Civil Structures Research Articles

Numerical Simulation of a Floating Offshore Wind Turbine Incorporating an Electromagnetic Inerter-Based Device for Vibration Suppression and Wave Energy Conversion

Offshore wind turbines (OWTs) are considered vital to the promotion of the development of renewable energy. Especially, floating OWTs can be deployed over a larger area than bottom-fixed OWTs. The floating OWTs, however, are vulnerable to vibration induced by disturbances and require a backup power supply in the case of power outage. On the one hand, various kinds of inerter-based devices have been proposed especially for vibration suppression of civil structures subjected to earthquake loadings. Recently, combined with electromagnetic devices, the inerter technologies have also been applied in the field of vibration energy harvesting such as point absorber wave energy converters. Thus, this paper proposes a novel floating OWT consisting of two bodies combined with inerter-based power take-off (PTO) devices which accomplishes vibration suppression and wave energy conversion at the same time. To investigate the vibration suppression and energy conversion capabilities of the proposed floating OWT with a variety of inerter-based PTO devices for ocean waves, numerical simulation studies employing WEC-Sim are conducted, and the performance of each system is compared for regular and irregular waves. Results show that the proposed floating OWT with the appropriately designed inerter-based PTO devices for the incident wave period has great potential for both vibration suppression and wave energy conversion in a specific frequency range.

A physics-informed neural network-based approach to reconstruct the tornado vortices from limited observed data

The measured wind data of tornado vortices is often insufficient and low-resolution for analysis of tornado-induced loads and effects on civil structures. This study proposes a physics-informed neural network-based (PINN-based) approach to reconstruct the tornado vortices from limited observed data. First, the PINN model is constructed by embedding the simplified tornado governing equations in a deep neural network as the prior physical information. The PINN model is then validated by the reconstruction of different numerical tornado vortices generated by large eddy simulations. Parametric analysis is also conducted to investigate the influence of the structure of the PINN and the number of observation points. Finally, the proposed approach is applied to reconstruct a real tornado vortex. Results show that the PINN-based method can accurately reconstruct the numerical tornado vortices in different stages with a relative error below 4% for tangential velocity and vertical velocity, and 11% for radial velocity. Increasing the number of observation points and the number of points for training physical equations will improve the reconstruction accuracy. The proposed approach can correctly reconstruct the real tornado vortex with a relative error of less than 10%.

A novel double-hybrid learning method for modal frequency-based damage assessment of bridge structures under different environmental variation patterns

Monitoring of modal frequencies under an unsupervised learning framework is a practical strategy for damage assessment of civil structures, especially bridges. However, the key challenge is related to high sensitivity of modal frequencies to environmental and/or operational changes that may lead to economic and safety losses. The other challenge pertains to different environmental and/or operational variation patterns in modal frequencies due to differences in structural types, materials, and applications, measurement periods in terms of short and/or long monitoring programs, geographical locations of structures, weather conditions, and influences of single or multiple environmental and/or operational factors, which may cause barriers to employing state-of-the-art unsupervised learning approaches. To cope with these issues, this paper proposes a novel double-hybrid learning technique in an unsupervised manner. It contains two stages of data partitioning and anomaly detection, both of which comprise two hybrid algorithms. For the first stage, an improved hybrid clustering method based on a coupling of shared nearest neighbor searching and density peaks clustering is proposed to prepare local information for anomaly detection with the focus on mitigating environmental and/or operational effects. For the second stage, this paper proposes an innovative non-parametric hybrid anomaly detector based on local outlier factor. In both stages, the number of nearest neighbors is the key hyperparameter that is automatically determined by leveraging a self-adaptive neighbor searching algorithm. Modal frequencies of two full-scale bridges are utilized to validate the proposed technique with several comparisons. Results indicate that this technique is able to successfully eliminate different environmental and/or operational variations and correctly detect damage.

Probabilistic machine learning approach for structural reliability analysis

In recent years, the surrogate modeling technique has been increasingly employed for the failure probability estimation of civil structures. The surrogate modeling technique acts as a black box that predicts the performance of expensive-to-evaluate problems. However, the uncertainty of failure probability estimation due to the discrepancy between the surrogate model and physical model has not been well studied. This study introduces a new surrogate modeling technique for failure probability estimation of civil structures that utilizes a Bayesian neural network. Specifically, a fully connected neural network is constructed to approximate the limit state function. The hyperparameters in the neural network are obtained within the Bayesian paradigm, and the probability distributions are estimated through Laplace approximation. When the Bayesian neural network method is used for reliability analysis, the failure curve and the corresponding uncertainty can be estimated. Numerical studies on 2d truss, a simple support beam under moving load, and dynamic analysis of subway station considering the soil–structure interaction are conducted to validate the efficiency of the approach. Results with Monte Carlo simulation and subset simulation are also presented. The results demonstrate the proposed method’s potential for improving prediction accuracy by factoring in the uncertainty embedded in the surrogate model. Additionally, it exhibits higher levels of efficiency than traditional methods.

