on a case study

Abstract The performance of blue-green infrastructures (BGIs) has been well documented in temperate and subtropical climates, but evidence supporting their application in cold climates, especially during snowmelt, is still scarce. To address this gap, the present study proposes a modeling method for simulating the performance of bioretention cells during snowmelt according to different spatial implementation scenarios. We used the Storm Water Management Model (SWMM) of a catchment in a medium-sized city in Quebec, Canada as a case study. Pollutants commonly found in the snow (TSS, Cr, Pb, Zn, Cl–) were included in the model using event mean concentrations (EMCs) documented in the literature. Bioretention cells performed best on industrial road sites for the entire snowmelt period. Bioretention cell performance was affected by snow management procedures applied to the roads in residential areas. Not modeling the snow cover build-up and meltdown in the simulation led to higher runoff and bioretention cell performance. Modeling results facilitated the identification of bioretention cell sites that efficiently controlled runoff during snowmelt. Such information is needed to support decision planning for BGIs in cities with cold climate.

Machine learning-based detection of transportation infrastructure internal defects using ground-penetrating radar: a state-of-the-art review

Abstract Early detection of internal defects is crucial to ensure the long-term performance and stability of transportation infrastructure. Researchers and practitioners have developed various nondestructive testing (NDT) methods for this purpose. Among them, the ground-penetrating radar (GPR) technique has been widely implemented due to its advantages such as large coverage, traffic-speed survey, and rich subsurface information. In addition, machine learning (ML) algorithms have been frequently applied to achieve automatic GPR data interpretations, which are essential for field applications. However, the fundamental concepts, architectures, and appropriate application scenarios of these algorithms are often questionable to practitioners and researchers. This paper presents a state-of-the-art review of ML applications in the internal defect detection of transportation infrastructure using GPR. In particular, pavements and bridges are covered. The basic knowledge of GPR working principles and ML algorithms is documented. The critical features of the ML algorithms for each detection task are presented. The drawbacks that may hinder the application of ML algorithms using GPR are indicated, including the insufficiency of labeled GPR data, unavailability of GPR dataset, customized ML architecture, and field validation. Finally, possible transfer learning, integrated robotic platform, and data fusion with other NDT methods are discussed. This review paper is expected to serve as a reference for practitioners to choose appropriate ML algorithms to detect internal defects in transportation infrastructure using GPR.

Assessment of Tertiary Education Trust Fund (TETFund) Interventions and Academic Staff Job Performance in Public Universities in Southwest, Nigeria

Purpose: The study assessed TETfund interventions and academic staff job performance in public universities in Southwest, Nigeria. This was by considering the provision, adequacy and accessibility to physical infrastructure and instructional material by academic staff in public universities.
 Methodology: Descriptive survey research design was adopted for the study. The population consisted of 11864 Nigeria public universities academic staff. Nine hundred and twenty-seven (927) lecturers, including Heads of Departments (HODs) formed the sample. Two sets of research instruments were designed for the study; tagged TETFUND Interventions Questionnaire (TIQ) for lecturers; and Academic Staff Job Performance Questionnaire (ASJPQ) for H.O.Ds. The instruments were validated by experts and the reliability of the instruments was also ensured using Test-re-test method. Coefficients of 0.86 and 0.87 were obtained for TIQ and ASJPQ respectively. All hypotheses were tested at 0.05 level of significance. Research questions were answered using frequency counts, mean and standard deviation. The hypotheses were tested using Pearson Product Moment Coefficient.
 Findings: The results revealed that there was a positive, high and significant relationship between physical infrastructure, instructional materials and academic staff job performance. The study concluded that, though TETFund might not have been able to meet all the needs of academic staff, it has to a very high extent positively improved their job performance in terms of physical infrastructures and instructional materials.
 Recommendations: It was therefore recommended that the tempo in the area of physical infrastructure and instructional materials should be intensified and sustained since these structures and facilities significantly contributed to the quality of teaching and research engagements that take place in the university system. The federal government should also increase the percentage of revenue allocated to TETfund for procuring more instructional materials and execution of more infrastructural projects which should be enough to accommodate the students’ enrolment in Nigeria public universities.

