Civil Infrastructure Research Articles

Optimizing facility maintenance planning under uncertainty

Maintenance of civil infrastructure is necessary to ensure assets perform as intended over their service life. As these elements age and degrade, appropriate repair planning helps ensure continued operation. Unfortunately, optimal repair planning can be complex due to uncertainty regarding component conditions. This uncertainty also makes budgeting difficult. This paper presents a novel methodology for optimizing maintenance planning of facility components under uncertainty. The proposed methodology integrates a Markov chain Monte Carlo process to capture the uncertainty inherent in equipment degradation, and a mixed integer programming model to generate an optimal repair plan and an associated minimized budget. This approach identifies an optimal set of facility components that need to be repaired to minimize the total facility repair cost required to meet or exceed a user-defined goal for facility health. The proposed method has distinct advantages over many non-linear approaches in literature as it can guarantee an optimal solution with significantly lower computational time. This computational advantage enables the technique to scale to more significant problems, such as determining repair plans across a portfolio of facilities. The method outperforms basic decision heuristics used to define repair plans and existing non-linear optimization methods in terms of consistently meeting a facility health metric at a minimal cost. Data obtained from the U.S. Army Corps of Engineers is used within a case study to demonstrate the feasibility of the proposed approach as it applies to building infrastructure maintenance.

Innovative Life-Cycle Inspection Strategy of Civil Infrastructure: Smartphone-Based Public Participation

The life-cycle inspection of civil infrastructure can guide decisions on structural safety and reliability. This paper proposes a strategy for smartphone-based public participation in the life-cycle inspection of civil infrastructure (SPIC). The SPIC strategy consists of three parts: participants, Urban Brain, and Global View. Next, the feasibility of this strategy is verified by simulation experiments implemented in Unity3D. Civil infrastructure inspection modes include routine inspection, focus inspection, and emergency inspection. The activation and transition of the three inspection modes are tested. Test results show that routine inspection with public participation could timely identify pre-set damaged civil infrastructures. All abnormal civil infrastructures are observed by the Urban Brain. The focus inspection mode is activated when an abnormal civil infrastructure is detected, and the focus inspection provides a detailed and professional assessment of the abnormal civil infrastructure. Then, the inspection of all civil infrastructures in emergency inspection mode is completed in the test. Furthermore, this paper carries out a factor sensitivity analysis of this strategy. The proposed strategy establishes the mechanism for the assign-accept-feedback inspection task. In addition, the simulation performed in Unity3D is one of the next alternatives for large-scale urban disaster prevention and mitigation experiments in the real world. The SPIC strategy can effectively enhance the disaster prevention and mitigation capabilities of civil infrastructure.

Genetic Algorithm-Based Task Assignment for Fleet of Unmanned Surface Vehicles in Dynamically Changing Environment

Unmanned vehicles are gaining the attention of professional operators and the general public. The implementation of unmanned vehicles is evident in, among other fields, emergency management, agriculture, traffic monitoring, post-disaster operations, and delivery of goods. Naturally, a group of unmanned vehicles can cooperatively complete operations more proficiently than a single vehicle. However, several issues must be resolved before a stable and reliable group of unmanned vehicles can be generally deployed to solve tasks in civil infrastructures and in industrial facilities. Here, a framework for the guidance of a fleet of unmanned surface vehicles is proposed. The framework utilizes several levels of control, namely Global Planning Level, Local Planning Level, and Low-Level Control. While the individual vehicles are completely autonomous in their operational locomotion and obstacle avoidance (low-level control and local planning), the task assignment for each vehicle (or group of them) is provided by a global planning process, based on the genetic algorithm. The framework provides a concept to solve complex tasks for the fleet of unmanned surface vehicles (USVs). This includes, but is not necessarily limited to, a dynamically changing environment, different types of USVs with special abilities, multiple types of areal restrictions and obstacles, different restrictions for individual USVs, cooperation of multiple USVs to solve their subtasks, energy consumption optimization, etc. The framework can be advantageously applied to tasks such as warehouse logistics, surface maintenance, area exploration, etc. At the end of the study, the application of the framework is presented using a simulated example of cooperative problem solving using six vehicles.

