Concrete mixture design for concrete slump test has many characteristics and mostly noisy. Such data will affect prediction of machine learning. This study aims to experiment on H2O Deep Learning framework and Bagging for noisy data and overfitting avoidance to create the Concrete Slump Model. The data consists of cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate, age, slump, and compressive strength. A primary data for concrete mixed design using the fine aggregate material from Merapi Volcano, the hills of Muntilan, and Kalioro. The coarse aggregate was obtained from Pamotan, Jepara, Semarang, Ungaran, and Mojosongo Boyolali Central Java. The cement was using Gresik and Holcim product and the water was from Tembalang, Semarang. The experiment model with one input layer with 7 neurons, one hidden layer with 20 neurons, and one output layer with 1 neuron using activation function TanH, with parameter L1=1.0E-5, L2=0.0, max weight=10.0, epsilon=1.0E-8, rho=0.99, and epoch=800 is able to achieve RMSE of 2.272. This result shows that after introducing Bagging, the error can be reduced up to 2.5 RMSE approximately (50% lower) compared to the model without Bagging. The manually tested mixture data was used to model evaluation. The result shows that the model was able to achieve RMSE 0.568. Following this study, this model can be used for further research such as creating slump design practicum equipment/ application software.
The weaknesses of Conventional Compression Testing Machines (CCTM) for student practicum are the high costs of procurement, maintenance, electricity, concrete materials, and human/equipment error factors. This research proposed of Virtual Compression Testing Machine (VCTM) based on Multilayer Perceptron that uses 2 hidden layers, 60 neurons with bagging, and other parameters of a Deep Neural Network with RMSE value of 4.738. The application of VCTM has been successfully carried out and there was no significant difference with CCTM. VCTM can be used for various types of concrete with high testing intensity. Lecturers and researchers can use VCTM to conduct research.
Artificial Neural Network (ANN) is a Machine Learning (ML) algorithm which learn by itself and organize its thinking to solve problems. Although the learning process involves many hidden layers (Deep Learning) this algorithm still has weaknesses when faced with a high noise data. Concrete mixture design data has a high enough noise caused by many unidentified / unmeasurable aspects. Information needs about the compressive strength of early age concrete (under 28 days) are often needed while the construction process is still ongoing. H2O’s Deep Learning type of ANN has been tried to predict the compressive strength of early age concrete, but the results are less than optimal. This study aims to improve the H2O’s-ANN prediction model using Bagging to reduce the influence of noise and overfitting. The lowest RMSE that are able to be achieved in this research with Bagging is 6.385 while it is 6,674 without Bagging. This result proves that Bagging is significant to reduce the Deep Learning error rate for predicting the compressive strength of early age concrete. Future work this model, as a new innovation in machine learning in civil engineering vocational education subject, can be used to various concrete mixture design and learning about concrete. Because this concrete mix design model is digital, it can be used for virtual learning. Currently the author is conducting research to create a virtual concrete mix design learning model.
The biggest challenge for architecture designers is the time required for the design process. Especially landscape architects who have different work limits from architects in general. In contrast to architects in general, who are assisted in producing design plans by building standards, building requirements, and space programs that adapt to the type of project being undertaken. At the same time, some design jobs demand high-productivity landscape animation presentation in a short time. The long process involved in designing animation often makes it difficult for designers to produce optimal work. This study proposes generative zooming animation with artificial intelligence support to shorten the designer’s work process and energy optimization. Deep learning with Vector Quantized Generative Adversarial Network and Contrastive Language-Image Pre-Training was used to generate alternative landscape designs from text prompt-based and compile them in animation. Our experiment shows that one frame can be generated roughly in 3.636 ± 0.089 s, which is significantly faster than the conventional method to create animation. Moreover, our method is able to achieve a good-quality image, which scored 3.2904 using inception score evaluation. The effectiveness of deep learning in visual landscape and animation creation can help designers speed up the design process. Furthermore, working time efficiency without compromising design quality will increase designer productivity and economic growth.
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