Performance evaluation of urban drainage systems: an analytic hierarchy process approach for the Jaracati basin in Brazil

The efficiency of drainage systems in urban centers and especially in their peripheral regions has deserved more and more special attention since their interrelationship with social and environmental aspects is associated with the performance of drainage infrastructures. Integrated urban water management is a major factor in the effectiveness of an urban drainage system. In large cities, negative effects caused by the disorderly urbanization process, especially those related to rainwater runoff, are increasingly present. An evaluation model of urban drainage systems was generated using System Fragility Indicators (IFS) and their ranking via Analytic Hierarchy Process (AHP), in the Jaracati River basin, Maranhão State, Brazil. Indicators were measured through the evaluation of the system and the experience of specialists in drainage systems. The results obtained allowed for the consolidation of a model to support planning and decision-making in the assessment processes of measures to mitigate socio-environmental impacts involved in the urban drainage of a watershed, with performance indicators capable of guaranteeing the support of a model being verified measurement of performance parameters of urban drainage systems.

Acoustic communication and imaging sonar guided AUV docking: system infrastructure, docking methodology and lake trials

Autonomous underwater vehicle (AUV) docking technique attracts attention which leverages the long-term, on-station vehicle launch and recovery for the underwater missions. In this paper, an acoustic communication and imaging sonar guided docking method is proposed to improve the docking accuracy for AUV. In docking system, multibeam forward looking sonar (MFLS) is deployed on the submerged docking station to reduce the noise in acoustic images rather than being fixed on AUV, and the ultra short base line (USBL) transceiver with acoustic communication modules is used to achieve positioning while AUV is out of the view of MFLS. For the docking methodology, a dedicated three-stage docking strategy is presented to guide AUV to the docking station. In the homing stage, AUV navigates to a preset homing place not very far from the docking station via the way-point guidance and self-positioning of the on-board inertial navigation system (INS). In the next stage, USBL provides the relative position for AUV controller via acoustic channel. Moreover, an exponential decay model based filtering algorithm is proposed to eliminate the positioning outliers of USBL. It is expected that AUV will be guided into the perceptual field of MFLS in this stage. In the final stage, the docking accuracy in short range should be improved while USBL may generate outliers due to the louder noise. In this case, the relative positioning is implemented by using kernelized correlation filter (KCF), which is a fast objective tracking algorithm to keep track of the AUV feature in acoustic image until AUV navigates into the docking station or out of the view of sensor. To validate feasibility and performance of the proposed system infrastructure and docking methodology, the docking results are analyzed by contrast with the experiments only use USBL during lake trials.

Deep learning-based energy harvesting with intelligent deployment of RIS-assisted UAV-CFmMIMOs

The ever-evolving internet of things (IoT) has spawned hundreds of wireless sensors that communicate via the internet infrastructure. The lifetime and self-sustainability of these sensors are pivotal factors dictating the performance of respective application infrastructure. Radio frequency energy harvesting (RFEH) technology has exhibited the capability of effectively augmenting the battery lifetime of these sensors. In this work, we introduce a novel framework called CURe, which combines the advantages of cell-free massive multiple-input multiple-output (CFmMIMO) and reconfigurable intelligent surfaces (RISs) to provide uninterrupted energy harvesting for IoT devices through RFEH. CFmMIMO integrates the advantages of distributed systems and massive MIMO, while RIS improves the signal strength of the information transfer and RFEH via its passive reflection capabilities. Moreover, we consider unmanned aerial vehicles (UAVs) equipped with CFmMIMO as mobile access points (APs) to better serve the moving sensory devices. To further enhance RFEH, we propose DeNCE, a channel estimation technique based on deep learning (DL) that eliminates the need for traditional closed-form equation-based channel estimation methods. Through evaluation, we first validate the performance of CURe by comparing it with the modified bisection search for max–min fairness (MBS-MMF) algorithm and later corroborate that DeNCE significantly improves the performances of both models. Finally, to optimize the UAV deployment and ensure continuous RFEH coverage, we propose dARL, a deep reinforcement learning (DRL)-based scheduling framework that enables UAV-CFmMIMO swarms to perform continuous energy harvesting in the coverage area collaboratively.

