Search

The aim of this study is to build Machine Learning models to evaluate the effect of blast furnace slag (BFS) and waste tire rubber powder (WTRP) on the compressive strength of cement mortars. In order to develop these models, 12 different mixes with 288 specimens of the 2, 7, 28, and 90 days compressive strength experimental results of cement mortars containing BFS, WTRP and BFS+WTRP were used in training and testing by Random Forest, Ada Boost, SVM and Bayes classifier machine learning models, which implement standard cement tests. The machine learning models were trained with 288 data that acquired from experimental results. The models had four input parameters that cover the amount of Portland cement, BFS, WTRP and sample ages. Furthermore, it had one output parameter which is compressive strength of cement mortars. Experimental observations from compressive strength tests were compared with predictions of machine learning methods. In order to do predictive experimentation, we exploit R programming language and corresponding packages. During experimentation on the dataset, Random Forest, Ada Boost and SVM models have produced notable good outputs with higher coefficients of determination of R2, RMS and MAPE. Among the machine learning algorithms, Ada Boost presented the best R2, RMS and MAPE values, which are 0.9831, 5.2425 and 0.1105, respectively. As a result, in the model, the testing results indicated that experimental data can be estimated to a notable close extent by the model. Düzce Üniversitesi - 2011.03.HD.011

Recently, the developments in the field of ADA systems shifted from comfort oriented systems to safety oriented systems. Consequently, the complexity of testing those systems increased. This leads to a demand for reproducible, efficient and safe testing methods for ADA systems in general, and pre-crash systems in particular. A method for pre-crash systems evaluation is presented, involving TNO's Vehicle Hardware-inthe-Loop facility (VeHIL) and its Crash Lab. This method, called VeHIL/Pre-crash, aims to support the development and verification of pre-crash systems, increasing the level of reproducibility, effectiveness and safety of the testing process. To this end, a test vehicle, positioned on a roller bench, is placed in a simulated traffic environment. In order to provide the test vehicle's environment sensor input, one of the simulated traffic participants is represented by a rail guided target vehicle, which is stopped by crumple tubes. Although the realization of this setup is not straightforward due to practical aspects such as high decelerations of the target and the challenge to establish a realistic sensory input in a confined environment, it is considered a promising development allowing for nondestructive testing of pre-crash systems in a hardwarein-the-loop setup. © 2008 IEEE.

Recently, the developments in the field of ADA systems shifted from comfort oriented systems to safety oriented systems. Consequently, the complexity of testing those systems increased. This leads to a demand for reproducible, efficient and safe testing methods for ADA systems in general, and pre-crash systems in particular. A method for pre-crash systems evaluation is presented, involving TNO's Vehicle Hardware-inthe-Loop facility (VeHIL) and its Crash Lab. This method, called VeHIL/Pre-crash, aims to support the development and verification of pre-crash systems, increasing the level of reproducibility, effectiveness and safety of the testing process. To this end, a test vehicle, positioned on a roller bench, is placed in a simulated traffic environment. In order to provide the test vehicle's environment sensor input, one of the simulated traffic participants is represented by a rail guided target vehicle, which is stopped by crumple tubes. Although the realization of this setup is not straightforward due to practical aspects such as high decelerations of the target and the challenge to establish a realistic sensory input in a confined environment, it is considered a promising development allowing for nondestructive testing of pre-crash systems in a hardwarein-the-loop setup. © 2008 IEEE.