Peer-reviewed journal articles covering structural engineering, computational mechanics, optimization, artificial intelligence, and related fields.
Research Article
J64. Explainable Ensemble Learning Framework for Estimating Corrosion Rate in Suspension Bridge Main Cables
Authors:
A. Jiménez Rios, M.E.A. Ben Seghier*, V. Plevris and J. Dai
Journal:
Results in Engineering
Publisher:
Elsevier
Volume: 23
Article ID:
102723
Year: 2024
(First online: 13 August 2024)
Ensuring the safe operation of suspension bridges is paramount to prevent unwanted events that can cause failures. Therefore, it is crucial to continuously monitor their operational status to uphold safety and reliability levels. However, natural deterioration caused by the surrounding environment, primarily due to corrosion, inevitably impacts these structures over time, particularly the main cables made of steel. In this study, a robust framework is proposed to predict the annual corrosion rate in main cables of suspension bridges, while investigating the impact of the surrounding environmental factors on this process. To do so, the implementation of four regression models and four machine learning techniques are used in the first phase for modeling the annual corrosion rate based on a comprehensive database containing various environmental factors. The modeling performance is evaluated through a range of statistical and graphical metrics. After that, Shapley Additive Explanations (SHAP) is utilized to explain the model and to extract the impact of each variable on the final modeling results. Overall, the findings demonstrate the effectiveness of the proposed framework for addressing this issue. The Extreme Gradient Boosting (XGB) emerged as the top-performing model, achieving an overall R2 of 0.982. Moreover, the SHAP findings highlight the impact of CL− on the annual corrosion rate as the factor with the highest influence during the modeling process. The high performance of the proposed model suggests its potential utility in further research concerning the reliability of suspension bridge main cables.
Study Protocol
J63. Industry 5.0 concepts and enabling technologies, towards an enhanced conservation practice: systematic literature review protocol
Authors:
A. Jiménez Rios*, M.L. Petrou, R. Ramirez, V. Plevris and M. Nogal
Journal:
Open Research Europe
Volume: 4
Article ID:
75
Year: 2024
(First online: 18 April 2024)
Industry 4.0 has led to digitalization and an increase in industrial activity. However, it has recently been recognized as inadequate for achieving European goals by 2030. Therefore, a novel Industry 5.0 paradigm has emerged in response to the unexpected negative effects caused by its predecessor. Industry 5.0 is mainly based on three foundational ideas: i) human-centrism, ii) resilience, and iii) sustainability. Human-centric solutions and human-machine-interaction; bio-inspired technologies and smart materials; real time-based digital twins and simulation; cyber safe data transmission, storage, and analysis; artificial intelligence; and energy efficiency and trustworthy autonomy have been recognized as the enabling technologies of this transformative vision. This paper outlines the protocol adopted to conduct a systematic literature review with the aim of exploring how the Architecture, Engineering, Construction, Management, Operation, and Conservation (AECMO&C) industry can adapt and be better prepared to embrace novel Industry 5.0 principles and enabling technologies, ultimately resulting in enhanced conservation practices for the built cultural heritage environment.
Research Article
J62. Multi-Directional Functionally Graded Sandwich Plates: Buckling and Free Vibration Analysis with Refined Plate Models under Various Boundary Conditions
Authors:
L. Hadji*, V. Plevris, R. Madan and H. Ait Atmane
Journal:
Computation
Publisher:
MDPI
Volume: 12, Issue: 4
Article ID:
65
Page count:
22
Year: 2024
(First online: 27 March 2024)
This study conducts buckling and free vibration analyses of multi-directional functionally graded sandwich plates subjected to various boundary conditions. Two scenarios are considered: a functionally graded (FG) skin with a homogeneous hard core, and an FG skin with a homogeneous soft core. Utilizing refined plate models, which incorporate a parabolic distribution of transverse shear stresses while ensuring zero shear stresses on both the upper and lower surfaces, equations of motion are derived using Hamilton’s principle. Analytical solutions for the buckling and free vibration analyses of multi-directional FG sandwich plates under diverse boundary conditions are developed and presented. The obtained results are validated against the existing literature for both the buckling and free vibration analyses. The composition of metal–ceramic-based FG materials varies longitudinally and transversely, following a power law. Various types of sandwich plates are considered, accounting for plate symmetry and layer thicknesses. This investigation explores the influence of several parameters on buckling and free vibration behaviors.
