Development of Autonomous Hammering Test Method for Deteriorated Concrete Structures Based on Artificial Intelligence and 3D Positioning System

2018 ◽  
pp. 219-228
Author(s):  
Katsufumi Hashimoto ◽  
Tomoki Shiotani ◽  
Takahiro Nishida ◽  
Hideo Kumagai ◽  
Katsuhiko Kokubo
Keyword(s):  
Artificial Intelligence ◽  
Concrete Structures ◽  
Test Method ◽  
Positioning System ◽  
3D Positioning

scholarly journals Determining the Precise Work Area of Agriculture Machinery Using Internet of Things and Artificial Intelligence

2020 ◽  
Vol 10 (10) ◽  
pp. 3365
Author(s):  
Muhammad Waleed ◽  
Tai-Won Um ◽  
Tariq Kamal ◽  
Aftab Khan ◽  
Adil Iqbal
Keyword(s):  
Artificial Intelligence ◽  
Internet Of Things ◽  
Global Positioning System ◽  
Positioning System ◽  
Allocation Of Resources ◽  
Smart System ◽  
Irregular Shapes ◽  
Work Area ◽  
Global Positioning ◽  
The Internet Of Things

Precisely measuring the work area of agriculture farm machinery is important for performing the authentication of machinery usage, better allocation of resources, measuring the effect of machinery usage on the yield, usage billing and driver’s behaviour. The manual measurement, which is a common practice is an error-prone and time-consuming process. The irregular fields make it even more difficult to calculate the work area. An automatic solution that uses smart technology and algorithms to precisely calculate the work area is crucial for the advancement of agriculture. In this work, we have developed a smart system that utilizes the Internet of Things (IoT), Global Positioning System (GPS) and Artificial Intelligence (AI) that records the movement of agriculture machinery and use it to measure the precise work area of its usage. The system couples the nearest neighbourhood algorithms with Contact-based mechanisms to find the precise work area for different shaped fields and activities. The system was able to record the movement of machinery and calculate its work area, regardless of how many times the machinery runs through a particular field. Our evaluation shows that the system was able to precisely find the work boundaries and calculate the area with a maximum of 9% error for irregular shapes.

scholarly journals Detection of Reinforcement Corrosion in Reinforced Concrete Structures by Potential Mapping: Theory and Practice

2018 ◽  
Vol 2018 ◽  
pp. 1-6
Author(s):  
Gino Ebell ◽  
Andreas Burkert ◽  
Jürgen Mietz
Keyword(s):  
Reinforced Concrete ◽  
Influencing Factors ◽  
Stress Measurement ◽  
Concrete Structures ◽  
German Society ◽  
Theory And Practice ◽  
Test Method ◽  
Reinforced Concrete Structures ◽  
Reinforcing Steel ◽  
Potential Mapping

Electrochemical potential mapping according to guideline B3 of DGZfP (German Society for Nondestructive Testing) is a recognized technique for the localization of corroding reinforcing steels. In reinforced concrete structures the measured potentials are not necessarily directly linked to the corrosion likelihood of the reinforcing steel. The measured values may be significantly affected, different from, e.g., stress measurement, by different influences on the potential formation at the phase boundary metal/concrete itself as well as the acquisition procedure. Due to the complexity of influencing factors there is a risk that the results are misinterpreted. Therefore, in a training concept firstly the theoretical basics of the test method should be imparted. Then, frequently occurring practical situations of various influencing factors will be made accessible to the participants by a model object specially designed for this purpose. The aim is to impart profound knowledge concerning the characteristics of potential mapping for detecting corrosion of reinforcing steel in order to apply this technique in practice as reliable and economical test method.

Artificial intelligence based on deep learning for differential diagnosis between benign and malignant pulmonary nodules: A real-world, multicenter, diagnostic study.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 9037-9037
Author(s):  
Tao Xu ◽  
Chuoji Huang ◽  
Yaoqi Liu ◽  
Jing Gao ◽  
Huan Chang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Lung Cancer ◽  
Deep Learning ◽  
Diagnostic Accuracy ◽  
Real World ◽  
Pulmonary Nodules ◽  
Ct Images ◽  
Test Method ◽  
Histopathological Diagnosis ◽  
Diagnostic Study

