Civil Infrastructure Research Articles

Conditional adaptive time series compensation and control design for multi-axial real-time hybrid simulation

The structural performance of critical infrastructure during extreme events requires testing to understand the complex dynamics. Shake table testing of buildings to evaluate structural integrity is expensive and requires special facilities that can allow for the construction of large-scale test specimens. An attractive alternative is a cyber-physical testing technique known as Real-Time Hybrid Simulation (RTHS), where a large-scale structure is decomposed into physical and numerical substructures. A transfer system creates the interface between physical and numerical substructures. The challenge occurs when using multiple actuators connected with a coupler (i.e., transfer system) to create translation and rotation at the interface. Tracking control strategies aim to reduce time delay errors to create the desired displacements that account for the complex dynamics. This paper proposes two adaptive control methodologies for multi-axial real-time hybrid simulations that improve capabilities for a higher degree of coupling, boundary, complexity, and noise reduction. One control method integrates the feedback proportional derivative integrator (PID) control with a conditional adaptive time series (CATS) compensation and inverse decoupler. The second proposed control method is based on a coupled Model Predictive Control (MPC) with the CATS compensation. The performance of the proposed methods is evaluated using the virtual multi-axial benchmark control problem consisting of a steel frame as the experimental substructure. The transfer system consists of a coupler that connects two hydraulic actuators generating the translation and rotation acting at the joint. Through sensitivity analysis, parameters were tuned for the decoupler components, CATS compensation, and the control design for PID, LQG, and MPC. Comparative results among different control methods are evaluated based on performance criteria, including critical factors such as reduction in the time delay of bothactuators. The research findings in this paper improve the tracking control systems for the multi-axial RTHS of building structures subjected to earthquake loading. It provides insight into the robustness of the proposed tracking control methods in addressing uncertainty and improves the understanding of multiple output controllers that could be used in future cyber-physical testing of civil infrastructure subjected to natural hazards.

Fine-tuning vision foundation model for crack segmentation in civil infrastructures

Large-scale foundation models have become the mainstream deep learning method, while in civil engineering, the scale of AI models is strictly limited. In this work, a vision foundation model is introduced for crack segmentation. Two parameter-efficient fine-tuning methods, adapter and low-rank adaptation, are adopted to fine-tune the foundation model in semantic segmentation: the Segment Anything Model (SAM). The fine-tuned CrackSAM shows excellent performance on different scenes and materials. To test the zero-shot performance of the proposed method, two unique datasets related to road and exterior wall cracks are collected, annotated and open-sourced, for a total of 810 images. Comparative experiments are conducted with twelve mature semantic segmentation models. On datasets with artificial noise and previously unseen datasets, the performance of CrackSAM far exceeds that of all state-of-the-art models. CrackSAM exhibits remarkable superiority, particularly under challenging conditions such as dim lighting, shadows, road markings, construction joints, and other interference factors. These cross-scenario results demonstrate the outstanding zero-shot capability of foundation models and provide new ideas for developing vision models in civil engineering.

An intelligent BIM-enabled digital twin framework for real-time structural health monitoring using wireless IoT sensing, digital signal processing, and structural analysis