Editorial
J61. Advanced Concrete and Construction Materials for Sustainable Structures
Authors:
M. Kioumarsi and V. Plevris*
Journal:
Sustainability
Publisher:
MDPI
Volume: 16, Issue: 4
Article ID:
1427
Page count:
5
Year: 2024
(First online: 08 February 2024)
This editorial introduces the Special Issue “Advanced Concrete and Construction Materials”, published in the Sustainable Materials section of Sustainability (MDPI). Guest-edited by Mahdi Kioumarsi and Vagelis Plevris, the collection brings together ten contributions addressing recent advances in concrete technology and sustainable construction materials. The Special Issue explores innovative approaches to improving the mechanical performance, durability, and environmental sustainability of cementitious materials and construction products. Its scope encompasses alternative binders, alkali-activated concrete, recycled and waste-derived materials, advanced cementitious composites, durability assessment, and numerical modeling of concrete structures. The editorial provides an overview of the contributions, emphasizing the importance of reducing the environmental impact of construction while enhancing structural performance, service life, and resilience. It highlights the potential of innovative materials and technologies to support more sustainable construction practices and encourages continued research toward a durable, resource-efficient, and environmentally responsible built environment.
Research Article
J60. OpenSeismoMatlab: New Features, Verification and Charting Future Endeavors
Authors:
G. Papazafeiropoulos and V. Plevris*
Journal:
Buildings
Publisher:
MDPI
Volume: 14, Issue: 1
Article ID:
304
Page count:
31
Year: 2024
(First online: 22 January 2024)
To facilitate the precise design of earthquake-resistant structures, it is imperative to accurately evaluate the impact of seismic events on these constructions and predict their responses. OpenSeismoMatlab, a robust, free ground motion data processing software, plays a pivotal role in this endeavor. It empowers users to compute a wide array of outcomes using input acceleration time histories, encompassing time histories themselves, as well as linear and nonlinear spectra. These capabilities are instrumental in supporting structural design initiatives. This study provides a comprehensive exposition of the latest version (v 5.05) of OpenSeismoMatlab. It delves into intricate facets of the software, encompassing a detailed exploration of the input and output variables integral to each operational category. Comprehensive calculation flowcharts are presented to elucidate the software’s organizational structure and operational sequences. Furthermore, a meticulous verification assessment is conducted to validate OpenSeismoMatlab’s performance. This verification entails a rigorous examination of specific cases drawn from existing literature, wherein the software’s outcomes are rigorously compared against corresponding results from prior studies. The examination not only underscores the reliability of OpenSeismoMatlab but also emphasizes its ability to generate outcomes that closely align with findings documented in the established body of literature. Concluding the study, the paper outlines potential directions for future research, shedding light on avenues where further development and exploration can enhance the utility and scope of OpenSeismoMatlab in advancing seismic engineering and structural design practices.
Research Article
J59. Explainable Machine Learning-Aided Efficient Prediction Model and Software Tool for Bond Strength of Concrete with Corroded Reinforcement
Authors:
T.G. Wakjira, A. Abushanab, M. Shahria Alam, W. Alnahhal* and V. Plevris
Journal:
Structures
Publisher:
Elsevier
Volume: 59
Article ID:
105693
Year: 2024
(First online: 18 December 2023)
The bond strength between concrete and reinforcement is crucial for the composite action and serviceability of reinforced concrete (RC) structures. However, it is vulnerable to deterioration from the corrosion of reinforcement bars, especially in marine structures. Thus, a precise and reliable model for the bond strength in corrosive environments is necessary to evaluate the serviceability and structural performance of corroded RC members. This study employs explainable machine learning (ML) techniques to assess the bond strength between concrete and corroded bars. Eight ML models are developed to establish the best predictive model for bond behavior, considering seven input parameters: corrosion level (CL), steel yield strength, compressive strength of concrete, concrete cover-to-bar diameter ratio, bar diameter-to-bonded length ratio, reinforcement type, and test type. The super learner (SL) model, integrating three ML models, outperforms other models and analytical methods with a large R2 value (98% on the test set) and minimal statistical errors. The SHapley Additive exPlanation (SHAP) technique identifies CL as the most influential parameter on bond strength, while the reinforcement and test types have the least effect. Finally, a user-friendly graphical user interface (GUI) tool is established to facilitate the practical implementation of the developed model and support accurate bond strength prediction in concrete with steel reinforcement under corrosive environments.