A Deep Transfer Learning Network for Structural Condition Identification with Limited Real-World Training Data

Structural condition identification based on monitoring data is important for automatic civil infrastructure asset management. Nevertheless, the monitoring data are almost always insufficient because the real-time monitoring data of a structure only reflect a limited number of structural conditions, while the number of possible structural conditions is infinite. With insufficient monitoring data, the identification performance may significantly degrade. This study aims to tackle this challenge by proposing a deep transfer learning (TL) approach for structural condition identification. It effectively integrates physics-based and data-driven methods by generating various training data based on the calibrated finite element (FE) model, pretraining a deep learning (DL) network, and transferring its embedded knowledge to the real monitoring/testing domain. Its performance is demonstrated in a challenging case, vibration-based condition identification of steel frame structures with bolted connection damage. First, disparate subsets of test data are used as training data, and the identification accuracy of the whole dataset is evaluated. The results demonstrate that the proposed approach can achieve high identification accuracy with limited types of training data, with the identification accuracy increasing up to 8.57%. Second, numerical simulation data are used as training data, and then different TL strategies and different DL architectures are compared on the performance of structural condition identification. The results show that even though the training data are from a different domain and with different types of labels, intrinsic physics can be learned through the pretraining process, and the TL results can be clearly improved, with the identification accuracy increasing from 81.8% to 89.1%. The comparative studies show that SHMnet with three convolutional layers stands out as the pretraining DL architecture, with 21.8% and 25.5% higher identification accuracy values over the other two networks, VGGNet-16 and ResNet-18. The findings of this study advance the potential application of the proposed approach towards expert-level condition identification based on limited real-world training data.

Automatic quantification of concrete cracks via multistage image filtration and trajectory-based local binarization

Ensuring the structural integrity of civil infrastructure and maintaining public safety necessitates accurate and reliable periodic inspection of cracks on concrete surfaces. Image-processing crack detection techniques offer an automated cost-effective alternative for identifying and sizing of cracks. However, the distinctive occurrence of cracks and the presence of environmental noises significantly limit detection capabilities. The method proposed in this study utilizes suitable image enhancement techniques based on the variance in gray pixel intensity in the input image. Moreover, the polynomial curve fit noise detrend technique is used to filter out low frequency noises, including shadows and nonuniform illumination. Symmetry and geometric-based filtration are also sequentially implemented for further crack detection. Furthermore, a trajectory-based local binarization is applied to recover broken crack features due to gray-pixel intensity variations. To achieve accurate and consistent results, the fundamental causes of error in crack sizing are identified and addressed. The proposed algorithm's effectiveness is validated using approximately 80 real images collected from various references. The results demonstrate promising outcomes in actual crack detection and sizing.

Finite element modelling of lattice angle steel structures exposed to wildland fires

Essential civil infrastructure such as transmission line supporting towers and communication base towers that are traditionally formed from lattice angle steel structures are critical global assets, since their development promotes the establishment of electric power supply systems and modern telecommunication networks. Because this infrastructure is commonly located in rural and urban wildland areas, such towers may be prone to wildland fire attack, particularly in the current climate of severe heatwaves and droughts. Accordingly, a comprehensive study of the behaviour and response of lattice angle steel structures under fire loading is needed to assist in structural design to resist fire loading. In this paper, a finite element (FE) model of lattice angle steel structures is constructed using ABAQUS software. Several aspects that include material and geometric non-linearities, load eccentricities on component members, bolted joint slippage, and the use of the explicit dynamic analysis technique for a quasi-static loading procedure, are considered and applied in the FE model. The accuracy and reliability of the FE representation are validated by comparing its predictions with relevant existing experimental results. By incorporating the codified temperature-dependent material properties of structural steel as well as a calibrated load-slip relationship for bolted connections at elevated temperatures, the FE model is extended for performing structural fire analysis. The variations of the steelwork temperatures in wildland fires can be calculated based on representative temperature-time wildland fire curves and is adopted in the fire analysis. By invoking the FE model, an example of lattice angle steel structures is contrived to elucidate the fundamental behaviour of these structures in wildland fires, through both steady and transient state fire analyses. Parametric studies are also undertaken to evaluate the effects of the changes in the wind load direction, bolt pretension and the protection of critical panel sections on the behaviour of lattice structures when exposed to wildland fires.

