scholarly journals An Adaptive Neuro-Fuzzy Inference Model to Predict Punching Shear Strength of Flat Concrete Slabs

2019 ◽  
Vol 9 (4) ◽  
pp. 809 ◽  
Author(s):  
Mohammed Mashrei ◽  
Alaa Mahdi
Keyword(s):  
Shear Strength ◽  
Fuzzy Inference ◽  
Concrete Strength ◽  
Input Parameter ◽  
Punching Shear ◽  
Concrete Slabs ◽  
Shear Reinforcement ◽  
Inference Model ◽  
Inference System ◽  
Neuro Fuzzy

An adaptive neuro-fuzzy inference system (ANFIS)-based model was developed to predict the punching shear strength of flat concrete slabs without shear reinforcement. The model was developed using a database collected from 207 experiments available in the existing literature. Five key input parameters were used to build the model, which were slab effective depth, concrete strength, reinforcement ratio, yield tensile strength of reinforcement, and width of square loaded area. The output parameter of the model was punching shear strength. The results from the adaptive neural fuzzy inference model were compared to those from the simplified punching shear equations of ACI, BS-8110, Model Code 2010, Euro-Code 2, and also experimental results. The root mean square error (RMSE) and the correlation coefficient (R) were used as evaluation criteria. Parametric studies were presented using ANFIS to assess the effect of each input parameter on the punching shear strength and to compare ANFIS results to those from the equations proposed in commonly used codes. The results showed that the ANFIS model is simple and provided the most accurate predictions of the punching shear strength of two-way flat concrete slabs without shear reinforcement.

Critical shear crack theory-based punching shear model for FRP-reinforced concrete slabs

2020 ◽  
pp. 136943322097814
Author(s):  
Xing-lang Fan ◽  
Sheng-jie Gu ◽  
Xi Wu ◽  
Jia-fei Jiang
Keyword(s):  
Shear Strength ◽  
Reinforced Concrete ◽  
Shear Crack ◽  
Concrete Strength ◽  
Weight Ratio ◽  
Punching Shear ◽  
Concrete Slabs ◽  
Crack Theory ◽  
Reinforced Concrete Slabs ◽  
Rc Slabs

Owing to their high strength-to-weight ratio, superior corrosion resistance, and convenience in manufacture, fiber-reinforced polymer (FRP) bars can be used as a good alternative to steel bars to solve the durability issue in reinforced concrete (RC) structures, especially for seawater sea-sand concrete. In this paper, a theoretical model for predicting the punching shear strength of FRP-RC slabs is developed. In this model, the punching shear strength is determined by the intersection of capacity and demanding curve of FRP-RC slabs. The capacity curve is employed based on critical shear crack theory, while the demand curve is derived with the help of a simplified tri-linear moment-curvature relationship. After the validity of the proposed model is verified with experimental data collected from the literature, the effects of concrete strength, loading area, FRP reinforcement ratio, and effective depth of concrete slabs are evaluated quantitatively.

Prediction of Concrete Strength Using Fuzzy Neural Networks

2011 ◽  
Vol 243-249 ◽  
pp. 6121-6126 ◽  
Author(s):  
Jing Xu ◽  
Xiu Li Wang
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Concrete Strength ◽  
Fuzzy Neural Networks ◽  
Output Data ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Fuzzy Neural

The purpose of this paper is to develop the Ⅰ-PreConS (Intelligent PREdiction system of CONcrete Strength) that predicts the compressive strength of concrete to improve the accuracy of concrete undamaged inspection. For this purpose, the system is developed with adaptive neuro－fuzzy inference system (ANFIS) that can learn cube test results as training patterns. ANFIS does not need a specific equation form differ from traditional prediction models. Instead of that, it needs enough input-output data. Also, it can continuously re-train the new data, so that it can conveniently adapt to new data. In the study, adaptive neuro－fuzzy inference system (ANFIS) based on Takagi-Sugeno rules is built up to prediction concrete strength. According to the expert experience, the relationship between the rebound value and concrete strength tends to power function. So the common logarithms of rebound value and strength value are used as the inputs and outputs of the ANFIS. System parameter sets are iteratively adjusted according to input and output data samples by a hybrid-learning algorithm. In the system, in order to improve of the ANFIS, condition parameter sets can be determined by the back propagation gradient descent method and conclusion parameter sets can be determined by the least squares method. As a result, the concrete strength can be inferred by the fuzzy inference. The method takes full advantage of the characteristics of the abilities of Fuzzy Neural Networks (FNN) including automatic learning, generation and fuzzy logic inference. The experiment shows that the average relative error of the predicted results is 10.316% and relative standard error is 12.895% over all the 508 samples, which are satisfied with the requirements of practical engineering. The ANFIS-based model is very efficient for prediction the compressive strength of in-service concrete.