Long-term continuous automatic modal tracking algorithm based on Bayesian inference

Modal tracking plays a vital role in structural health monitoring since changes in modal parameters help us understand a structure’s dynamic characteristics and may reflect the potential deterioration of structural performance. Although numerous modal parameter estimation (MPE) methods exist, it is not guaranteed that an MPE process will exclude all spurious modes and not lose any physical modes every time over a long-term monitoring period. Relatively large damping of a structure, poor data quality, and significant changes in structural modal parameters may make the estimated modal parameters spurious, missing, or misclassified. It makes long-term modal tracking semiautomated or manual, which constrains timely downstream applications such as anomaly detection, condition assessment, and decision making. This research aims to propose a long-term continuous automatic modal tracking algorithm based on Bayesian inference even when the modal parameters, damping, and data quality change significantly. Bayesian inference is used to determine the physical modes from the results of existing MPE methods. Both the modes identified from the most recent response set and the modal probability model from multiple previous response sets are considered in the Bayesian model to better determine the physical modes from the results of MPE. Moreover, the proposed algorithm requires only three extra hyperparameters compared to general modal tracking algorithms, and they can be quickly determined by a grid search method. The performance of the proposed algorithm is verified by a numerical example and a real-world civil structure Z24 Bridge benchmark.

Investigation of the electrical sensing properties of cementitious composites produced with multi-wall carbon nanotubes dispersed in NaOH

The use of nanomaterials, such as carbon nanotubes, in cement-based composites has been a topic of considerable attention in recent years due to their potential to monitor civil structures and infrastructures. One of the main challenges is the adequate and efficient dispersion of the carbon nanotubes due to their nanoparticle size and the high adhesion forces developing among them. The most common strategy to tackle this challenge is to use sonic treatments that are unfeasible for field-scale implementations. Alternatively, specific chemicals (i.e. surfactants) facilitating nanotube mixing are used, but this often results in poor mechanical properties, reduced electrical conductivity and high environmental impact of the composites. This works has as the main aim to analyze the self-sensing properties of the cementitious composites developed with NaOH as the dispersing agent. For this purpose, the work investigates electrical and sensing properties of cement-based nanocomposites with different contents of multi-wall carbon nanotubes (MWCNT), namely 1%, 1.5%, and 2% with respect to cement weight, previously dispersed in NaOH aqueous solution. The composites' fresh and electrical properties are evaluated, and a microstructural analysis is carried out. The major findings of the work reveal that cement paste samples with different contents of MWCNT presented increasing sensing properties up to 2%, and that the dispersing strategy was promising despite its simplicity.

Condition transfer between prestressed bridges using structural state translation for structural health monitoring

Implementing Structural Health Monitoring (SHM) systems with extensive sensing layouts on all civil structures is obviously expensive and unfeasible. Thus, estimating the state (condition) of dissimilar civil structures based on the information collected from other structures is regarded as a useful and essential way. For this purpose, Structural State Translation (SST) has been recently proposed to predict the response data of civil structures based on the information acquired from a dissimilar structure. This study uses the SST methodology to translate the state of one bridge (Bridge #1) to a new state based on the knowledge acquired from a structurally dissimilar bridge (Bridge #2). Specifically, the Domain-Generalized Cycle-Generative (DGCG) model is trained in the Domain Generalization learning approach on two distinct data domains obtained from Bridge #1; the bridges have two different conditions: State-H and State-D. Then, the model is used to generalize and transfer the knowledge on Bridge #1 to Bridge #2. In doing so, DGCG translates the state of Bridge #2 to the state that the model has learned after being trained. In one scenario, Bridge #2’s State-H is translated to State-D; in another scenario, Bridge #2’s State-D is translated to State-H. The translated bridge states are then compared with the real ones via modal identifiers and mean magnitude-squared coherence (MMSC), showing that the translated states are remarkably similar to the real ones. For instance, the modes of the translated and real bridge states are similar, with the maximum frequency difference of 1.12% and the minimum correlation of 0.923 in Modal Assurance Criterion values, as well as the minimum of 0.947 in Average MMSC values. In conclusion, this study demonstrates that SST is a promising methodology for research with data scarcity and population-based structural health monitoring (PBSHM). In addition, a critical discussion about the methodology adopted in this study is also offered to address some related concerns.