Infrastructure availability and disruption levels on performance of firms: a survey of Sub-Saharan Africa informal enterprises

PurposeInfrastructure deficiency and supply disruption challenges are quite common among developing economies. While Sub-Saharan Africa is not unique to these challenges, it is the extent of levels of infrastructure deficiency and disruptions that affect the level of performance of small businesses. Literature on the performance of small businesses suggests both infrastructure availability and disruptions affect the performance of small businesses, but the effects on informal enterprises that operate from locations where the supply of infrastructure is weak are less documented. The paper, therefore, investigates the effects of four types of infrastructure supply in two dimensions of availability and disruption levels on the performance of informal enterprises in 12 Sub-Saharan African countries.Design/methodology/approachThe study uses data from World Bank informal enterprises surveys based on a sample of 3 735 informal enterprises. The study uses the multiple analysis of variance method based on the World Bank's Informal Enterprise Surveys (IFS) country-level cross-sectional data collected between 2009 and 2019.FindingsResults show infrastructure supply is quite low irrespective of the form of infrastructure. Infrastructure availability is associated with high supply disruptions. Infrastructure supply deficiency and disruption intensities are negatively associated with informal enterprises' performance. Finally, the effects of both infrastructure availability and supply disruptions are positively associated with informal enterprises' business activity levels.Research limitations/implicationsDue to data limitations, only four types of infrastructure are captured in the analysis. A wider variety of types of infrastructure could improve the analysis.Originality/valueGiven the deficiency level of infrastructure and its implications on informal enterprise development, therefore, policy interventions aiming at addressing informal enterprises' challenges should focus on improving infrastructure supply deficiencies and disruption challenges. This paper provides the link between infrastructure levels, infrastructure supply disruptions and performance of the informal enterprises which is an essential starting point for policy intervention in informal enterprise development.

In-Service Performance Evaluation of Flexible Pavement with Lightweight Cellular Concrete Subbase

The objective of engineers to improve the long-term performance of road infrastructure in changing global climate has led to the development of alternate materials for pavement construction. Lightweight cellular concrete (LCC) is a viable option for colder climates where pavements undergo several freeze-thaw cycles each year, resulting in faster deterioration of pavements. This is due to LCCs’ excellent freeze-thaw resistance, ease of placement, and potential sustainability benefits such as reduced use of virgin material and industrial by-products. However, there is a need to quantify these benefits and develop unified specifications for using LCC in the pavement structure. Therefore, this study examined the performance of flexible pavement sections that included a subbase layer, unbound granular materials for the control section, and three LCC densities (400, 475, and 600 kg/m3) for the LCC sections. Post-construction evaluation of pavement stiffness and roughness were evaluated using a Lightweight deflectometer and SurPro equipment. The results showed that LCC subbase thickness ≥ 250 mm produced over 22% smoother riding surfaces than unbound granular pavements while increasing pavement stiffness by up to 21%. Finally, this study recommends that LCC subbase thickness should not be thinner than 250 mm when using densities below 475 kg/m3 over weak subgrades.

Infrastructure investment planning through scenario-based system-of-systems modelling

ABSTRACT The socioeconomic, technological, and environmental needs and developments call for effective infrastructure investment planning. Because infrastructures are interdependent, this requires the entanglement of their demand and performance development. We propose a scenario-based system-of-systems model to support strategic decision-makers in evaluating the effects of planned investments on transport infrastructure demand and performance under different plausible futures and identifying required investments across transport infrastructure sectors considering interdependencies. The model estimates freight and passenger demand and their consequent emission and energy performance for rail, road, and waterway networks. Through a case study, we show how the model can assist decision-makers in tracing the effects of silo-based planned investments on interconnected infrastructures. Moreover, the case demonstrates how possible investments can be detected to ensure desirable infrastructure performance. Finally, it presents how a better understanding can be created about the impacts of transport demand and energy transition policies on the performance of infrastructure system-of-systems.

Drying of cable backfill by vapour diffusion balanced by capillary water suction: a laboratory experiment with altered groundwater levels

The performance of underground infrastructures, such as an electrical transmission cable, is strongly affected by transmission-induced high temperatures, which have an impact on the properties of the cable and the electric load. Therefore, it is essential to determine if a high load will cause a dry out of a sandy thermal backfill close to the cable. This paper aims at describing processes and establishing criteria for when a dry out can occur. Processes that are addressed are e.g., vapour diffusion and capillary water transport. Criteria of interest are e.g., groundwater levels and capillarity of soil. A new pilot laboratory experiment, with varying groundwater levels, was developed to investigate the dry out of the cable sand in close proximity to an operating cable. The experimental results show that a thermal backfill can withstand a dry out, at a high current load in a cable, if there is a capillary contact to a groundwater level 2 m below the backfill material.