Research Article
J58. Computing the Characteristics of Defects in Wooden Structures using Image Processing and CNN
Authors:
R. Ehtisham, W. Qayyum, C.V. Camp, V. Plevris*, J. Mir, Q.Z. Khan and A. Ahmad
Journal:
Automation in Construction
Publisher:
Elsevier
Volume: 158
Article ID:
105211
Year: 2024
(First online: 28 November 2023)
Wood, a time-honored construction material prized for its exceptional properties, has been in use for millennia. Its enduring popularity is attributed to its remarkable strength, aesthetic appeal, and favorable environmental footprint. However, wooden structures are susceptible to various defects and imperfections that pose threats to their structural integrity, durability, and safety. These issues encompass knots, cracks, warping, twisting, decay, insect infestations, and more, all of which, if left unaddressed, can culminate in structural failures. Thus, a comprehensive strategy involving inspection, maintenance, and remediation is indispensable for safeguarding wooden structures. Traditional manual inspections, while effective, are characterized by their resource-intensive nature, entailing significant time and cost investments. This study presents a pioneering approach that leverages Convolutional Neural Networks (CNNs) and Image Processing techniques to revolutionize the assessment of damage in wooden structures using digital imagery. Initially, CNNs are employed to categorize images into three fundamental classes: cracks, knots, and undamaged sections. Subsequently, Image Processing techniques are harnessed to compute precise characteristics of these defects, including parameters such as crack length, width, angle, and the extent of the defective area within knots. The Inception-ResNet-V2 pre-trained model is utilized, fine-tuned and validated with a robust dataset comprising 9000 wooden defect images, evenly distributed across the three aforementioned categories. A prudent division allocates 70% of the dataset for model training, with the remaining 30% reserved for validation. Following successful training, the model demonstrates an impressive overall accuracy of 92% when classifying an independent test set comprising 100 new images. To illustrate the model's performance, two images from each damage category are selected and tested to compute the characteristics of the defects. The quantification error for crack angle is only 0.15%, while it is 0.99% for crack length, and 2% for crack width, demonstrating the high performance of the model. The practical implications of this work are profound. By automating defect assessment in wooden structures, our approach offers significant advantages to industry professionals. It expedites inspections, reduces labor costs, and enhances the accuracy of defect quantification.
Research Article
J57. Chatbots put to the test in math and logic problems: A comparison and assessment of ChatGPT-3.5, ChatGPT-4, and Google Bard
Authors:
V. Plevris, G. Papazafeiropoulos and A. Jiménez Rios*
Journal:
AI
Publisher:
MDPI
Volume: 4, Issue: 4
Pages:
949–969
Year: 2023
(First online: 24 October 2023)
In an age where artificial intelligence is reshaping the landscape of education and problem solving, our study unveils the secrets behind three digital wizards, ChatGPT-3.5, ChatGPT-4, and Google Bard, as they engage in a thrilling showdown of mathematical and logical prowess. We assess the ability of the chatbots to understand the given problem, employ appropriate algorithms or methods to solve it, and generate coherent responses with correct answers. We conducted our study using a set of 30 questions. These questions were carefully crafted to be clear, unambiguous, and fully described using plain text only. Each question has a unique and well-defined correct answer. The questions were divided into two sets of 15: Set A consists of “Original” problems that cannot be found online, while Set B includes “Published” problems that are readily available online, often with their solutions. Each question was presented to each chatbot three times in May 2023. We recorded and analyzed their responses, highlighting their strengths and weaknesses. Our findings indicate that chatbots can provide accurate solutions for straightforward arithmetic, algebraic expressions, and basic logic puzzles, although they may not be consistently accurate in every attempt. However, for more complex mathematical problems or advanced logic tasks, the chatbots’ answers, although they appear convincing, may not be reliable. Furthermore, consistency is a concern as chatbots often provide conflicting answers when presented with the same question multiple times. To evaluate and compare the performance of the three chatbots, we conducted a quantitative analysis by scoring their final answers based on correctness. Our results show that ChatGPT-4 performs better than ChatGPT-3.5 in both sets of questions. Bard ranks third in the original questions of Set A, trailing behind the other two chatbots. However, Bard achieves the best performance, taking first place in the published questions of Set B. This is likely due to Bard’s direct access to the internet, unlike the ChatGPT chatbots, which, due to their designs, do not have external communication capabilities.