Rehabilitation of reinforced concrete slab–column connections

2002 ◽  
Vol 29 (4) ◽  
pp. 602-611 ◽  
Author(s):  
Ehab F El-Salakawy ◽  
Maria Anna Polak ◽  
Khaled A Soudki
Keyword(s):  
Compressive Strength ◽  
Shear Strength ◽  
Concrete Slab ◽  
Normal Weight ◽  
Punching Shear ◽  
Concrete Slabs ◽  
Shear Reinforcement ◽  
Reinforced Concrete Slab ◽  
Normal Weight Concrete ◽  
Average Compressive Strength

The paper presents work on punching shear rehabilitation and strengthening of existing slab–column connections. Four full-scale specimens representing slab–column edge connections were built and tested to failure. Three slabs were then repaired and strengthened and tested again. In the originally tested slabs, which were chosen for repair, one slab had an opening in front of the column and contained shear reinforcement, one slab had an opening and no shear reinforcement, and one had no opening and no reinforcement. The dimensions of the slabs were 1540 × 1020 × 120 mm with square columns (250 × 250 mm). The openings in the specimens were square (150 × 150 mm) with the sides parallel to the sides of the column. The slabs were made using normal weight concrete (28-day average compressive strength of 32 MPa) and reinforced with a reinforcement ratio of 0.75%. The slabs were repaired by replacing old-damaged concrete with new concrete of the same properties. Strengthening was carried out using shear studs for the two slabs, which originally did not have shear reinforcement. The rehabilitation increased the punching shear strength (by 26–41%) and the ductility of the connections. All repaired specimens failed in flexure.Key words: concrete slabs, punching shear, rehabilitation, edge connections, openings, studs, repair.

scholarly journals PREDIKSI VOLUME LALU LINTAS ANGKUTAN LEBARAN PADA WILAYAH JAWA TENGAH DENGAN METODE K-MEANS CLUSTERING UNTUK ADAPTIVE NEURO FUZZY INFERENCE SYSTEM (ANFIS)

2016 ◽  
Vol 7 (1) ◽  
pp. 199
Author(s):  
Evanita Evanita ◽  
Edi Noersasongko ◽  
Ricardus Anggi Pramunendar
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Input Parameter ◽  
Inference System ◽  
Neuro Fuzzy

Di Indonesia kepadatan arus lalu lintas terjadi pada jam berangkat dan pulang kantor, hari-hari libur panjang atau hari-hari besar nasional terutama saat hari raya Idul Fitri (lebaran). Mudik sudah menjadi tradisi bagi masyarakat Indonesia yang ditunggu-tunggu menjelang lebaran, berbondong-bondong untuk pulang ke kampung halaman untuk bertemu dan berkumpul dengan keluarga. Kegiatan rutin tahunan ini banyak di lakukan khususnya bagi masyarakat kota-kota besar seperti Jakarta, dimana diketahui bahwa Jakarta adalah Ibu kota negara Republik Indonesia dan menjadi tujuan merantau untuk mencari pekerjaan yang lebih layak yang merupakan harapan besar bagi masyarakat desa. Volume kendaraan bertambah sejak 7 hari menjelang lebaran sampai 7 hari setelah lebaran tiap tahunnya terutama pada arah keluar dan masuk wilayah Jawa Tengah yang banyak menjadi tujuan mudik. Volume kendaraan saat arus mudik yang selalu meningkat inilah yang akan diteliti lebih lanjut dengan metode ANFIS agar dapat menjadi alternatif solusi langkah apa yang akan dilakukan di tahun selanjutnya agar pelayanan lalu lintas, kemacetan panjang dan angka kecelakaan berkurang. Dengan input parameter ANFIS yang digunakan yaitu pengclusteran hingga 5 cluster, epoch 100, error goal 0 diperoleh performa terbaik ANFIS dengan K-Means clustering yang terbagi menjadi 3 cluster, epoch terbaik sebesar 20 dengan RMSE Training terbaik sebesar 0,1198, RMSE Testing terbaik sebesar 0,0282 dan waktu proses tersingkat sebesar 0,0695.Selanjutnya hasil prediksi diharapkan dapat bermanfaat menjadi alternatif solusi langkah apa yang akan dilakukan di tahun selanjutnya agar pelayanan lalu lintas lebih baik lagi.Kata kunci: angkutan lebaran, Jawa Tengah, ANFIS.

scholarly journals Adaptive Neuro-Fuzzy Inference Model for Monitoring Hypertension Risk

2021 ◽  
Vol 16 (4) ◽  
pp. 0-0
Keyword(s):  
High Risk ◽  
Healthcare Professionals ◽  
Fuzzy Inference ◽  
Teaching Hospitals ◽  
Inference Model ◽  
Inference System ◽  
Hypertension Risk ◽  
Neuro Fuzzy ◽  
Medium Risk ◽  
Very High

This study presented a model to classify risk of hypertension using Adaptive Neuro-Fuzzy Inference System (ANFIS). In order to develop the model cardiologists from teaching hospitals in Nigeria were interviewed so as to identify required variables for classification. Structured questionnaires were used to elicit information about the risk factors and the associated risk of hypertension from respondents. The MATLAB ANFIS Toolbox was used to simulate the model. The result of this study revealed that there were 33 main variables identified for monitoring hypertension risk and they were in line with the WHO/ISH classification standard. The result showed that majority of the patients selected had very high risk (57.0%) of hypertension which consisted more than 50% of the patients selected followed by 19% representing patients with high risk of hypertension, followed by patients with medium risk of hypertension. In conclusion, the model assist healthcare professionals to have accurate diagnosis, early detection and proper management of hypertension.

Potential of adaptive neuro fuzzy inference system for evaluating the factors affecting steel-concrete composite beam's shear strength

2016 ◽  
Vol 21 (3) ◽  
pp. 679-688 ◽  
Author(s):  
M. Safa ◽  
M. Shariati ◽  
Z. Ibrahim ◽  
A. Toghroli ◽  
Shahrizan Bin Baharom ◽  
...  
Keyword(s):  
Shear Strength ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Factors Affecting ◽  
Neuro Fuzzy
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