Structural health monitoring (SHM) of civil infrastructure is critically important due to its direct influence on public safety and economic activities. Exiting Building Information Modeling (BIM)-based SHM systems often use offline data processing techniques to analyze and visualize structural health data. Despite some of them adopting Internet of Things (IoT) to enable real-time sensor data collection, sensor data quality still remains uncertain due to a lack of sensor signal preprocessing in those existing systems. Additionally, the IoT-based SHM systems often disregard structural analysis domain knowledge, which is important for accurate and precise SHM. Therefore, there is still a need to improve existing systems and practices by enabling more efficient and reliable data collection and processing as well as providing more representative SHM data visualization in BIM. As such, this paper proposes an intelligent BIM-enabled digital twin (DT) framework that integrates wireless IoT sensing and communication, digital signal processing (DSP), and structural analysis domain knowledge. The proposed system (1) leverages IoT sensing and wireless communication to enable autonomous and real-time SHM sensor data collection and transmission, (2) applies and compares multiple DSP techniques to preprocess the sensor data/signals, and (3) innovatively embraces structural analysis expertise into structural behavior visualization in BIM by performing sensor data interpolation for enabling the visualization of structural behaviors at different locations of a structural element/component. The proposed BIM-enabled DT framework was demonstrated and tested for monitoring and visualizing the structural deformations of critical structural components using a prototyped structural frame subject to bending forces. The developed framework could be used and extended for any structural elements (such as beams, columns, trusses, slabs, arches, bracings, walls, footings, foundations, and girders, among others) and could be applied to any kind of structure. Experimental results showed that the proposed framework could effectively monitor and intuitively visualize the structural deformations under different load configurations with a high DT updating frequency of 5 Hz. The innovation of this study is reflected by integrating structural analysis expertise with IoT-enabling sensing data analytics in order to improve the representativeness of real-time structural behavior visualization in BIM and to advance the DT-based SHM systems in a faster and more adaptive direction. Ultimately, this paper contributes to the body of knowledge by developing a generic and easily extendable BIM-enabled DT framework for SHM with high sensor data quality and improved visualization to advance the existing practices of BIM-based SHM for civil infrastructure asset management.

Development and validation of graphics‐based digital twin framework for UAV‐aided post‐earthquake inspection of high‐rise buildings

SummaryTraditional post‐earthquake inspection of civil infrastructure is conducted manually, taking a considerable amount of time and often putting inspectors in harm's way. This problem is exacerbated in modern cities, where millions of people can be left homeless until their residences are deemed safe to reinhabit. Image collection enabled by commercial unmanned aerial vehicles (UAVs) combined with computer vision‐based techniques has provided an alternative with high potential for rapid post‐earthquake inspection. However, the extracted images of the damage alone are inadequate to evaluate the system‐level safety condition of a structure. The quality of the visual information also heavily relies on the effectiveness of the UAV inspection scheme which is susceptible to environmental uncertainties. To this end, a graphics‐based digital twin (GBDT) framework is developed for UAV‐aided post‐earthquake inspection of high‐rise buildings and validated using a high‐rise building in Guangzhou, China. The GBDT is comprised of a finite element (FE) model and a photorealistic computer graphics (CG) model, with the latter being informed by the former, jointly providing as a comprehensive virtual representation of the structure so that every step of the post‐earthquake inspection procedure can be planned and evaluated virtually. First, to avoid the cumbersome nature of constructing the graphical representation of the numerous components in high‐rise buildings, the CG model in the GBDT is created by automatically importing structural components from the FE model and adding nonstructural components according to the dimensions of the as‐built structure. This fast modeling process as well as the accuracy of the virtual presentation are validated by point cloud comparisons between the CG model and the as‐built structure. Subsequently, the GBDT is used to showcase the evaluation of UAV flight schemes for post‐earthquake inspection of high‐rise buildings. To shorten flight time and place more emphasis on potential damage, FE analysis is conducted to determine the earthquake‐induced damage locations. Consistent damage hotspots are then marked on the CG model, along with restrictions from the real environment such as obstacles, weak satellite signal, wind speed, and lighting conditions considered in the synthetic environment. Finally, applying the synthetic environment as the testbed, three UAV‐aided inspection schemes are implemented virtually and the best UAV flight scheme is determined for the assumed field inspection. This example demonstrates the flexibility of the GBDT in representing the real‐world structure and environmental conditions and its efficacy in assisting decision making for rapid and effective structural inspection in the aftermath of an earthquake.