Research Article
J56. Classification of defects in wooden structures using pre-trained models of convolutional neural network
Authors:
R. Ehtisham, W. Qayyum, C.V. Camp, V. Plevris*, J. Mir, Q.Z. Khan and A. Ahmad
Journal:
Case Studies in Construction Materials
Publisher:
Elsevier
Volume: 19
Article ID:
e02530
Year: 2023
(First online: 04 October 2023)
Wooden structures, over time, are challenged by different types of defects. Due to mechanical and weathering effects, these defects can occur in the form of cracks, live and dead knots, dampness, and others. Because of the risk of damage or complete failure, treatment of these defects is necessary, but doing so necessitates their proper identification and classification (categorization). Crack identification and categorization must be part of the inspection procedure for engineering structures in the built environment. Convolutional neural networks (CNNs), a sub-type of Deep Learning (DL), can automatically classify the images of wooden structures to identify such defects. In this study, ten pre-trained models of CNN, namely ResNet18, ResNet50, ResNet101, ShuffleNet, GoogLeNet, Inception-V3, MobileNet-V2, Xception, Inception-ResNet-V2, and NASNet-Mobile are evaluated for the tasks of classification and prediction of defects in wooden structures. Each pre-trained CNN model is additionally trained and validated on an image dataset of 9000 images, equally divided into three classes: cracks, knots, and intact (undamaged). A smaller dataset of 300 images is separately used for testing purposes. Statistical parameters such as accuracy, precision, recall, and F1-score are computed for each CNN model. The Inception-V3 model proved to be the best CNN model for classifying defects in wooden structures based on the model’s accuracy, processing time and overall performance.
Research Article
J55. Minimum Design Bending Moment for Systems of Equivalent Stiffness
Authors:
A. de Macêdo Wahrhaftig, V. Plevris, B.A. Mohamad and D. Lopes Pereira
Journal:
Structures
Publisher:
Elsevier
Volume: 57
Article ID:
105224
Year: 2023
(First online: 16 September 2023)
In this article, the minimum design bending moment of concrete slender columns is studied by assuming a system of equivalent stiffness. For concrete structural parts such as slender columns, their stiffness is dependent on the loading, originated from lumped and distributed masses, and the rheological behavior of the material. The latter alters the concrete’s modulus of elasticity, introducing changes over time. Basically, the desired transformation is from a one-dimensional non-prismatic system to another prismatic one which exhibits an equivalent bending stiffness. As the bending stiffness changes due to the change of the problem’s independent variables, the geometric characteristics of the transformed system reflect the same dependence as the original system. This implies changes in the minimum design moment since it is linked to the dimensions of the equivalent section. To assess the hypothesis proposed, a numerical simulation is conducted over a real structural system using a vertical loading ranging from zero up to the critical buckling force, taking into account the change in the modulus of deformation of concrete and assuming a certain level of cracking of the material. The results obtained showed that the strategy of using a system of equivalent stiffness simplifies the analysis of non-prismatic elements because the problem is reduced to a prismatic element of equivalent properties. Besides that, due to the incorporation of the concrete creep in the problem, the maximum moment obtained in the equivalent system needs to be multiplied by a factor of 2.94 in order to equal the maximum moment occurring in the original system.