Incorporating Traffic Models into Seismic Damage Analysis of Bridge Road Networks: A Case Study in Central Italy

Transportation networks are one of the most vulnerable civil infrastructures during an earthquake and an estimation of traffic impacts in the post-earthquake scenario is a crucial aspect in the context of risk assessment and evaluation of remedial measures. In this paper, a methodology is presented, combining GIS tools, probabilistic seismic risk analysis and traffic simulation models, which is able to assess the direct and indirect (social) costs: bridge repairs, increase in travel time and a lack of accessibility. Operating issues related to the development and calibration of traffic models applicable to a damaged road network are carefully analysed and reviewed and an innovative approach to evaluate the social cost due to the lack of accessibility is also proposed. The developed modelling framework has been applied on a realistic bridge stock within a road transportation network in central Italy where local land-use data have been collected, extensive traffic surveys have been performed and a traffic model has been calibrated. A probabilistic risk analysis employing a ShakeMap derived from a historical real seismic event has been carried out.

ОПОДАТКУВАННЯ ПІДПРИЄМСТВ В КОНТЕКСТІ ПОВОЄННОГО СТИМУЛЮВАННЯ ЕКОНОМІЧНОГО РОЗВИТКУ УКРАЇНИ

The tax system of Ukraine has gone through a rather long path of transformation and improvement, but even today it has a number of shortcomings that do not allow it to fulfill one of its main tasks - to contribute to the solution of the primary needs of the state economy. A full-scale military invasion, the occupation of part of the territories, and unprecedented levels of destruction of civil and industrial infrastructure require the search for ways to actively stimulate postwar development. And it is the tax system in this context that can act as one of the key factors of economic recovery, which actualizes the need to study the spectrum of its modern problems and find possible ways to solve them. Purpose. Analytical substantiation of the problems of the current system of taxation of enterprises in Ukraine and possible ways of its optimization in the context of the post-war stimulation of economic development. Methods. The methodological basis of the study was made up of general scientific and special methods of economic theory, in particular, methods of theoretical generalization and comparative analysis with the aim of a comprehensive consideration of the issue under study. In the course of the study, statistical materials of the European Union and the State Statistics Service and the Ministry of Finance of Ukraine on the issues of taxation of enterprises and were used and processed by methods of statistical analysis: comparison, grouping, calculation of shares. Results. The level of tax burden of enterprises in Ukraine was calculated and compared with similar indicators of the EU countries. The key problems of the current system of taxation of enterprises in Ukraine in terms of value added tax and income tax are analytically substantiated. Alternative options for taxation for the post-war period are proposed with justification of the expected effects for the country’s economy as a whole.

Building Surface Crack Detection Using Deep Learning Technology

Cracks in building facades are inevitable due to the age of the building. Cracks found in the building facade may be further exacerbated if not corrected immediately. Considering the extensive size of some buildings, there is definitely a need to automate the inspection routine to facilitate the inspection process. The incorporation of deep learning technology for the classification of images has proven to be an effective method in many past civil infrastructures like pavements and bridges. There is, however, limited research in the built environment sector. In order to align with the Smart Nation goals of the country, the use of Smart technologies is necessary in the building and construction industry. The focus of the study is to identify the effectiveness of deep learning technology for image classification. Deep learning technology, such as Convolutional Neural Networks (CNN), requires a large amount of data in order to obtain good performance. It is, however, difficult to collect the images manually. This study will cover the transfer learning approach, where image classification can be carried out even with limited data. Using the CNN method achieved an accuracy level of about 89%, while using the transfer learning model achieved an accuracy of 94%. Based on this, it can be concluded that the transfer learning method achieves better performance as compared to the CNN method with the same amount of data input.