Dynamic response of ultra-high performance engineered cementitious composites (UHP-ECC) under low-velocity impact: Effect of waste rubber incorporation and low temperatures

This study aims to explore the dynamic response of ultra-high performance engineered cementitious composites (UHP-ECC) incorporating waste crumb rubber (CR) at various temperatures, focusing on its potential to enhance the resilience and sustainability of civil infrastructures against low-velocity impacts. To date, the impact behaviour of UHP-ECC under low temperatures has rarely been explored. Firstly, natural river sand and waste tyre CR was utilized to prepare the UHP-ECC. Then, a series of mechanical tests, including compression test, flexural test and uniaxial tensile test were carried out to investigate the static mechanical properties of rubberised UHP-ECCs. In addition, the effects of different waste CR incorporations (0%, 5%, 10%, and 15%) and various temperatures (25 °C, −5 °C, −30 °C, −50 °C, −100 °C and −196 °C) were comprehensively investigated by low-velocity impact tests with constant impact energy. Lastly, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) model was introduced to evaluate the overall performance of UHP-ECC. It was found that the use of river sand and CR significantly enhanced the tensile ductility and impact toughness of UHP-ECC. Impact energy primarily dissipates through damage such as matrix crack initiation, propagation, and fibre pull-out/rupture within the specimen. Adding CR notably decreased stress fluctuations during impact at room temperature, facilitating steady state energy absorption. Moreover, the time to reach peak impact force decreased with decreasing temperature across all UHP-ECC groups. At room temperature during impact process, fibre failure mode is dominated by pull-out failure, while lower temperatures lead to increased fibre rupture at the cracking surface. In low-temperature conditions, the impact response of rubberised UHP-ECC necessitates a comprehensive consideration of the synergistic effects, including material contraction, fibre bridging capacity, rubber phase transition, and water freezing.

AI-Driven Sensing Technology: Review.

Machine learning and deep learning technologies are rapidly advancing the capabilities of sensing technologies, bringing about significant improvements in accuracy, sensitivity, and adaptability. These advancements are making a notable impact across a broad spectrum of fields, including industrial automation, robotics, biomedical engineering, and civil infrastructure monitoring. The core of this transformative shift lies in the integration of artificial intelligence (AI) with sensor technology, focusing on the development of efficient algorithms that drive both device performance enhancements and novel applications in various biomedical and engineering fields. This review delves into the fusion of ML/DL algorithms with sensor technologies, shedding light on their profound impact on sensor design, calibration and compensation, object recognition, and behavior prediction. Through a series of exemplary applications, the review showcases the potential of AI algorithms to significantly upgrade sensor functionalities and widen their application range. Moreover, it addresses the challenges encountered in exploiting these technologies for sensing applications and offers insights into future trends and potential advancements.

Improved implementation of concentrated plasticity models in real-time hybrid simulation

Real-time hybrid simulation (RTHS) is a cost-effective experimental testing technique for evaluating the dynamic response of structural systems. In RTHS, a portion of the structure is modeled numerically (i.e., numerical substructure), while the remaining part is tested physically (i.e., experimental substructure). These substructures are coupled in real time. A significant challenge in RTHS is accurately computing the dynamic response of the numerical substructure in real time during testing. Constrained by this challenge, numerical substructures in RTHS are often oversimplified (e.g., spring-mass-dashpot models), failing to capture more complex dynamic behaviors and limiting the applicability of RTHS. However, with the increasing use of innovative structural systems, new materials, and advanced design guidelines to ensure resilient and sustainable civil infrastructure against natural hazards, the demand for real-time executable higher-fidelity numerical substructures in RTHS is growing. Consequently, concentrated plasticity model (CPM) has gained attention as the numerical substructure in RTHS for its good balance between numerical accuracy and computational efficiency, as well as its popularity in structural engineering applications. Currently, static condensation is utilized to prevent an increase in computational degrees of freedom (DOFs) induced by the hinges in CPM, aiming to save computational cost. However, this method proves counterproductive for nonlinear analyses. While reducing the number of computational DOFs, it complicates the stiffness representation, thereby necessitating more demanding arithmetic operations for stiffness updating. In this study, multi-point constraints are applied to enhance the computational efficiency, specifically for nonlinear analyses involving stiffness updating, and the versatility of CPM. Moreover, direct stiffness updating method and sparse matrix-vector multiplication are introduced for further enhancement in computation. An open-source MATLAB/Simulink-based computational tool, namely the Hybrid2D, is developed to promote the use of the improved CPMs in RTHS as numerical substructures. Numerical simulations are first conducted to verify the accuracy and the computational efficiency of the improved CPMs in Hybrid2D. Subsequently, two RTHS examples are experimentally conducted to demonstrate the effectiveness of the Hybrid2D in enabling RTHS with a higher-fidelity numerical substructure for more complex structural systems.

