Internationales Kolloquium über Anwendungen der Informatik und Mathematik in Architektur und Bauwesen : 20. bis 22.7. 2015, Bauhaus-Universität Weimar

The 20th International Conference on the Applications of Computer Science and Mathematics in Architecture and Civil Engineering will be held at the Bauhaus University Weimar from 20th till 22nd July 2015. Architects, computer scientists, mathematicians, and engineers from all over the world will meet in Weimar for an interdisciplinary exchange of experiences, to report on their results in research, development and practice and to discuss. The conference covers a broad range of research areas: numerical analysis, function theoretic methods, partial differential equations, continuum mechanics, engineering applications, coupled problems, computer sciences, and related topics. Several plenary lectures in aforementioned areas will take place during the conference. We invite architects, engineers, designers, computer scientists, mathematicians, planners, project managers, and software developers from business, science and research to participate in the conference

Shear Strength Prediction of Reinforced Concrete Shear Wall Using ANN, GMDH-NN and GEP

To provide lateral resistance in structures as well as buildings, there are some types of structural systems such as shear walls. The utilization of lateral loads occurs on a plate on the wall's vertical dimension. Conventionally, these sorts of loads are transferred to the wall collectors. There is a significant resistance between concrete shear walls and lateral seismic loading. To guarantee the building's seismic security, the shear strength of the walls has to be prognosticated by using models. This paper aims to predict shear strength by using Artificial Neural Network (ANN), Neural Network-Based Group Method of Data Handling (GMDH-NN), and Gene Expression Programming (GEP). The concrete's compressive strength, the yield strength of transverse reinforcement, the yield strength of vertical reinforcement, the axial load, the aspect ratio of the dimensions, the wall length, the thickness of the reinforced concrete shear wall, the transverse reinforcement ratio, and the vertical reinforcement ratio are the input parameters for the neural network model. And the shear strength of the reinforced concrete shear wall is considered as the target parameter of the ANN model. The results validate the capability of the models predicted by ANN, GMDH-NN, and GEP, which are suitable for use as a tool for predicting the shear strength of concrete shear walls with high accuracy

Internationales Kolloquium über Anwendungen der Informatik und Mathematik in Architektur und Bauwesen : 20. bis 22.7. 2015, Bauhaus-Universität Weimar

The 20th International Conference on the Applications of Computer Science and Mathematics in Architecture and Civil Engineering will be held at the Bauhaus University Weimar from 20th till 22nd July 2015. Architects, computer scientists, mathematicians, and engineers from all over the world will meet in Weimar for an interdisciplinary exchange of experiences, to report on their results in research, development and practice and to discuss. The conference covers a broad range of research areas: numerical analysis, function theoretic methods, partial differential equations, continuum mechanics, engineering applications, coupled problems, computer sciences, and related topics. Several plenary lectures in aforementioned areas will take place during the conference. We invite architects, engineers, designers, computer scientists, mathematicians, planners, project managers, and software developers from business, science and research to participate in the conference

Applied Mathematics and Computational Physics

As faster and more efficient numerical algorithms become available, the understanding of the physics and the mathematical foundation behind these new methods will play an increasingly important role. This Special Issue provides a platform for researchers from both academia and industry to present their novel computational methods that have engineering and physics applications

Innovative model updating procedure for dynamic identification and damage assessment of structures

The model updating technique allows the understanding of the dynamic behavior of a system and its damage state. In the last years, the structural monitoring has increased its applicability thanks to the decrease of the cost of sensors and improvements in the computational power. More and more structures are today instrumented in order to assess the intervention of progressive damages, understand their structural behavior and safety in almost real time. Nowadays, the real time identification of structural parameters and damage assessment is no longer unachievable. Moreover, the uncertainties evaluation is another important task required by the model updating procedures. Combining real time assessment and uncertainties evaluation, the algorithms can drive to a judgment about unsafety conditions in the buildings, with possible evacuation and securing of the structures, which is more and more required to structural health monitoring systems. The algorithms developed in this work are focused on these topics, especially on very quick model updating procedure, with uncertainties evaluation, which allows to estimate the structural parameters along with an error assessment. The quickness of the algorithm enables for its use in real time monitoring of actual structures. The algorithm itself is based on an innovative two steps procedure, with uncertainties evaluation, solving the inverse eigenvalues problem. The first step is achieved with closed form solution (without considering the determinant equations). If the solution does not satisfy the fixed thresholds, the second iterative step should be performed in order to improve the agreement between experimental outcomes and numerical ones. This procedure allows us to write the partial derivatives of the problem itself, with respect to the experimental outcomes, in closed form. Therefore, the parameters uncertainties are computed using the errors propagation. A second procedure is developed facing the complete problem entirely in iterative way, using a genetic algorithm with response surfaces.La procedura di model updating è una tecnica alquanto datata che permette di comprendere il comportamento dinamico di un sistema e il suo stato di danno. Negli ultimo anni, il monitoraggio strutturale ha incrementato la sua applicabilità grazie al ridotto costo dei sensori e al miglioramento della potenza computazionale. Sempre più strutture sono oggi strumentate per valutare i loro danni e capire il comportamento dinamico stesso. La valutazione in tempo reale dei parametri strutturali e dello stato di danno è oggigiorno non più irraggiungibile. La valutazione delle incertezze sui parametri è, inoltre, richiesta ai moderni algoritmi di model updating. La combinazione della valutazione in tempo reale e dell'incertezza possono portare a un giudizio di situazioni potenzialmente pericolose in strutture esistenti con possibile evacuazione e messa in sicurezza delle strutture stesse. Questa valutazione è sempre più richiesta ai sistemi di monitoraggio strutturale. L'algoritmo sviluppato in questo lavoro è incentrato su questi aspetti, in particolare sulla rapida valutazione dei parametri strutturali e delle relative incertezze. La velocità dell'algoritmo permette l'uso dello stesso per il monitoraggio in tempo reale delle strutture. L'algoritmo è basato su una procedura innovativa a due fasi, con valutazione dell'incertezza, risolvendo un problema inverso agli autovalori. La prima fase è risolta con formulazione chiusa del problema (senza considerare le equazioni ai determinanti). Se la soluzione non soddisfa delle soglie prefissate per i parametri di controllo, la seconda fase, iterativa, deve essere eseguita in modo da migliorare la corrispondenza tra risultati sperimentali e numerici. La procedura permette, inoltre, di scrivere le derivate parziali del problema stesso, rispetto ai risultati sperimentali, in formulazione chiusa; pertanto le incertezze sui parametri sono calcolate mediante la teoria della propagazione degli errori. Una seconda procedura è sviluppata affrontando il problema completamente in forma iterativa, usando un algoritmo genetico con superfici di risposta