Optimal sensor placement for structural parameter identification of bridges with modeling uncertainties

The uncertainties in the numerical models used for the optimal sensor placement (OSP) studies of civil infrastructures, specifically bridges, considerably affect the results. These effects can be more prominent if the modeling uncertainties are of a kind that significantly alters the mode shapes of the structure, such as the boundary conditions of the model. Yet, these effects on the results of OSP analysis remain unexplored, and there are no available methodologies to address all types of model uncertainties in OSP for civil infrastructures. This research presents a new framework to determine the optimal sensor locations to identify the modal properties of bridges under severe modeling uncertainties and its application on a railway bridge. The framework includes finite element model generation and a sensitivity study to select the most influential parameters that change the dynamic response of the bridge. The selected parameters are used in Monte Carlo simulations, and the results enable quantifying the relative amount of information presented at the candidate sensor locations. This information is combined with the spatial position of the candidate sensor locations, and a hierarchical clustering algorithm is used to obtain the optimal sensor locations and the number of sensors. In addition, the OSP analysis is carried out using the Effective Independence method to contribute to the state-of-the-art literature that investigates the uncertainties in OSP for civil infrastructures and uses this method.

Analysis of RC Multistory Building with and Without Blast Forces

Blast resistant and anti-terrorism design is essential in order to save the civil infrastructure and human lives. In the design of structures, a consideration for energy absorbing capabilities to resist the effects of blast loading or other severe dynamic loads it is vital, and structural provision of elements with large plastic deformation capacities is desirable. Structures need to be designed for ductile response in order to prevent partial or total collapse due to locally failed elements. The work in this paper considers theoretical studies to resist the structure against blast loading to understand the basic concept of explosion phenomena, parameters of blast loading, and different types of blast waves. The analysis focuses on the estimation of blast loading on structures and their response to explosions. Design of blast resistant structures requires thorough understanding of the structural dynamics, behaviour of materials under high strain rate of loading, and blast analysis. This paper concludes with an explanation of findings by different researchers using different country codes to simplify the design process for calculating blast load. Keywords: Blast Resistant Design, Blast Load, Explosion, ETABS.

The silent impact of underground climate change on civil infrastructure

Urban areas increasingly suffer from subsurface heat islands: an underground climate change responsible for environmental, public health, and transportation issues. Soils, rocks, and construction materials deform under the influence of temperature variations and excessive deformations can affect the performance of civil infrastructure. Here I explore if ground deformations caused by subsurface heat islands might affect civil infrastructure. The Chicago Loop district is used as a case study. A 3-D computer model informed by data collected via a network of temperature sensors is used to characterize the ground temperature variations, deformations, and displacements caused by underground climate change. These deformations and displacements are significant and, on a case-by-case basis, may be incompatible with the operational requirements of civil structures. Therefore, the impact of underground climate change on civil infrastructure should be considered in future urban planning strategies to avoid possible structural damage and malfunction. Overall, this work suggests that underground climate change can represent a silent hazard for civil infrastructure in the Chicago Loop and other urban areas worldwide, but also an opportunity to reutilize or minimize waste heat in the ground.

Problems and prospects of entrepreneurship development in Ukraine during the state of war

Problems and prospects of entrepreneurship development in Ukraine during the state of war

Seismic site characterization using MASW and correlation study between shear wave velocity and SPT[sbnd