3D printed self-sensing cementitious composites using graphite and carbon microfibers

Cementitious materials possessing self-sensing properties conferred through the incorporation of conductive fillers have attracted significant interest in the research community due to their potential application to structural health monitoring of civil infrastructure. A key advantage of cementitious sensors is that they can be organically embedded within cementitious structures to provide extremely robust monitoring capabilities. Of interest to this paper, the integration of cementitious sensors could be critical in empowering the 3D printing of cementitious composites by enabling real-time monitoring of additive manufacturing. Such integration could be achieved by 3D printing self-sensing cementitious nodes during the fabrication process. 3D printed self-sensing cementitious sensors could also be topologically engineered to achieve new opportunities in sensing of existing and new concrete and other cementitious structures. This paper presents one of the earliest studies, if not the first, on the 3D printing of self-sensing functionalized composites. The self-sensing cementitious composite sensors are fabricated using a cementitious matrix doped with graphite (G) and carbon microfibers. Various mixtures are studied, in particular G-only, G with milled carbon microfibers (MCMF), G with chopped carbon microfibers (CCMF), and G with MCMF and CCMF. A study of the percolation curves reveals that it is possible to attain electrical percolation using these conductive particles to create strain-sensitive sensors by leveraging the boosted piezoresistive effect. A study of strain sensing performance conducted on various specimens fabricated using different mixture types reveals the optimal concentration of dopants under different types of conductive particles. It is found that the optimal mixture that yields optimal performance also improves the Young’s Modulus of the specimens by 42.2% compared to cement-only specimens. Overall, this optimal mixture composed of 10% weight-to-cement G (10 wt% G), 0.250 wt% MCMF, and 0.125 wt% CCMF yielded a static gauge factor of 622, a resolution of 167 με , an accuracy of 19.24 με , and a Young’s Modulus of 798 MPa.

Integrated approach for sustainability assessment and reporting for civil infrastructures projects: Delivering the UN SDGs

Large civil infrastructure projects, such as airports, highways, railways, townships, and other developments, are critical for contributing to the social, economic, and environmental pillars of sustainability along with the UN-SDGs. However, it is not enough to simply assess their impacts; organizations and stakeholders involved in creating, operating, and maintaining these projects must also report on them. Recent reports by UNEP indicate that the civil infrastructure sector is slow in adopting these principles of sustainability assessment and reporting. The aim of this study is to develop an integrated approach for assessing the sustainability of civil infrastructure projects and sustainability reporting for organizations and stakeholders while achieving the UN-SDGs. To achieve this aim, the study employs a two-step process. In the first step, a correspondence matrix is derived from the sustainability assessment parameters of various infrastructure rating tools (InRTs), Global Reporting Initiative (GRI) standards, and UN-SDGs targets. In the second step, a composite indicator called the Sustainability Alignment Index (SAI) is developed. The SAI comprises two sub-indices, namely the TBL-Sustainability Coverage Index (TSCI) and the TBL-Sustainability Disclosure Quality Index (TSDQI). The correspondence matrix, indices, and sub-indices are then used to evaluate the quantitative and qualitative aspects of the sustainability disclosures of selected civil infrastructure companies in Asia and Europe. The study's findings question the comprehensiveness of the evaluating criteria used in InRTs. It also reveals the lack of alignment between an infrastructure project's sustainability assessment criteria, GRI standards, and UN-SDGs targets. Moreover, the results emphasize the insufficient coverage of environmental sustainability assessment and reporting parameters in the disclosures made by the companies. The developed correspondence matrix, indices, and sub-indices can be used by organizations for comparing both quantitative and qualitative measures to understand their relative positions. Additionally, it will enable the public, policymakers, and other key stakeholders to compare different dimensions of sustainability more effectively.

