Partitioned time integration methods for hardware in the loop based on linearly implicit L-Stable Rosenbrock methods

Hardware in the loop based on dynamic substructuring was conceived to be a hybrid numerical-experimental technique to simulate the non-linear behaviour of an emulated structure. Its challenge is to ensure that both numerical and physical substructures interact in real time by means of actuators –transfer systems-. With this objective in mind, the development and implementation of partitioned real-time compatible Rosenbrock algorithms are presented in this paper. In detail, we shortly introduce monolithic linearly implicit L-stable algorithms with two stages; and in view of the analysis of complex emulated structures, we present a novel interfield partitioned algorithm. Both the stability and accuracy properties of the proposed algorithm are examined through analytical and numerical studies carried out on Single-DoF model problems. Moreover, a novel test rig conceived to perform both linear and nonlinear substructure tests is introduced, and tests on a two-DoF split-mass system are illustrated. The drawbacks of this algorithm are underlined and improvements are introduced on a companion solution procedure

Monolithic and partitioned Rosenbrock-based time integration methods for dynamic substructure tests

Real-time testing with dynamic substructuring provides an efficient way to simulate the nonlinear dynamic behaviour of civil structures or mechanical facilities. In this technique, the test structure is divided onto two substructures: the relatively crucial substructure is tested physically and the other is modelled numerically in the computer. The key challenge is to ensure that both substructures interact in real-time, in order to simulate the behaviour of the emulated structure. This has special demands on the utilized integration methods and their implementations. Researchers have devoted significant effort to implement second-order integrators, such as Newmark integration methods, in a monolithic way where both substructures are integrated altogether. However, in view of large and complex structures, time integration methods are required to advance large-scale systems hence endowed with high-frequency components of the response or mixed first- and second- order systems like in the case of controlled systems. In this case, the monolithic implementation of a second-order time integration method becomes inefficient or inaccurate. With these promises, the thesis adopts the Rosenbrock-based time integration methods for both dynamic simulations of complex systems and substructure tests, and in particular, focuses on the development of monolithic schemes with subcycling strategies for nonlinear cases and partitioned methods with staggered and parallel solution procedures for linear and nonlinear cases. Initially, the Rosenbrock integration methods endowed with one stage to three stages are introduced and their applicabilities to second-order systems are investigated in terms of accuracy, stability and high-frequency dissipation, such as stability analysis of the Rosenbrock methods with one stage and two stages via the energy approach and numerical experiments on an uncoupled spring-pendulum system. Then, these methods are implemented in a monolithic way for real time substructure tests also considering subcycling strategies. Meanwhile, real-time substructure tests considering nonlinearities both in the numerical and physical substructures were carried out to illustrate the performances of the monolithic methods. Moreover, three types of partitioned algorithms based on the element-to-element partitioning are successively proposed. Two of them are based on acceleration continuity with a staggered solution procedure and a parallel solution procedure, respectively, and one of them is based on velocity continuity and a projection method. Both stability and accuracy properties of the proposed algorithms are examined by means of analytical techniques and numerical studies on single-, two-, three- and four-degree-of-freedom model problems and a coupled spring-pendulum system. Finally, a novel test rig conceived to perform both linear and nonlinear substructure tests with different combinations of numerical and physical substructures are presented and commented

Seismic Performance of a New Type of Fabricated Tie-Column

The reinforced concrete (RC) frames infilled with masonry walls are widely used in buildings. It has been well recognized that the arrangement of tie-columns can improve the wall integrity and seismic performance. However, the existing cast-in-situ tie-column (CSTC) has some problems, and a new type of fabricated tie-column (FTC) which can be recycled for secondary use is proposed in this study. Two specimens, the wall constrained by the cast-in-situ tie-columns (W-CSTC) and the wall constrained by the fabricated tie-columns (W-FTC), were designed and constructed. Low cyclic loading tests were carried out and some parameters, such as the failure modes, hysteretic curves and so forth, were used to evaluate the applicability of the FTC. The results show the W-FTC has a certain initial stiffness and strength, favorable deformation capacity, and the FTC can not only enhance the wall integrity to meet the functional requirements of tie-columns, but also solve the connection problems and reduce the adverse effects on the frame structure

Machine Learning Assessment of Damage Grade for Post-Earthquake Buildings: A Three-Stage Approach Directly Handling Categorical Features

The rapid assessment of post-earthquake building damage for rescue and reconstruction is a crucial strategy to reduce the enormous number of human casualties and economic losses caused by earthquakes. Conventional machine learning (ML) approaches for this problem usually employ one-hot encoding to cope with categorical features, and their overall procedure is neither sufficient nor comprehensive. Therefore, this study proposed a three-stage approach, which can directly handle categorical features and enhance the entire methodology of ML applications. In stage I, an integrated data preprocessing framework involving subjective–objective feature selection was proposed and performed on a dataset of buildings after the 2015 Gorkha earthquake. In stage II, four machine learning models, KNN, XGBoost, CatBoost, and LightGBM, were trained and tested on the dataset. The best model was judged by comprehensive metrics, including the proposed risk coefficient. In stage III, the feature importance, the relationships between the features and the model’s output, and the feature interaction effects were investigated by Shapley additive explanations. The results indicate that the LightGBM model has the best overall performance with the highest accuracy of 0.897, the lowest risk coefficient of 0.042, and the shortest training time of 12.68 s due to its relevant algorithms for directly tackling categorical features. As for its interpretability, the most important features are determined, and information on these features’ impacts and interactions is obtained to improve the reliability of and promote practical engineering applications for the ML models. The proposed three-stage approach can provide a reference for the overall ML implementation process on raw datasets for similar problems