Improved intensity measures considering soil inelastic properties via multi-regression analysis

At certain depths, the elastic properties of the ground are not affected by seismic waves. However, as they reach the surface, the soil density decreases and so does its elastic limit. This means that the expected ground motion acting at the foundation of a structure cannot be adequately described without considering the inelastic response of the soil near the surface. Therefore, one of the key elements in characterizing the seismic response of civil structures is the site effect. These depend mainly on the parameters of the soil beneath the structure and the features of the ground motion acting at the depth, where non-linear effects are negligible. Therefore, the main objective of this paper is to find an intensity measure that incorporates the information provided by the soil profile under the structure and the ground motion acting at the bedrock level. Due to the random nature of both elements, a probabilistic framework using Monte Carlo simulation has been developed to analyze this problem. For this purpose, random soil profiles have been generated to obtain a representative sample of likely scenarios of the study area. A large database of Colombian ground motion records has been used to model the seismic hazard. Finally, power functions capable of relating the input variables to the dynamic response of a large set of reinforced concrete structures have been derived by considering multi-regression analysis. It has been observed that, in several cases, intensity measures extracted from the displacement spectrum appear in the mathematical arrangements. These functions could be used to improve the efficiency of seismic risk prediction at the urban level.

Active Infrared Thermography applied for concrete structures inspection in Nuclear Power Plants

Due to the new requirements that are being demanded of the Nuclear Power Plants for their License Renewal (LR), Life Extension (LE) and Long Term Operation (LTO), it is necessary to verify the integrity of several civil structures, such as the containment building, used fuel building and cooling towers, among others. In general, the objectives of Non-Destructive Test methods (NDT) for the inspection of concrete structures in nuclear power plants can be arranged into: location of the reinforcement and tendons, determination of the state of the concrete matrix, determination of the corrosion of the metallic armor and detection of cracks, voids, inclusions and delaminations in the matrix of the structure. For this, Tecnatom, based on the experience acquired in the field of NDT, has developed a set of techniques for determining the structures condition including surface hardness testing, ultrasound (transverse wave pulse-echo and transmission speed), electromagnetic methods (Ground Penetrating Radar - GPR) and non-contact techniques such as Infrared Thermography (IRT). The method and technique best suited for a given inspection has to be selected depending on the objective sought, the typical defectology of the component, the characteristics of the structure and the conditions of the inspection itself. In this work it is presented an example of the development and in-field application of the IRT method for the inspection of a concrete structure in a nuclear power plant to detect possible delaminations caused by the corrosion of the metallic reinforcement.

Post Fire Behavior of Structural Reinforced Concrete Member (Slab) Repairing with Various Materials

One of the most significant building materials used to build a variety of infrastructure, military, and civil structures is concrete. It can effectively withstand fire mishaps for a long period of time. This study employs a finite element simulation approach in Three steps: the first involves applying mechanical loading, the second involves applying mechanical and thermal loading; and the third involves strengthening the damaged model. Two different strengthening procedures were used to evaluate the performance of the fire-damaged slab. Two types of strengthening techniques—carbon-fiber-reinforced polymer (CFRP) sheet and slurry-infiltrated fibrous concrete (SIFCON) jacketing—were used. Studying the performance of SIFCON and CFRP together and in two different thicknesses of each for repairing both normal and high-strength concretes after fire exposure is considered limited. An investigation of their behavior can provide insights into how effective the restoration of strength is. The study aims to assess how well various repair materials perform in restoring the durability and strength of reinforced concrete members after being exposed to fire. This will assist in determining the best materials for concrete repair after a fire. Results show that the enhancements by SIFCON with a thickness of 30 mm significantly improved many indices, including load displacement behavior, ductility, and absorption energy of the slab. Doi: 10.28991/CEJ-2023-09-08-013 Full Text: PDF

Analytical and numerical study of rectangular reinforced concrete beam using robot structural analysis software

This simulation is significant in providing value for the fields of civil engineering, architecture and structure maintenance. While the studies of reinforcement concrete beam focus on the bending moment, shear force deflection and cracks, this has demonstrated its importance in ensuring the stability of structures. This study aims at delimiting and comparing the analytical and numerical results examined through Robot Structural Analysis software. That is, it is applied to investigate the rectangular reinforced concrete beam by uniformly load in order to effectively, intuitively, rapidly and simply modify and deal with any physical and geometrical parameter by means of this software. The use of this software can ensure non-linear analyses and take into account plastic behaviors of materials as well as big deformation and instability of structure element. The numerical results are compared with the analytical results in the middle of the span. It is found that the maximum bending moment and the maximum shear force have the same values in the case of a live load, but in the case of the combination load, there is a small difference. As regards the maximum deflection, there is a small difference which is linear for the load range. As for cracks, it is observed that the width of cracks is in accordance with Eurocode 2 standards and the approximative results are found experimentally in comparison with other studies.