Environmental Justice and Systems Analysis for Air Quality Planning in the Port of Oakland in California.

Many frontline communities experience adverse health impacts from living in proximity to high-polluting industrial sources. Securing environmental justice requires, in part, a comprehensive set of quantitative indicators. We incorporate environmental justice and life-cycle thinking into air quality planning to assess fine particulate matter (PM2.5) exposure and monetized damages from operating and maintaining the Port of Oakland, a major multimodal marine port located in the historically marginalized West Oakland community in the San Francisco Bay Area. The exposure domain for the assessment is the entire San Francisco Bay Area, a home to more than 7.5 million people. Of the more than 14 sources included in the emissions inventory, emissions from large container ships, or ocean-going vessels (OGVs), dominate the PM2.5 intake, and supply chain sources (material production and delivery, fuel production) represent between 3.5% and 7.5% of annual intake. Exposure damages, which model the costs from excess mortalities resulting from exposure from the study's emission sources, range from USD 100 to 270 million per annum. Variations in damages are due to the use of different concentration-response relationships, hazard ratios, and Port resurfacing area assumptions. Racial and income-based exposure disparities are stark. The Black population and people within the lowest income quintile are 2.2 and 1.9 times more disproportionately exposed, respectively, to the Port's pollution sources relative to the general population. Mitigation efforts focused on electrifying in-port trucking operations yield modest reductions (3.5%) compared to strategies that prioritize emission reductions from OGVs and commercial harbor craft operations (8.7-55%). Our recommendations emphasize that a systems-based approach is critical for identifying all relevant emission sources and mitigation strategies for improving equity in civil infrastructure systems.

Data-driven learning algorithm to predict full-field aerodynamics of large structures subject to crosswinds

Quick and high-fidelity updates about aerodynamic loads of large-scale structures, from trains, planes, and automobiles to many civil infrastructures, serving under the influence of a broad range of crosswinds are of practical significance for their design and in-use safety assessment. Herein, we demonstrate that data-driven machine learning (ML) modeling, in combination with conventional computational methods, can fulfill the goal of fast yet faithful aerodynamic prediction for moving objects subject to crosswinds. Taking a full-scale high-speed train, we illustrate that our data-driven model, trained with a small amount of data from simulations, can readily predict with high fidelity pressure and viscous stress distributions on the train surface in a wide span of operating speed and crosswind velocity. By exploring the dependence of aerodynamic coefficients on yaw angles from ML-based predictions, a rapid update of aerodynamic forces is realized, which can be effectively generalized to trains operating at higher speed levels and subject to harsher crosswinds. The method introduced here paves the way for high-fidelity yet efficient predictions to capture the aerodynamics of engineering structures and facilitates their safety assessment with enormous economic and social significance.

STRENGTH DESIGN OF REINFORCED CONCRETE SECTIONS BASED ON A NUMERICAL OPTIMIZATION METHOD

Reinforced concrete structures are widely used in construction of civil infrastructures. Classical methods are available for the analysis and design of reinforced concrete members, such as beams or columns, which are typically done by hand or using a spreadsheet program. In this study, an analysis and design approach of reinforced concrete sections is developed based on a numerical optimization method, and applied in accordance with the latest concrete design code. The basic idea of the proposed approach is to derive an optimization formulation in order to determine the minimum steel area required to support a given factored load combinations applied on a concrete section. A rigorously derived analysis and design framework of general beam sections is presented. For practical applications by civil and structural engineers, the readily-available optimization solver in Microsoft Excel spreadsheet is used. To illustrate the applicability of the approach, a numerical example with different analysis and design cases is studied. The proposed analysis and design approach using numerical optimization and spreadsheet solver works very well and is easy to implement in the Excel environment. The analysis and design results compare well with hand calculation results based on the classical method.

