Inverse Analysis of Aesthetic Evaluation of Planted Concrete Structures by Neural Networks

Chikata, Yasuo; Yasuda, Noboru; Matsushima, Manabu; Kobori, Tameo

doi:10.1111/0885-9507.00104

Cited by 4 publications

(2 citation statements)

References 2 publications

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“…They used the analytic hierarchy process (AHP) method to represent the user preference in a fitness function. Chikata et al [16] simulated user aesthetic evaluation information in the design of concrete retaining walls based on neural networks. For the design of discrete structures, a remarkable work was conducted by Bailey and Raich [17].…”

Section: Related Work 21 Research On User Design Preferencementioning

confidence: 99%

An Automated Design Method for Plane Trusses Based on User Preference Information

2023

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Structural design is a complicated decision-making process involving multiple qualitative and quantitative factors. Currently, most automated design methods consider only quantitative objectives and constraints, ignoring the qualitative design information that is difficult define mathematically, such as the user preference for structural shapes. This limits the functionality and efficiency of such design methods. In this study, a design method named STSA-P is proposed for plane trusses to incorporate user preference into the automatic design process. Two main problems are addressed, i.e., how to quantify user preference information and how to coordinate it with other quantitative design objectives. A prediction model of user preference is developed for the first problem by generating the data set and selecting an appropriate machine learning (ML) algorithm. Specifically, a set of truss features quantitatively representing the structural shapes are identified for the truss sample population. Furthermore, an interactive system is developed for collecting user evaluation information as data labels. Strategies for reducing user fatigue are also considered during the evaluation process. A set of numerical experiments are conducted to select the suitable ML algorithm. Regarding the second problem, the physical programming method is modified to construct a new aggregate function which effectively coordinates user preference with other design objectives. A cost function is designed by considering the design constraints. On this basis, the prediction model is incorporated into the Structural Topology and Shape Annealing (STSA) method to form the STSA-P method. Two students are invited to perform a design case using the STSA-P method. It is demonstrated that the results verify the practicality and validity of the proposed method.

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Section: Related Work 21 Research On User Design Preferencementioning

confidence: 99%

An Automated Design Method for Plane Trusses Based on User Preference Information

2023

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“…Goh (1995) demonstrates application of the BP algorithm for correlating various experimental parameters and evaluating the CPT calibration chamber test data. The BP algorithm is used by Chikata et al (1998) to develop a system for aesthetic evaluation of concrete retaining walls and by Teh et al (1997) to estimate static capacity of precast reinforced concrete piles from dynamic stress wave data. Juang and Chen (1999) present neural network models for evaluating the liquefaction potential of sandy soils.…”

Section: Geotechnical Engineeringmentioning

confidence: 99%

Neural Networks in Civil Engineering: 1989–2000

Adeli

2001

Computer aided Civil Eng

708

344

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The first journal article on neural network application in civil/structural engineering was published in this journal in 1989. This article reviews neural network articles published in archival research journals since then. The emphasis of the review is on the two fields of structural engineering and construction engineering and management. Neural networks articles published in other civil engineering areas are also reviewed, including environmental and water resources engineering, traffic engineering, highway engineering, and geotechnical engineering. The great majority of civil engineering applications of neural networks are based on the simple backpropagation algorithm. Applications of other recent, more powerful and efficient neural networks models are also reviewed. Recent works on integration of neural networks with other computing paradigms such as genetic algorithm, fuzzy logic, and wavelet to enhance the performance of neural network models are presented.

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