Comprehensive Measurement of the Adhesion between Ice or Glass and Model PDMS Coatings

Ice accretion is an unavoidable phenomenon that endangers the performance of outdoor infrastructure and vehicles at low temperatures. To combat ice accretion, the use of anti‐icing coatings has been one of the most appealing approaches because of their simplicity. Silicone elastomers have been increasingly employed due to their natural hydrophobicity, simple preparation, and low price. However, most of the characterization of silicone‐based polymers and other anti‐icing coatings has been done using unproven techniques that often do not directly relate to materials’ properties. In this work, the adhesion between glass and ice and four PDMS‐based elastomers has been studied by a combination of a macroscale shear test, a microscale technique (the Johnson–Kendall–Roberts (JKR) approach), and a commonly used push‐off test. Results are obtained at different temperatures and, importantly, different test velocities. The shear tests yield an energy release rate (the material's property related to adhesion) in the range of 1–10 J m−2, whereas the quasistatic JKR test yields values in the 0.1–0.5 J m−2 range for glass/PDMS interfaces. Ice/PDMS interfaces are found to have larger energy release rates in shear tests and lower values in quasistatic JKR tests. Both differences can be attributed to differences in interfacial crack dynamics.

Fragility-based system performance assessment of critical power infrastructure

Climate change and population growth require the development and operations of critical power infrastructure systems to meet the energy demand. Performance assessment of the power infrastructure usually focuses primarily on the thermodynamic analysis calculating the energy output and system efficiency. However, the energy output from the thermodynamic analysis under normal operation could not fully represent the infrastructure behavior due to the neglect of the component reliability in the energy generation system. There is a lack of consideration of the energy delivery system and its fragility with respect to external meteorological parameters (e.g., wind). In this study, the fragility of the energy-delivery system concerning wind speed is modeled and integrated to assess critical power infrastructure performance. The reliability consideration of the energy generation system is included based on the state space diagram and the frequency-balance method. The fragility of the energy-delivery system is used to derive a system reliability index for the system which is the probability that end-users will receive the energy. Besides, sensitivity analysis is conducted to investigate the influence of the important decision variables on the system performance. The study employs a small-scale power infrastructure system at the University of Connecticut as an example to demonstrate the feasibility of the performance assessment framework. The system is composed of a cogeneration plant and energy distribution systems. The results from the case study prove that the decision variables have considerable nonlinear impacts on the system performance. With the integration of fragility, the proposed approach could estimate the energy received by consumers from the generation site.

Simulating a Strong Magnetic Storm Impact on Russia’s Interconnected Power System of Center

Studying of a negative impact of the space weather and magnetic storms as its special case, on the performance of a critical technological infrastructure, with development of methods and means for protecting it is a relevant but difficult task. Many advanced countries, primarily the United States, are dealing with this problem. The article presents the results of elaborating a methodology for computational simulation of the impact of geomagnetically induced currents on large electric power systems. The developed methodology makes it possible to take into account the influence of changes in the reactive power and temperature of power transformer structural elements caused by magnetic biasing of their core, as well as the operation of relay protection. The impact of the geomagnetically induced currents expected during a strong magnetic storm on Russia’s Interconnected Power System of Center, including the power systems of Moscow, Leningrad and other major administrative and industrial regions of the country has been simulated for the first time. It is shown that such impact gives rise to a set of factors that can cause the development of a system-wide accident: a significant decrease in voltage at a number of power system facilities, mass-scale disconnection of power lines by their relay protections, and unacceptable heating of structural elements of some power transformers.