STRENGTH DESIGN OF REINFORCED CONCRETE SECTIONS BASED ON A NUMERICAL OPTIMIZATION METHOD

Reinforced concrete structures are widely used in construction of civil infrastructures. Classical methods are available for the analysis and design of reinforced concrete members, such as beams or columns, which are typically done by hand or using a spreadsheet program. In this study, an analysis and design approach of reinforced concrete sections is developed based on a numerical optimization method, and applied in accordance with the latest concrete design code. The basic idea of the proposed approach is to derive an optimization formulation in order to determine the minimum steel area required to support a given factored load combinations applied on a concrete section. A rigorously derived analysis and design framework of general beam sections is presented. For practical applications by civil and structural engineers, the readily-available optimization solver in Microsoft Excel spreadsheet is used. To illustrate the applicability of the approach, a numerical example with different analysis and design cases is studied. The proposed analysis and design approach using numerical optimization and spreadsheet solver works very well and is easy to implement in the Excel environment. The analysis and design results compare well with hand calculation results based on the classical method.

Mechanical Characterization of Pultruded GFRP Round Tube under Axial Loading

Abstract: Pultruded Fibre‐Reinforced Polymer profiles are widely used as structural elements in many civil infrastructure applications. However, the anisotropic elasticity and the application‐driven slenderness make these profiles prone to local buckling failure, well below their ultimate load capacity. In this paper, an experimental study was undertaken to characterise the tensile and compressive failure of GFRP round tube under axial loading. Based on the research focus of GFRP structures, this work will present a comprehensive study of the mechanical properties of GFRP tubes in terms of both tensile and compressive loads and investigate the mechanical parameters. This paper presents an investigation on the mechanical properties of pultruded Glass fiber reinforced polymer (GFRP) round tubes for structures subjected to tensile and compressive axial loading. The tensile and compressive strength of GFRP round tubes were first tested. For the stability under compression, the slenderness ratio 6 is adopted. The results show that the tensile strength of GFRP tube could reach 580 MPa, while the compressive strength has been around 72% of tensile strength. Also Experimental results showed that specimens exhibited linearly elastic up to failure. Compared with the single tensile failure mode of GFRP tubes, two types of compressive failure modes, including micro-buckling and local buckling were observed. Moreover, a finite element (FE) analysis was carried to simulate the tensile and compressive behaviours of round tube specimen. The comparison between the tensile and compressive peak load values obtained experiment and FE methods revealed that their difference is less than 5%. These indicated that FE analysis predicted reasonably the actual tensile and compressive behaviours of the pultruded GFRP round tube.

Effects of oil palm mesocarp fibers on the physical and mechanical properties of expansive soils

Palm fiber is one of the favorable materials used in stabilization of soft soil in geotechnical engineering projects, nowadays due to its sustainability, less damage of environment, biodegradability, availability and cost-effectiveness in a context of widespread appeal to the world for return to nature by protecting the earth. On the other hand, expansive soils are renowned for their swelling-shrinkage property and these volumetric changes resultantly cause huge damage to civil infrastructures. Likewise, subgrades consisting of expansive soils instigate serviceability failures in pavements across various regions worldwide. This paper presents results of the laboratory evaluation of expansive soils stabilized with oil palm mesocarp fiber (OPMF), with a view to determine its suitability as flexible pavement construction material. The mixtures were subjected to British Standard heavy (modified Proctor) compaction energy to determine their strength characteristics. The samples were subjected to different tests to ascertain their index properties. Varying proportions of OPMF from 1% to 4% were incorporated into the soil samples and the effects were observed based on compaction and California Bearing Ratio (CBR) results. The control samples without inclusion of OPMF achieved the highest Maximum Dry Densities (MDD), the MDDs reduced linearly as the OPMF content increased. Consequently, the CBR values decreased with increase in OPMF. The reduction in MDD ranged from 20.28g/cm3, 18.5 g/cm3, 15.65 g/cm3, 12.53 g/cm3 as the OPMF increased from 1%, 2% to 3%, 4%. and the optimum moisture content remained almost unaffected due to high-absorbent nature of the fibers. With the inclusion of the OPMF, the CBR value under soaked conditions dropped from 5% to 1.8% rendering them very unsuitable for pavement subgrade. It was concluded that the presence of fiber depreciated the engineering properties of the earth materials. Direct application of OPMF in any part of road pavement has been dissuaded.

