• Structural Engineering and Mechanics
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• v.26 no.3
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• pp.251-262
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• 2007

This paper examines the application of artificial neural networks (ANN) to the response prediction of geometrically nonlinear truss structures. Two types of analysis (deterministic and probabilistic analyses) are considered. A three-layer feed-forward backpropagation network with three input nodes, five hidden layer nodes and two output nodes is firstly developed for the deterministic response analysis. Then a back propagation training algorithm with Bayesian regularization is used to train the network. The trained network is then successfully combined with a direct Monte Carlo Simulation (MCS) to perform a probabilistic response analysis of geometrically nonlinear truss structures. Finally, the proposed ANN is applied to predict the response of a geometrically nonlinear truss structure. It is found that the proposed ANN is very efficient and reasonable in predicting the response of geometrically nonlinear truss structures.

• Computers and Concrete
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• v.31 no.4
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• pp.315-325
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• 2023

Calcium leaching is one of the main deterioration factors in concrete structures contact with water, such as dams, water treatment structures, and radioactive waste structures. It causes a porous microstructure and may be coupled with various harmful factors resulting in mechanical degradation of concrete. Several numerical modeling studies focused on the calcium leaching depth prediction. However, these required a lot of cost and time for many experiments and analyses. This study presents an artificial neural network (ANN) approach to predict the leaching depth quickly and accurately. Totally 132 experimental data are collected for model training and validation. An optimal ANN model was proposed by ANN topology. Results indicate that the model can be applied to estimate the calcium leaching depth, showing the determination coefficient of 0.91. It might be used as a simulation tool for engineering problems focused on durability.

• Structural Monitoring and Maintenance
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• v.9 no.3
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• pp.221-235
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• 2022

Among nondestructive damage detection methods, impedance-based methods have been recognized as an effective technique for damage identification in many kinds of structures. This paper proposes a method to detect cracks in metal structures by combining electro-mechanical impedance (EMI) responses and artificial neural networks (ANN). Firstly, the theories of EMI responses and impedance-based damage detection methods are described. Secondly, the reliability of numerical simulations for impedance responses is demonstrated by comparing to pre-published results for an aluminum beam. Thirdly, the proposed method is used to detect cracks in the beam. The RMSD (root mean square deviation) index is used to alarm the occurrence of the cracks, and the multi-layer perceptron (MLP) ANN is employed to identify the location and size of the cracks. The selection of the effective frequency range is also investigated. The analysis results reveal that the proposed method accurately detects the cracks' occurrence, location, and size in metal structures.

• Journal of Korean Association for Spatial Structures
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• v.12 no.1
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• pp.14-19
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• 2012

• Journal of the Korean Institute of Landscape Architecture International Edition
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• no.2
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• pp.109-113
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• 2004

It is important to consider urban parks and greenery not only from the viewpoint of amenity or aesthetics but also from the viewpoint of biodiversity. In this study, we focused on bryophytes (mosses), and analyzed how existence of artificial structures in urban greeney, such as concrete curbs and stone walls, affect species diversity of bryophytes. Kyoto Gyoen in Kyoto City, western Japan, was selected as the study site. In consideration of kinds of substrates on which bryophytes grow, microhabitats of Kyoto Gyoen were divided into ten types including concrete curbs and stone walls. In each type of microhabitats, we selected the area where bryophyte diversity was highest, and established a quadrat for bryophyte flora survey. Our results showed that the number of bryophyte species and growth forms and the value of diversity indices on concrete curbs or stone walls were higher than the averages of those. The bryophyte species were divided into the four groups by TWINSPAN as follows: Group A (epiphyte species), Group B (rocky species), Group C (roadsides, grassland or forest species), and Group D (waterside species). Bryophytes classified into Group B (rocky species) were mainly recorded on concrete curbs or stone walls. It was considered that the existence of artificial structures (concrete curbs and stone walls) provided favorite habitats for the bryophytes classified into Group B (rocky species), which mainly grows on concrete or rocks, and enhanced species diversity of bryophytes in Kyoto Gyoen.

• Journal of Ocean Engineering and Technology
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• v.24 no.2
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• pp.18-24
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• 2010

Submerged rubble structures include artificial reef and the mound part of the rubble mound breakwater. Artificial reef is a type of the submerged wave absorbing structure installed in a coastal zone to prevent beach erosion and designed to initially reduce the energy of incoming waves so that its run-up height and overtopping quantity can be decreased. In order to ascertain the stability of such submerged rubble structures, minimum weight of the rubble has to be calculated first from the incoming wave height using Hudson's formula or Brebner-Donnelly formula. Based on the calculated minimum weight, a model is built for use in a hydraulic model test carried out to check its stability. The foregoing two formulas used to calculate the minimum weight are empirically derived formulas based on the result of the tests on the rubble mound breakwater and it is, therefore, difficult for us to apply them directly in the calculation of the minimum weight of the submerged structures. Accordingly, this study comes up with a numerical simulation method capable of deformation analysis for rubble structures. This study also tries to identify the deformation mechanism of the submerged rubble structures using the numerical simulation. The method researched through this study will be sufficient for use for usual preparations of the design guidelines for submerged rubble structures.