A seismic site characterization study is carried out in the south of the foothills of tectonically active Himalayan region. During an earthquake, the dynamic characteristics of the shallow subsurface strata can influence seismic shaking in the region, causing damage to civil infrastructures. The seismic site characterization study with shear wave velocity (Vs) is performed using the Multi-channel Analysis of Surface Wave (MASW) technique, which is particularly challenging to implement in the densely inhabited areas. However, the standard penetration test (SPTN) commonly measured by civil engineers can be utilized to compute Vs of such areas by establishing a correlation between Vs and the SPT-N values.In the study area, MASW data were acquired at twelve different locations, whereas SPT-N tests were available from 26 boreholes. Twelve borehole locations closer to the MASW survey lines were paired to establish relationships between SPT-N and Vs. The developed linear and non-linear correlation functions were found to be statistically similar. Furthermore, the linear correlation function is used for estimating Vs from SPT-N in remaining 14 boreholes. The new correlation functions were further compared with previously reported empirical relationships from other areas. The current relationship can be used for geoengineering purposes at different sites with similar geological conditions.Shear wave velocity derived from the MASW surveys and estimated from the new empirical relationship was utilized for seismic site characterization based on average shear wave velocity up to 30 m depth (Vs30). The Vs30 map of the study site is prepared and classified in accordance with the National Earthquake Hazard Reduction Program (NEHRP-2020). The calculated Vs30 value of the study site ranges from 285 to 375 m/s. The major portion of the study area lies under Site Class CD, with a minor portion under Site Class D. In the event of an earthquake, some zones can potentially have stronger ground amplification, particularly in Site Class D and may have damaging effects. A series of average shear wave velocity maps for different depths are prepared to help geotechnical engineers in identifying and mitigating geotechnical risks. Furthermore, there are potential geohazard risks as certain depths show low shear velocity.

Simulating floodwater movement in pavements for developing post-flooding time-depth-damage functions

Flooding has caused severe damage to civil infrastructure worldwide. Roadway pavements are one of the infrastructures most affected by flooding, however, quantification of post-flooding pavement damage has not received attention until recently. There remains a gap to advance knowledge on how floodwater moves inside pavements and, consequently, how it will impact post-flooding pavement damage. This study adopted finite element seepage modeling to observe floodwater movement in three different pavements, each under three flood scenarios. Laboratory testing is conducted to validate the accuracy of the model. It is concluded that both the finite element seepage modeling and laboratory testing show similar post-flooding weakening and recovery behavior in pavements. The recovery phase is much longer than the weakening phase, due to the hysteretic nature of soil moisture related to suction. The base layer loses stiffness drastically during the weakening phase, more than any studied subgrade materials. The improved understanding reported here can enhance the development of time-depth-damage functions for post-flooding pavements. It will enable the quantification of post-flooding damage and thus facilitate estimation of flood induced economic losses in practices.

A novel smart steel strand based on optical-electrical co-sensing for full-process and full-scale monitoring of prestressing concrete structures

ABSTRACT As the main load bearing component, the steel strand has a significant impact on the safety of civil infrastructure. Real-time monitoring of steel strand stress distribution throughout the damage process is an important aspect of civil infrastructure health assessment. Hence, this study proposes an optical-electrical co-sensing (OECS) smart steel strand with the DOFS and CCFPI embedded in. It can simultaneously measure small strains in the initial damage phase with high accuracy and obtain information in the large deformation phase with relatively low precision. Several experiments were carried out to test its sensing performance. It shows both DOFS and CCFPI have good linearity, repeatability and hysteresis. In comparison to DOFS, CCFPI has a relatively lower accuracy and resolution, but a large enough measurement range to tolerate the large strain in the event of a steel strand failure. To verify the reliability of the proposed smart steel strand in real structures, the strand strain distribution in the full damage process of bonded prestressed beams under four-point bending loading was monitored using the smart steel strand as a prestressing tendon. The strain measured by the OECS steel strand is shown to reflect the deformation and stiffness variation of prestressed beams under different load.