Infrared imaging for detection of defects in concrete structures

Deterioration of concrete structures is an issue that affects the entire world and is brought on by various causes like climatic conditions, aging, attack of aggressive media, etc. Periodic inspections and proper maintenance are crucial to prevent the degradation of structures. Conventionally, the condition of the structures is monitored visually but over a while, non-destructive evaluation (NDE) technologies have gained significance and are expected to be successfully used for the inspection and management of civil structures. Infrared imaging is an effective NDT tool to monitor the health of civil structures. In this study, Infrared Thermography (IRT), an image-based method, is introduced and implemented for the assessment of concrete structures. Experiments on cement concrete cubes were conducted to investigate and enhance the applicability of the infrared thermography technique. The results are quite promising and helps to detect defects like delamination, voids, and cracks, etc. as the IR camera can pick-up the difference in temperature between sound and defective regions. However, the utilization of IRT for inspection poses many challenges but they can be overcome by combining it with other techniques like finite element method, acoustic emission and ultrasonic guided waves. Hence, the infra-red thermography technique is an excellent technology for practically evaluating concrete structures.

Bio Concrete: A Sustainable Future Alternative

Bio concrete is a type of concrete which fills the voids developed in a civil structure due to presence of bacteria. The cracks are formed due to various activities like evaporation, shrinkage, land slip, vibration etc. Bacteria helps in formation of limestone. When calcium lactate reacts with water, limestone is formed which helps to fill the voids present. The void formation can be analysed by XRD and SEM analysis.

Impact-based energy harvesting characteristics of cement/Pb(Zr0.52Ti0.48)O3 composites

This work presents the impact generated energy harvesting characteristics of cement/lead zirconate titanate (Pb(Zr0.52Ti0.48)O3; PZT) composites. A soft grade PZT (SP 5 A) with ∼1 µm sized particles in an unpoled state was utilised to prepare cement/PZT composites. The experiments were performed on 28 days’ water cured unpoled samples. The total cumulative volume and average pore radius are observed to decrease with PZT concentration in the composite. The values of piezoelectric coefficient and capacitance were, respectively, found to increase by 300 and 275% for cement/PZT composites consisting of 40% PZT compared to that of 5% added PZT. The impact generated voltage and energy increases significantly with increasing PZT-content. The voltage generation from cement/PZT could be because of the oscillations of dipoles and the mobility of ions in the pores of the hydrated cement. The results obtained foreshadow the prospect of utilising cement/PZT composites for energy harvesting from civil structures, structural health monitoring, and as sensors.

Monocular vision based 3D vibration displacement measurement for civil engineering structures

Vibration displacement of civil structures are crucial information for structural health monitoring (SHM). However, the challenges and costs associated with traditional physical sensors make displacement measurement difficult. In recent years computer vision (CV) techniques have been employed for measuring vibration displacement in civil structures. There has been a growing interest in CV-based three-dimensional (3D) displacement measurement, as it provides comprehensive information for structural health assessment. Most existing methods use multi-view geometry, requiring multiple cameras for depth measurement. This paper proposes a new system for measuring the 3D vibration displacement utilising a single camera. Instead of using multi-view geometry, deep neural networks are utilised to learn the depth of scenes from monocular images. Compared with the multi-view methods, the proposed 3D measurement system with monocular vision is more cost-effective and much more convenient to set up and use in practice, avoiding the complicated calibration and object matching between multiple cameras. Experimental tests are conducted in the laboratory to investigate the feasibility of the proposed system. Physical displacement sensors are equipped with the testing structure to provide the ground truth data. The results demonstrate that the proposed monocular 3D displacement system is able to produce reasonable 3D full-field displacement measurement, which makes monocular image based CV system a promising approach to achieve 3D displacement measurement, with its obvious advantages in cost and convenience compared to the traditional sensor-based or multi view CV-based methods.

