• 대한교통학회지
• /
• 제30권1호
• /
• pp.125-133
• /
• 2012

본 논문에서는 스마트카드 데이터를 이용하여 서울시의 대중교통 환승통행 현황을 분석하고, 이를 통해 환승체계 효율화의 필요성을 살펴보았다. 특별히 환승효율성 제고를 위한 환승센터 건립 시 그 입지선정을 위한 수리모형을 이중구조모형으로 구축/제시하였다. 상위모형은 입지결정 모형으로 지역별 환승센터 개수 및 총건설비용 예산 제약하에서 총통행비용을 최소화하는 선형 0-1 정수모형이고, 하위 모형은 환승센터 입지 여부에 따른 사용자 평형 경로 선택 모형으로 구성된다. 모형의 해법으로 상위 및 하위 모형의 해를 순차적으로 구하는 방법을 제시하였고, 예제네트워크 적용을 통해 해법의 수렴성과 모형의 적용성을 평가하였다. 그 결과 본 논문에서 구축된 모형과 해법을 이용하여 효과적으로 최적의 환승센터 입지를 결정할 수 있음을 확인하였다. 마지막으로, 실제 네트워크에 적용하기 위한 방법론을 검토한 결과 본 논문의 모형과 해법이 현실에서도 충분히 활용될 수 있을 것으로 기대된다.

• Structural Engineering and Mechanics
• /
• 제85권4호
• /
• pp.469-484
• /
• 2023

A deep recursive bidirectional Cuda Deep Neural Network Long Short Term Memory (Bi-CuDNNLSTM) layer is recruited in this paper to predict the entire force time histories, and the corresponding hysteresis and backbone curves of reinforced concrete (RC) bridge piers using experimental fast and slow cyclic tests. The proposed stacked Bi-CuDNNLSTM layers involve multiple uncertain input variables, including horizontal actuator displacements, vertical actuators axial loads, the effective height of the bridge pier, the moment of inertia, and mass. The functional application programming interface in the Keras Python library is utilized to develop a deep learning model considering all the above various input attributes. To have a robust and reliable prediction, the dataset for both the fast and slow cyclic tests is split into three mutually exclusive subsets of training, validation, and testing (unseen). The whole datasets include 17 RC bridge piers tested experimentally ten for fast and seven for slow cyclic tests. The results bring to light that the mean absolute error, as a loss function, is monotonically decreased to zero for both the training and validation datasets after 5000 epochs, and a high level of correlation is observed between the predicted and the experimentally measured values of the force time histories for all the datasets, more than 90%. It can be concluded that the maximum mean of the normalized error, obtained through Box-Whisker plot and Gaussian distribution of normalized error, associated with unseen data is about 10% and 3% for the fast and slow cyclic tests, respectively. In recapitulation, it brings to an end that the stacked Bi-CuDNNLSTM layer implemented in this study has a myriad of benefits in reducing the time and experimental costs for conducting new fast and slow cyclic tests in the future and results in a fast and accurate insight into hysteretic behavior of bridge piers.

• Advances in Computational Design
• /
• 제6권1호
• /
• pp.15-30
• /
• 2021

With improvement in innovative manufacturing technologies, it became possible to fabricate any complex shaped structural design for practical applications. This allows for the fabrication of curvilinearly stiffened pressure vessels and pipes. Compared to straight stiffeners, curvilinear stiffeners have shown to have better structural performance and weight savings under certain loading conditions. In this paper, an optimization framework for designing curvilinearly stiffened composite pressure vessels and pipes is presented. NURBS are utilized to define curvilinear stiffeners over the surface of the pipe. An integrated tool using Python, Rhinoceros 3D, MSC.PATRAN and MSC.NASTRAN is implemented for performing the optimization. Rhinoceros 3D is used for creating the geometry, which later is exported to MSC.PATRAN for finite element model generation. Finally, MSC.NASTRAN is used for structural analysis. A Bi-Level Programming (BLP) optimization technique, consisting of Particle Swarm Optimization (PSO) and Gradient-Based Optimization (GBO), is used to find optimal locations of stiffeners, geometric dimensions for stiffener cross-sections and layer thickness for the composite skin. A cylindrical pipe stiffened by orthogonal and curvilinear stiffeners under torsional and bending load cases is studied. It is seen that curvilinear stiffeners can lead to a potential 10.8% weight saving in the structure as compared to the case of using straight stiffeners.