Lessons Learned and Recommendations for the Application of Systems Engineering as an Emerging Discipline in Transportation & Infrastructure Projects

AbstractWhile systems engineering (SE) has been a well‐established discipline focusing on interdisciplinary systems and engineering management of complex systems over their life cycles, SE is still widely unknown in the U.S. infrastructure industry.The U.S. has recently passed a $1 trillion infrastructure bill (H.R. 3684), calling for investments in roads, bridges, rail, ports, airports, power, water, broadband, and other major projects. It is the intent of this paper to provide practical guidance to infrastructure owners and operators who are interested in reaping the benefits of applying SE to their transportation and infrastructure projects.This paper memorializes the lessons learned from over a of decade of real‐world, hands‐on experience of introducing and gradually increasing the application of systems engineering to building the civil infrastructure of the California High‐Speed Rail System (CHSRS), starting with the environmental impact review, preliminary engineering, final design, construction, inspection and testing, and finally the certification and planned handover of 119 miles of civil work and over 225 individual structures to the following track and systems contracts.As the first three CHSRS civil works construction packages (CP) are currently nearing completion, three new civil work and passenger station procurement contracts have recently been awarded, extending CHSRS to 171 miles and close to 300 structures, including four passenger stations, with additional track and systems, trainset, and train operator contracts planned in the near future.The extension of the CHSRS presented a timely opportunity to incorporate the SE lessons learned during the first three construction packages and update the systems engineering process requirements for the new CHSRS extension projects going forward. The SE requirements are presented in form of Systems Development Life Cycle (SDLC) requirements and have been prepared as tailored requirements for both civil infrastructure and track and systems contracts.This paper intends to share the lessons learned and present them as specific and actionable recommendations, providing practical guidance for the application of SE to transportation and/or infrastructure projects.

Corrosion inhibitors for enhanced strength, durability, and microstructure of coastal concrete structures

The prevalence of catastrophic structural member failure caused by steel corrosion in civil infrastructure underscores the importance of reducing reinforcement corrosion to enhance overall infrastructure costs, reliability, and sustainable development. The present research investigates the potential of corrosion inhibitors to enhance the durability and strength of concrete structures, with a focus on their long-term effectiveness in resisting corrosion in reinforced concrete structures. Multiple approaches such as inhibitors, repairing processes, and coatings have been explored to prevent concrete corrosion damage, with an emphasis on concrete corrosion performance in coastal and corrosive situations. This study investigates the effect of six different corrosion inhibitors (zinc oxide, magnesium oxide, urea, sodium nitrate, sodium molybdate, and diethyl ether) on the compressive strength and durability of concrete samples. The compressive strength is assessed using both destructive (28 days cube compressive strength) and non-destructive (Ultrasonic Pulse Velocity) test methods, while concrete durability is evaluated using the rapid chloride permeability test (RCPT). The compressive strength of the admixture incorporated samples are found to be higher than the control sample by almost 50% and above with excellent concrete quality. The RCPT values of inhibitor-incorporated samples are moderate and low with control samples having high permeability even in adverse conditions of freezing, thawing, and deicing. The samples incorporated with inhibitors also show less negative half-cell potential which is 1.43 times less than that of the control sample indicating the lesser probability of occurrence of corrosion. SEM imaging is also conducted to analyze the microstructure of each mix. The findings of this study highlight the importance of inhibitors in enhancing the durability of reinforced concrete structures.

Deep Learning-Based RGB-D Fusion for Multimodal Condition Assessment of Civil Infrastructure

Deep Learning-Based RGB-D Fusion for Multimodal Condition Assessment of Civil Infrastructure

Russia’s War Against Ukraine: A Lithuanian Perspective

In this essay, we present the Lithuanian perspective on Russia’s war against Ukraine. We discuss the facts on the ground, the attribution of responsibility, and touch upon the potential ways forward. In Lithuania’s view, it is clear that Russia is responsible for the war and all of the associated outcomes, including first and foremost the loss of life and other forms of suffering in Ukraine, the destruction of Ukraine’s economy and civil infrastructure, but also the wider repercussions for the world regarding higher prices, increased instability, and heightened political/military tensions. We refute the argument that the enlargement of NATO/EU in any way provoked or posed a threat to Russia and instead argue that this process created conditions for economic growth and stability in the admitted countries. Finally, on both moral and practical grounds, we suggest that the best course of action for creating long-lasting conditions for peace and stability is to support Ukraine in its struggle against the illegal and unprovoked invasion.