In-silico investigation of mechanical behavior of hydrated Na-montmorillonite tactoid

Swelling clay minerals, exemplified by Na-montmorillonite (Na-Mt), are vital constituents in expansive soils and hold significant relevance in geotechnical engineering. Comprehending their distinctive swelling behavior upon hydration is imperative for managing potential damage to civil infrastructure. Conversely, this swelling property gives rise to numerous advantageous applications, including using these clays in barrier materials and nanocomposites. The hierarchical structure of montmorillonite, consisting of clay mineral layers, tactoids, aggregates, and assemblies of aggregates, plays a pivotal role in the swelling behavior of expansive clays. This investigation employs molecular dynamics (MD) and steered molecular dynamic (SMD) simulations to explore the nanomechanical properties of hydrated Na-Mt tactoids at various hydration levels. It provides insights into their responses to compression, tensile, and shear deformation. This study reveals increased hydration levels increase interlayer spacing in clay structures, resulting in higher average d‐values. Water molecules drive attractive electrostatic interactions with clay mineral layers, predominantly mediated by sodium ions, highlighting the complex interplay between interlayer hydration and tactoid stability. Higher hydration enhances tactoid compressibility, reducing the stress required for deformation by compressing water molecules and clay mineral layers in the interlayer space. The study also demonstrates a notable reduction in tensile modulus with increasing hydration, signifying the significant impact of even minimal hydration on interlayer cation-clay layer interactions. Hydrated tactoids exhibit altered mechanical responses in shear deformation compared to dry ones, requiring lower shear stress to detach the top clay layer from the tactoid. The presence of water molecules impedes the locking of clay mineral layers, further influencing mechanical properties. The results and insight provided by this work will contribute to a better understanding of the impact of interlayer hydration on the stability and response of swelling clay minerals.

Erosion behaviour of cold sprayed stainless-steel coatings for civil infrastructures: An energetic approach

Along with corrosion, erosion is one of the main causes of damage to civil infrastructures. The continuous impact of fine particles carried by the air produces a deterioration in the elements of these structures. When this damage is significant, these elements are replaced by new ones. In this work, the possibility of extending the useful life of these components by spraying coatings on the damaged areas is proposed. In this sense, repair techniques such as cold spray deposition are possibilities for this purpose. Therefore, this study focuses on evaluating the erosion behaviour of stainless-steel coatings deposited on structural steel. In addition, the erosion results have been correlated with the model proposed by Hutchings, and an energetic interpretation of the erosion process through this model has been proposed. To carry out this study, stainless steel coatings have been deposited on carbon steel substrates to improve their resistance to corrosion. Different spraying temperatures, from 800 °C to 1100 °C, combined with different pressures of the carrier gas, from 40 to 70 bar, have been analysed. The elastic moduli of the coatings, and their stress-strain curve, have been determined by depth sensing indentation tests with Berkovich and spherical tips, respectively. Erosion tests with normal incidence have been programmed to obtain the corresponding erosion rates. From the results of this work, the optimal projection conditions of stainless steel on carbon steel substrates for the repair of civil infrastructures subjected to erosion processes could be identified.