• Journal of Korean Association for Spatial Structures
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• v.7 no.5
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• pp.67-74
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• 2007

The unsymmetrically layered artificial material model is consistently introduced to find the optimum topologies of the plate structures. Reissner-Mindlin (RM) plate theory is adopted to formulate the present 9-node plate element considering the first-order shear deformation of the plates. In the topology optimization process, the strain energy to be minimized is employed as the objective function and the initial volume of structures is adopted as the constraint function. In addition, the resizing algorithm based on the optimality criteria is used to update the hole size introduced in the proposed artificial material model. Several numerical examples are rallied out to investigate the performance of the proposed technique. From numerical results, the proposed topology optimization techniques are found to be very effective to produce the optimum topology of plate structures. In particular, the proposed unsymmetric stiffening layer model make it possible to produce more realistic stiffener design of the plate structures.

• Journal of Environmental Science International
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• v.22 no.3
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• pp.309-318
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• 2013

This study was conducted for determining the influence of artificial structures on benthic macroinvertebrate communities in stream. Sampling was taken at upper(pool), down(riffle) and control(riffle) from two check dams, two weirs, one agricultural reservoir, and one multipurpose dam in northern part of Gyeongsangbuk-do. The benthic macroinvertebrate communities of these structures were surveyed during 2009 to 2011. The simple habitat of benthic macroinvertebrates occurred at the upper sites due to pooling effects from artificial structures. Specifically, Check dam1, Jusanji, Imha dam showed very low biological attribute values compared to the down and control sites, which have greater difference in substrate characteristics. However, in the upper sites of Check dam2, Weir1 and Weir2, the difference of values of biological attributes was relatively smaller. Also, proportion of functional feeding groups and functional habit groups were relatively simpler at upper stream and the degree of community differences was greater between upper and down, control sites. Spearman's correlation between biological attributes and substrate characteristics, water quality parameters had significant correlations; particularly, the substrate characteristics were more significantly related. In conclusion, the pool caused by artificial structures had negative effects on benthic macroinvertebrate communities thus leading to simplified stream habitats at upper stream ecosystems.

• International Journal of High-Rise Buildings
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• v.12 no.3
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• pp.203-208
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• 2023

The implementation of artificial intelligence (AI) design for tall building structures is an essential solution for addressing critical challenges in the current structural design industry. Generative AI technology is a crucial technical aid because it can acquire knowledge of design principles from multiple sources, such as architectural and structural design data, empirical knowledge, and mechanical principles. This paper presents a set of AI design techniques for building structures based on two types of generative AI: generative adversarial networks and graph neural networks. Specifically, these techniques effectively master the design of vertical and horizontal component layouts as well as the cross-sectional size of components in reinforced concrete shear walls and frame structures of tall buildings. Consequently, these approaches enable the development of high-quality and high-efficiency AI designs for building structures.

• Imaging Science in Dentistry
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• v.53 no.2
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• pp.101-108
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• 2023

Purpose: The aim of this study was to conduct a scoping review and meta-analysis to provide overall estimates of the recall and precision of artificial intelligence for detection and segmentation using oral and maxillofacial cone-beam computed tomography (CBCT) scans. Materials and Methods: A literature search was done in Embase, PubMed, and Scopus through October 31, 2022 to identify studies that reported the recall and precision values of artificial intelligence systems using oral and maxillofacial CBCT images for the automatic detection or segmentation of anatomical landmarks or pathological lesions. Recall (sensitivity) indicates the percentage of certain structures that are correctly detected. Precision (positive predictive value) indicates the percentage of accurately identified structures out of all detected structures. The performance values were extracted and pooled, and the estimates were presented with 95% confidence intervals(CIs). Results: In total, 12 eligible studies were finally included. The overall pooled recall for artificial intelligence was 0.91 (95% CI: 0.87-0.94). In a subgroup analysis, the pooled recall was 0.88 (95% CI: 0.77-0.94) for detection and 0.92 (95% CI: 0.87-0.96) for segmentation. The overall pooled precision for artificial intelligence was 0.93 (95% CI: 0.88-0.95). A subgroup analysis showed that the pooled precision value was 0.90 (95% CI: 0.77-0.96) for detection and 0.94 (95% CI: 0.89-0.97) for segmentation. Conclusion: Excellent performance was found for artificial intelligence using oral and maxillofacial CBCT images.