Research Article
J54. Investigation of the Static Bending Response of FGM Sandwich Plates
Authors:
L. Hadji*, V. Plevris and G. Papazafeiropoulos
Journal:
Journal of Applied and Computational Mechanics
Publisher:
Shahid Chamran University of Ahvaz
Volume: 10, Issue: 1
Pages:
26–37
Year: 2024
(First online: 13 August 2023)
In the present work, a displacement-based high-order shear deformation theory is introduced for the static response of functionally graded plates. The present theory is variationally consistent and strongly similar to the classical plate theory in many aspects. It does not require the shear correction factor, and gives rise to the transverse shear stress variation so that the transverse shear stresses vary parabolically across the thickness to satisfy free surface conditions for the shear stress. By dividing the transverse displacement into the bending and shear parts and making further assumptions, the number of unknowns and equations of motion of the present theory is reduced a and hence makes them simple to use. The material properties of the plate are assumed to be graded in the thickness direction according to a simple power-law distribution in terms of volume fractions of material constituents. The equilibrium equations of a functionally graded plate are given based on the higher order shear deformation theory. The numerical results presented in the paper are demonstrated by comparing the results with solutions derived from other higher-order models found in the literature and the present numerical results of Finite Element Analysis (FEA). In the numerical results, the effects of the grading materials, lay-up scheme and aspect ratio on the normal stress, shear stress and static deflections of the functionally graded sandwich plates are presented and discussed. It can be concluded that the proposed theory is accurate, elegant and simple in solving the problem of the bending behavior of functionally graded plates.
Research Article
J53. An Open-Source Framework for Modeling RC Shear Walls Using Deep Neural Networks
Authors:
G. Solorzano and V. Plevris*
Journal:
Advances in Civil Engineering
Publisher:
Wiley
Volume: 2023
Article ID:
7953869
Page count:
17
Year: 2023
(First online: 11 July 2023)
Reinforced concrete (RC) shear walls macroscopic models are simplified strategies able to simulate the complex nonlinear behavior of RC shear walls to some extent, but their efficacy and robustness are limited. In contrast, microscopic models are sophisticated finite element method (FEM) models that are far more accurate and reliable. However, their elevated computational cost turns them unfeasible for most practical applications. In this study, a data-driven surrogate model for analyzing RC shear walls is developed using deep neural networks (DNNs). The surrogate model is trained with thousands of FEM simulations to predict the characteristic curve obtained when a static nonlinear pushover analysis is performed. The surrogate model is extensively tested and found to exhibit a high degree of accuracy in its predictions while being extremely faster than the detailed FEM analysis. The complete framework that made this study possible is provided as an open-source project. The project is developed in Python and includes a parametric FEM model of an RC shear wall in OpenSeesPy, the training and validation of the DNN model in TensorFlow, and an application with an interactive graphical user interface to test the methodology and visualize the results.
Research Article
J52. Performance-based seismic assessment of a historical masonry arch bridge: Effect of pulse-like excitations
Authors:
A. Shabani, M. Kioumarsi* and V. Plevris
Journal:
Frontiers of Structural and Civil Engineering
Publisher:
Springer Nature
Volume: 17, Issue: 6
Pages:
855–869
Year: 2023
(First online: 04 July 2023)
Seismic analysis of historical masonry bridges is important for authorities in all countries hosting such cultural heritage assets. The masonry arch bridge investigated in this study was built during the Roman period and is on the island of Rhodes, in Greece. Fifteen seismic records were considered and categorized as far-field, pulse-like near-field, and non-pulse-like near-field. The earthquake excitations were scaled to a target spectrum, and nonlinear time-history analyses were performed in the transverse direction. The performance levels were introduced based on the pushover curve, and the post-earthquake damage state of the bridge was examined. According to the results, pulse-like near-field events are more damaging than non-pulse-like near-field ground motions. Additionally the bridge is more vulnerable to far-field excitations than near-field events. Furthermore, the structure will suffer extensive post-earthquake damage and must be retrofitted.
Research Article
J51. Response Spectrum Analysis of Multi-Story Shear Buildings Using Machine Learning Techniques
Authors:
M. Georgioudakis* and V. Plevris
Journal:
Computation
Publisher:
MDPI
Volume: 11, Issue: 7
Article ID:
126
Page count:
22
Year: 2023
(First online: 29 June 2023)
The dynamic analysis of structures is a computationally intensive procedure that must be considered, in order to make accurate seismic performance assessments in civil and structural engineering applications. To avoid these computationally demanding tasks, simplified methods are often used by engineers in practice, to estimate the behavior of complex structures under dynamic loading. This paper presents an assessment of several machine learning (ML) algorithms, with different characteristics, that aim to predict the dynamic analysis response of multi-story buildings. Large datasets of dynamic response analyses results were generated through standard sampling methods and conventional response spectrum modal analysis procedures. In an effort to obtain the best algorithm performance, an extensive hyper-parameter search was elaborated, followed by the corresponding feature importance. The ML model which exhibited the best performance was deployed in a web application, with the aim of providing predictions of the dynamic responses of multi-story buildings, according to their characteristics.