Impact of space systems capabilities and their role as critical infrastructure

The cyber domain has led to growth in current satellite capabilities, which have become essential due to the increased use of both civil and military critical infrastructure (CI) management systems. In recent decades, outer space has proven to be an increasingly critical sector for the international management of commercial CI, with private operators acting on both multi- and transnational levels. However, the space domain is characterised by not only opportunities but also risks and threats. As the security implications of space were not sufficiently considered at the beginning of the space era, some of the predominant risks currently extend into the commercial sphere. These risks must be considered to ensure the resilience of connected CIs in outer space. Security is a vital issue in the cyber and space domains and should be considered in every phase of a space system's life cycle, from the development and manufacturing of space assets to their deployment and end of life. This involves CI in several sectors, each of which exhibits different but interrelated risks. For example, telecommunications and location systems increasingly require the use of CI, which creates a fragile interdependence that is extremely vulnerable to threats. This paper underlines the importance of recognising space systems as CI and emphasises the need for a better integration of these assets in a system-of-systems analysis. The consequences of global satellite disruption on terrestrial CI are used to support this view. In such a disruptive scenario, mitigation measures based on in-orbit servicing or responsive space capabilities, for example, would allow CI to be restored to first ensure national security followed by commercial activities. Moreover, this paper provides an overview of the legal and policy aspects of using space systems’ capabilities in CI to better understand their implications and encourage the development of recommendations.

Multi-visual modality micro drone-based structural damage detection

Accurate detection and resilience of object detectors in structural damage detection are important in ensuring the continuous use of civil infrastructure. However, achieving robustness in object detectors remains a persistent challenge, impacting their ability to generalize effectively. This study proposes DetectorX, a robust framework for structural damage detection coupled with a micro drone. DetectorX addresses the challenges of object detector robustness by incorporating two innovative modules: a stem block and a spiral pooling technique. The stem block introduces a dynamic visual modality by leveraging the outputs of two Deep Convolutional Neural Network (DCNN) models. The framework employs the proposed event-based reward reinforcement learning to constrain the actions of a parent and child DCNN model leading to a reward. This results in the induction of two dynamic visual modalities alongside the Red, Green, and Blue (RGB) data. This enhancement significantly augments DetectorX's perception and adaptability in diverse environmental situations. Further, a spiral pooling technique, an online image augmentation method, strengthens the framework by increasing feature representations by concatenating spiraled and average/max pooled features. In three extensive experiments: (1) comparative and (2) robustness, which use the Pacific Earthquake Engineering Research Hub ImageNet dataset, and (3) field-experiment, DetectorX performed satisfactorily across varying metrics, including precision (0.88), recall (0.84), average precision (0.91), mean average precision (0.76), and mean average recall (0.73), compared to the competing detectors including You Only Look Once X-medium (YOLOX-m) and others. The study's findings indicate that DetectorX can provide satisfactory results and demonstrate resilience in challenging environments.

A stereovision-based efficient measurement approach for surface flatness of concrete members

The surface flatness of concrete members is an important quality characteristic of buildings and civil infrastructure. Existing measurement methods such as the straightedge method, F-numbers method, and laser scanning method have various limitations such as inefficiency in engineering practices, low accuracy, and high costs. Therefore, this paper presents a novel stereovision-based system comprising two industrial cameras and one projector alongside optimized algorithms to efficiently measure the surface flatness of concrete member. The proposed system is highlighted by effective feature point extraction and matching algorithms, the innovative introduction of feature point extraction error to system measurement error evaluation, and enriched flatness indicators to comprehensively assess surface flatness of concrete members. The outputs of the system, namely, the contour maps as visual demonstrations and the flatness indicator results, can collectively reflect various roughness conditions with high speed and precision. Experiments at multiple levels have validated the proposed algorithms and measurement system, showing that it is capable for providing fast and accurate measures for the surface flatness of concrete members. Further research and developments are sought in system optimization and expanding its applications to other structural members and engineering practices.