Impact of extreme climate and train traffic loads on the performance of high-speed railway geotechnical infrastructures

Impact of extreme climate and train traffic loads on the performance of high-speed railway geotechnical infrastructures

Iterative Feature Selection-Based DDoS attack Prevention Approach in Cloud

Distributed Denial of Service (DDOS) attacks aim to exploit the capacity and performance of a network's infrastructure, making the cloud environment one of the biggest targets for attackers. Many efforts are being made in the field of technology to prevent them from disrupting the services provided. Machine Learning techniques are a means to protect against DDOS attacks. Data preprocessing, feature selection, and classifiers are the main components of any prevention framework. The focus of this study is to find and enhance the feature selection approach for increasing the accuracy of the classifiers in detecting DDOS attacks from regular traffic. We used four different techniques, including Pearson Correlation Coefficient (PCC), Random Forest Feature Importance (RFFI), Mutual information (MI), and Chi-squared(X2) measure which we tested on different classifiers. The first selection approach was based on the feature’s independency level then the second iteration was based on the feature’s importance. We also examined the claim of dropping attacks from the dataset for better accuracy. The best performing set of features was from using PCC and RFFI together for feature selection with average accuracy and precision of 99.27% and 97.60%, which is higher than the use of PCC for both measures by almost 2%. The accuracy is also higher by nearly 12% from the same approach dropping 50% of the attacks.

Effects of Freeze-Thaw Cycles on Permeability Behavior and Desiccation Cracking of Dalian Red Clay in China Considering Saline Intrusion

Red clay with features of high liquid (plastic) limit, low permeability, medium-low compressibility and high strength is widely used in anti-seepage projects including roadbed, earth dam, tailings and landfill cover. This study investigates the hydraulic conductivity and propagation of desiccation cracks of compacted red clay in Dalian, China, considering the effect of freeze-thaw (F-T) cycles and saline intrusion. A series of compacted specimens were subjected to different F-T cycles at various controlled salt concentration of 0.2% and 4%. The surface cracking initiation and propagation process of compacted specimens under wetting-drying (W-D) cycles were monitored by Digital Image Correlation technique. The results indicated that permeability coefficient of compacted specimens increased significantly after the first F-T cycle regardless of specimens with variable dry density and salt concentration. The relationship between the number of F-D cycles and permeability coefficient can be expressed as the exponential function for Dalin red clay. Dry density and Saline inhibits the desiccation cracks of compacted specimens under W-D cycles. However, the F-T cycles have a modest promoting effect on crack propagation on the surface of saturated red clay. This study analyzes the underlying formation mechanisms of desiccation cracking-inducing geohazards and provides some guidance for the long-term performance of infrastructures upon saline intrusion and F-T cycles for red clay.

Effects of Al2O3, SiO2 nanoparticles, and g-c3n4 nanosheets on biocement production from agricultural wastes

Environmental issues are brought up concerning the production of Portland cement. As a result, biocement serves as a reliable substitute for Portland cement in green construction projects. This study created a brand-new technique to create high-quality biocement from agricultural wastes. The technique is based on nanomaterials that improve and accelerate the "Microbially Induced Calcite Precipitation (MICP)" process, which improves the quality of the biocement produced. The mixture was further mixed with the addition of 5 mg/l of graphitic carbon nitride nanosheets (g-C3N4 NSs), alumina nanoparticles (Al2O3 NPs), or silica nanoparticles (SiO2 NPs). The cement: sand ratio was 1:3, the ash: cement ratio was 1:9, and water: cement ratio was 1:2. Cubes molds were prepared, and then cast and compacted. Subsequent de-molding, all specimens were cured in nutrient broth-urea (NBU) media until testing at 28 days. The medium was replenished at an interval of 7 days. The results show that the addition of 5 mg/l of g-C3N4 NSs with corncob ash delivered the highest “Compressive Strength” and the highest “Flexural Strength” of biocement mortar cubes of 18 and 7.6 megapascal (MPa), respectively; and an acceptable “Water Absorption” (5.42%) compared to all other treatments. This treatment delivered a “Compressive Strength”, “Flexural Strength”, and “Water Absorption” reduction of 1.67, 1.26, and 1.21 times the control (standard Portland cement). It was concluded that adding 5 mg/l of g-C3N4 NSs to the cementitious mixture enhances its properties, where the resulting biocement is a promising substitute for conventional Portland cement. Adding nanomaterials to cement reduces its permeability to ions, increasing its strength and durability. The use of these nanomaterials can enhance the performance of concrete infrastructures. The use of nanoparticles is an effective solution to reduce the environmental impact associated with concrete production.