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S.V.R. Tosee, I. Faridmehr, M.L. Nehdi, V. Plevris and K.A. Valerievich, "Predicting Crack Width in CFRP Strengthened RC One-Way Slabs Using Hybrid Grey Wolves Optimizer Neural Network Model", Buildings, 12(11), Article ID 1870, 26 pages (DOI: 10.3390/buildings12111870), 2022.

Abstract:
This study deploys a hybrid Grey Wolf Optimizer Neural Network Model for predicting the crack width in reinforced concrete slabs strengthened with carbon fiber-reinforced polymers (CFRP). Reinforced concrete (RC) one-way slabs (1800 × 400 × 120 mm in size) were strengthened with CFRP with various lengths (1800, 1100, and 700 mm) and subjected to four-point bending. The experimental results were compared to corresponding values for conventional RC slabs. The observed crack width results were recorded, and subsequently examined against the expression recommended by Eurocode 2. To estimate the crack width of CFRP-reinforced slabs, ANN combined with the Grey Wolf Optimizer algorithm was employed whereby the applied load, CFRP width/length, X/Y crack positions, and stress in steel reinforcement and concrete were defined as the input parameters. Experimental results showed that the larger the length and width of the carbon fiber, the smaller the maximum crack width in the tensile area of the slab at the final load step. On average, the crack width in slabs retrofitted with CFRP laminates increased by around 80% compared to a slab without CFRP. The results confirm that the equation provided by Eurocode 2 provides an unconservative estimation of crack widths for RC slabs strengthened with CFRP laminates. On the other hand, the results also confirm that the proposed informational model could be used as a reliable tool for estimating the crack width in RC slabs. The findings provide valuable insight into the design approaches for RC slabs and rehabilitation strategies for existing deficient RC slabs using CFRP.

Keywords:
crack width; CFRP; artificial intelligence; neural networks; concrete slab.

 

M.E.A. Ben Seghier, V. Plevris and Α. Malekjafarian, “Development of Hybrid Adaptive Neural Fuzzy Inference System-Based Evolutionary Algorithms for Flexural Capacity Prediction in Corroded Steel Reinforced Concrete Beam”, Arabian Journal for Science and Engineering (DOI: 10.1007/s13369-023-07708-w), 2023.

Abstract:
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.



Keywords:
Flexural strength capacity; Prediction; Machine learning; Adaptive neural fuzzy inference system; Nature-inspired algorithms; Firefly algorithm.

 

A. Jimenez Rios, V. Plevris and M. Nogal, “Bridge management through digital twin-based anomaly detection systems: A systematic review”, Frontiers in Built Environment, 9:1176621 (DOI: 10.3389/fbuil.2023.1176621), 2023.

Abstract:
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.



Keywords:
Bridges, digital twins, anomaly detection algorithms, finite element method, cultural heritage conservation, bridge information modeling, bridge health monitoring.

 

G. Papazafeiropoulos and V. Plevris, "Kahramanmaraş—Gaziantep, Türkiye Mw 7.8 Earthquake on 6 February 2023: Strong Ground Motion and Building Response Estimations", Buildings, 13(5), Article ID 1194, 30 pages (DOI: 10.3390/buildings13051194), 2023.

Abstract:
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.

Keywords:
earthquake; Türkiye; design; collapse; ductility; reinforcement; concrete.

 

G. Solorzano* and V. Plevris, "DNN-MLVEM: A Data-Driven Macromodel for RC Shear Walls Based on Deep Neural Networks", Mathematics, 11(10), Article ID 2347, 19 pages (DOI: 10.3390/math11102347), 2023.

Abstract:
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%.

Keywords:
shear wall, macromodel, deep neural network, genetic algorithm, OpenSees.

 

 

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