Vision based defect detection technologies in civil structures: a review study

Vision based defect detection technologies in civil structures: a review study

The Social Role of Donation in the Structure of Islamic Civilization

:چکیده نقش اجتماعی وقف در ساختار تمدن اسلامی از موضوعات بسیار مهم در ایجاد تمدن انسانی و اسلامی بوده که همواره برای خیر و صلاح جامعه اسلامی نقش ارزنده ی را در تمدن اسلامی داشته و دین مبین اسلامی پیرامون آن تحت شرایط خاص تأکیدات زیادی نموده است. هدف اصلی و اساسی این پژوهش نهادینه ساختن فرهنگ وقف در جامعه بشری و خصوصا اسلامی است. بنابراین سوال اصلی این است که وقف چه نقشی در ساختار تمدن اسلامی دارد؟ و در کجا اموال وقفی سرمایه گزاری گردد تا تمدن اسلامی به شگوفایی برسد؟ این پژوهش به پاسخ این پرسش‌ها و پرسش‌های دیگر به‌طور علمی پرداخته و نقش اجتماعی وقف را به روش تحلیلی – توصیفی و کتاب خانه ای به بحث و بررسی گرفته و پشنهاد های راهبردی را برای بهینه سازی عملکرد وقفی در جامعه اسلامی ارائه نموده است یافته های این پژوهش برمی گردد به اینکه بیشتر اوقات به آثار و پیامدهای اقتصادی و آموزشی و... وقف نگریسته می شود در حالیکه این تحقیق به اثبات می رساند که آثار اجتماعی آن در تمدن اسلامی کمتر از پیامد های اقتصادی و آموزشی آن نبوده و نیست و همچنان این پژوهش این را اضافه مینماید که علاوه از راه های مصارف اموال و درآمدهای وقفی باید نظام منظمی از اموال وقفی ایجاد گردد و درآمد آن به راه های مختلف و مصالح اجتماعی مسلمانان به مصرف برسد. :واژه های کلیدی نقش اجتماعی، وقف، تمدن، ساختار، تمدن اسلامی

Collapse strength ratios for structures under mainshock-aftershock subduction seismic sequences

The effects of aftershocks on civil structures have called the attention of the earthquake engineering community mainly in the last two decades. However, neither seismic design codes nor seismic assessment guidelines for existing structures worldwide consider the possible detrimental effects of strong aftershocks nowadays. From this aim, this paper presents the results of a statistical study focused on computing the relative lateral strength to avoid collapse, named lateral strength ratio Rc, of degrading (i.e., having stiffness and in-cycle strength degradation) single-degree-of-freedom, SDOF, systems under mainshock-aftershock seismic sequences. For this purpose, the degrading SDOF systems have a trilinear force–displacement backbone mainly defined by their displacement ductility capacity, μc, and the negative slope after peak strength, αc. Therefore, median Rc ratios were computed for a site-specific mainshock-aftershock scenario at rock sites considering as-recorded and artificial seismic sequences. From the results of this investigation, it is shown that median Rc ratios that take into account strong aftershocks are smaller than those computed for only the mainshocks, which means that the yield strength coefficient, Cy, for structures subjected to mainshock-strong aftershock pairs should be greater than that for structures under only the mainshocks. The ordinates of median Rc ratios depend on the site class, period of vibration, T, as well as μc and αc. A predictive equation for obtaining estimates of period-dependent Rc ordinates under mainshock and mainshock-aftershock sequences at rock sites is introduced whose fitted coefficients depend on μc and αc. The predictive equation of Rc allows obtaining estimates of the yield strength coefficient required to avoid collapse, Cyc, under mainshock-aftershock sequences for the design of new structures.

Data-driven structural dynamic characteristics investigation by combining RDT and IVMD method

Data-driven structural monitoring results are often mixed with noise, resulting in the inability to identify the dynamic characteristics. In order to develop an efficient approach to correctly estimate the dynamic modal parameters of structures, a method combining random decrement technology (RDT) and improved variational mode decomposition (IVMD) was proposed. The approach focuses on obtaining the free decay response (FDR) with noise directly by RDT from the original monitoring signal rather than pre-denoising for the raw signal and then derives the noiseless FDR using IVMD to get the dynamic parameters with fewer data. First, the steps of the IVMD principle were established based on traditional VMD. Next, the performance of IVMD was verified. The simulated signal test shows that the IVMD addresses the shortcoming of the VMD algorithm and has better advantages in signal decomposition and reconstruction than complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). Finally, the frequencies and damping ratios of two large-scale civil structures (a super-high-rise building and a suspension bridge) in the field experiments were captured from the signal response data via RDT-IVMD, proving the reliability of the presented method. The results can provide a basis for structural preventive maintenance.

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.