Mini Review
J50. Computational Methods Applied to Earthen Historical Structures
Authors:
A. Jiménez Rios*, S. Ruiz-Capel, V. Plevris and M. Nogal
Journal:
Frontiers in Built Environment
Publisher:
Frontiers
Volume: 9
Article ID:
1219108
Year: 2023
(First online: 15 June 2023)
Earthen structures have an important representation among the UNESCO World Heritage List sites as well as among the built environment in general. Unfortunately, earthen heritage structures are also numerous within the UNESCO List of World Heritage in Danger whereas other existing common earthen structures are extremely vulnerable to seismic and climate change events. Within the field of heritage conservation, structural analysis contributes to the safety evaluation of the structure, the diagnosis of the causes of damage and decay, and to the validation of interventions. Thus, the need to develop effective and accurate computational methods suitable for the study of both monumental and vernacular earthen structures becomes evident. This paper compiles, summarizes, and highlights the latest developments and implementations of computational methods for the study of such structure typologies. The literature has been explored following the PRISMA-S checklist methodology and a narrative synthesis was used for the presentation of results. Finally, future trends, opportunities, and challenges are discussed.
Research Article
J49. Seismic response modification, over-strength, and displacement amplification factors of steel special truss moment frames
Authors:
G. Nekouei, A. Vatani Oskouei, S. Gharehbaghi and V. Plevris*
Journal:
Asian Journal of Civil Engineering
Publisher:
Springer Nature
Volume: 24, Issue: 7
Pages:
2531–2548
Year: 2023
(First online: 26 May 2023)
In this paper, the seismic design parameters of steel special truss moment frames (STMFs), including the response modification factor (R), over-strength factor (Ω) and displacement modification factor (Cd) are evaluated for two performance levels, namely life safety (LS) and collapse prevention (CP). The effects of geometrical dimensions of the special segment located at the middle part of truss girder and the number of stories are investigated. Twelve steel STMFs with 3, 5, 7, and 9 stories with three bays are considered to evaluate the parameters. In addition, three special segment lengths are considered for each STMF. The truss-girder members are made of hollow structural sections (HSSs) and W-sections are used for columns, which are designed based on ASCE7-16 recommendations. The results show that the number of stories and the different special segment lengths affect the seismic parameters significantly. Moreover, the values obtained for R, Ω and Cd show some differences compared to the ones recommended by ASCE7-16.
Research Article
J48. DNN-MLVEM: A Data-Driven Macromodel for RC Shear Walls Based on Deep Neural Networks
Authors:
G. Solorzano* and V. Plevris
Journal:
Mathematics
Publisher:
MDPI
Volume: 11, Issue: 10
Article ID:
2347
Page count:
19
Year: 2023
(First online: 18 May 2023)
This study proposes the DNN-MVLEM, a novel macromodel for the non-linear analysis of RC shear walls based on deep neural networks (DNN); while most RC shear wall macromodeling techniques follow a deterministic approach to find the right configuration and properties of the system, in this study, an alternative data-driven strategy is proposed instead. The proposed DNNMVLEM is composed of four vertical beam-column elements and one horizontal shear spring. The beam-column elements implement the fiber section formulation with standard non-linear uniaxial material models for concrete and steel, while the horizontal shear spring uses a multi-linear force–displacement relationship. Additionally, three calibration factors are introduced to improve the performance of the macromodel. The data-driven component of the proposed strategy consists of a large DNN that is trained to predict the force–displacement curve of the shear spring and the three calibration factors. The training data is created using a parametric microscopic FEM model based on the multi-layer shell element formulation and a genetic algorithm (GA) that optimizes the response of the macromodel to match the behavior of the microscopic FEM model. The DNN-MVLEM is tested in two types of examples, first as a stand-alone model and then as part of a two-bay multi-story frame structure. The results show that the DNN-MVLEM is capable of reproducing the results obtained with the microscopic FEM model up to 100 times faster and with an estimated error lower than 5%.
Research Article
J47. Kahramanmaraş—Gaziantep, Türkiye Mw 7.8 Earthquake on 6 February 2023: Strong Ground Motion and Building Response Estimations
Authors:
G. Papazafeiropoulos and V. Plevris*
Journal:
Buildings
Publisher:
MDPI
Volume: 13, Issue: 5
Article ID:
1194
Page count:
30
Year: 2023
(First online: 30 April 2023)
The effects on structures of the earthquake with the magnitude of 7.8 on the Richter scale (moment magnitude scale) that took place in Pazarcık, Kahramanmaraş, Türkiye at 04:17 a.m. local time (01:17 UTC) on 6 February 2023, are investigated by processing suitable seismic records using the open-source software OpenSeismoMatlab. The earthquake had a maximum Mercalli intensity of XI (Extreme) and it was followed by a Mw 7.5 earthquake nine hours later, centered 95 km to the north–northeast from the first. Peak and cumulative seismic measures as well as elastic response spectra, constant ductility (or isoductile) response spectra, and incremental dynamic analysis curves were calculated for two representative earthquake records of the main event. Furthermore, the acceleration response spectra of a large set of records were compared to the acceleration design spectrum of the Turkish seismic code. Based on the study, it is concluded that the structures were overloaded far beyond their normal design levels. This, in combination with considerable vertical seismic components, was a contributing factor towards the collapse of many buildings in the region. Modifications of the Turkish seismic code are required so that higher spectral acceleration values can be prescribed, especially in earthquake-prone regions.
Review Article
J46. Bridge management through digital twin-based anomaly detection systems: A systematic review
Authors:
A. Jiménez Rios*, V. Plevris and M. Nogal
Journal:
Frontiers in Built Environment
Publisher:
Frontiers
Volume: 9
Article ID:
1176621
Year: 2023
(First online: 07 April 2023)
Bridge infrastructure has great economic, social, and cultural value. Nevertheless, many of the infrastructural assets are in poor conservation condition as has been recently evidenced by the collapse of several bridges worldwide. The objective of this systematic review is to collect and synthesize state-of-the-art knowledge and information about how bridge information modeling, finite element modeling, and bridge health monitoring are combined and used in the creation of digital twins (DT) of bridges, and how these models could generate damage scenarios to be used by anomaly detection algorithms for damage detection on bridges, especially in bridges with cultural heritage value. A total of 76 relevant studies from 2017 up to 2022 have been taken into account in this review. The synthesis results show a consensus toward the future adoption of DT for bridge design, management, and operation among the scientific community and bridge practitioners. The main gaps identified are related to the lack of software interoperability, the required improvement of the performance of anomaly-detection algorithms, and the approach definition to be adopted for the integration of DT at the macro scale. Other potential developments are related to the implementation of Industry 5.0 concepts and ideas within DT frameworks.
Research Article
J45. Development of Hybrid Adaptive Neural Fuzzy Inference System-Based Evolutionary Algorithms for Flexural Capacity Prediction in Corroded Steel Reinforced Concrete Beam
Authors:
M.E.A. Ben Seghier*, V. Plevris and A. Malekjafarian
Journal:
Arabian Journal for Science and Engineering
Publisher:
Springer Nature
Volume: 48, Issue: 10
Pages:
13147–13163
Year: 2023
(First online: 02 March 2023)
The damages in reinforced concrete (RC) beams due to reinforcement corrosion is a major problem in the RC industry. Accurate prediction of the residual bearing capacity of RC beams can effectively prevent structural failures or unwanted over-costs of inspections and rehabilitations. This paper proposes a novel machine learning-based prediction framework that combines the adaptive neural fuzzy inference system (ANFIS) with several metaheuristic algorithms for the effective estimation of the flexural strength capacity. Five optimization algorithms are employed for auto-selection of the optimum ANFIS parameters, including differential evolution (DE), genetic algorithm, particle swarm optimization, artificial bee colony, and firefly algorithm (FFA). A comprehensive experimental database of the flexural capacity of corroded steel reinforced concrete beams obtained from the literature, consisting of 177 tests, is used as a case study to evaluate the prediction performance of the proposed hybrid models. The results demonstrate that the proposed hybrid models transcend the previously developed models, while the optimized ANFIS using FFA represents the highest accuracy and strong stability among the proposed models. It is concluded that the proposed framework using ANFIS-FFA can be effectively employed as a useful tool for the accurate estimation of the flexural strength capacity of corroded reinforced concrete beams.