9037 Background: Lung cancer is the most common cancer worldwide. Artificial intelligence (AI) platform using deep learning algorithms have made a remarkable progress in improving diagnostic accuracy of lung cancer. But AI diagnostic performance in identifying benign and malignant pulmonary nodules still needs improvement. We aimed to validate a Pulmonary Nodules Artificial Intelligence Diagnostic System (PNAIDS) by analyzing computed tomography (CT) imaging data. Methods: This real-world, multicentre, diagnostic study was done in five different tier hospitals in China. The CT images of patients, who were aged over 18 years and never had previous anti-cancer treatments, were retrieved from participating hospitals. 534 eligible patients with 5-30mm diameter pulmonary nodules identified by CT were planning to confirm with histopathological diagnosis. The performance of PNAIDS was also compared with respiratory specialists and radiologists with expert or competent degrees of expertise as well as Mayo Clinic’s model by area under the curve (AUC) and evaluated differences by calculating the 95% CIs using the Z-test method. 11 selected participants were tested circulating genetically abnormal cells (CACs) before surgery with doctors suggested. Results: 611 lung CT images from 534 individuals were used to test PNAIDS. The diagnostic accuracy, valued by AUC, in identifying benign and malignant pulmonary nodules was 0.765 (95%CI [0.729 - 0.798]). The diagnostic sensitivity of PNAIDS is 0.630(0.579 – 0.679), specificity is 0.753 (0.693 – 0.807). PNAIDS achieved diagnostic accuracy similar to that of the expert respiratory specialists (AUC difference: 0.0036 [-0.0426 - 0.0497]; p = 0.8801) and superior when compared with Mayo Clinic’s model (0.120 [0.0649 - 0.176], p < 0·0001), expert radiologists (0.0620 [0.0124 - 0.112], p = 0.0142) and competent radiologists (0.0751 [0.0248 - 0.125], p = 0.0034). 11 selected participants were suggested negative in AI results but positive in respiratory specialists’ result. 8 of them were malignant in histopathological diagnosis with tested more than 3 CACs in their blood. Conclusions: PNAIDS achieved high diagnostic accuracy in differential diagnoses between benign and malignant pulmonary nodules, with diagnostic accuracy similar to that of expert respiratory specialists and was superior to that of Mayo Clinic’s model and radiologists. CACs may be able to assist CT-based AI in improving their effectiveness but it still need more data to be proved. Clinical trial information: ChiCTR1900026233.

Behavior of Continuous FRP Rectangular Spirals as Shear Reinforcement for Concrete Beams

2011 ◽  
Vol 418-420 ◽  
pp. 307-312
Author(s):  
Shi Yong Jiang ◽  
Bing Hong Li ◽  
Qian Hua Shi ◽  
Xian Qi Hu
Keyword(s):  
Experimental Investigation ◽  
Tensile Strength ◽  
Concrete Beams ◽  
Concrete Strength ◽  
Concrete Structures ◽  
Test Method ◽  
Test Results ◽  
Shear Reinforcement ◽  
Straight Portion ◽  
Embedment Length

The replacement of steel stirrups with FRP stirrups in concrete structures can significantly improve the durability under severe environmental conditions, increase the service life of the structure. In some cases where the application of traditional concrete structures is restricted, such as structures that require the environment without magnetic and electric interferences, the best way is to use nonmetallic materials. Considering the unique properties compared with traditional reinforcements, FRP reinforcements is very suitable in such cases. This paper discussed the behavior of FRP stirrups used as shear reinforcement for concrete structures, continuous FRP rectangular spirals, a type of FRP stirrups, was used in the experimental investigation. Inspired by the test method suggested by ACI Committee 440, L shape specimen and U shape specimen are designed to test the tensile strength of continuous FRP rectangular spirals. Through the analysis of test results, it is indicated that the strength of the bent portion of FRP spirals is significantly lower than that of the straight portion, and the strength of FRP spirals would increase as the concrete strength or the embedment length of the bent portion increased. The loading mechanism of continuous FRP rectangular spirals embedded in concrete beams can be better represented by U shape specimen compared with L shape specimens, it is suggested by the author that the U shape specimen can be use when the dimension of FRP stirrups or spirals does not meet the requirements of ACI Committee 440 test method.

The Discussion about Concrete on the Relationship between Corrosion Resistance and Time under Bending Loads

2014 ◽  
Vol 599-601 ◽  
pp. 148-152
Author(s):  
Zhao Feng Xue ◽  
Jian Li
Keyword(s):  
Corrosion Resistance ◽  
Saline Soil ◽  
Concrete Structures ◽  
Concrete Specimen ◽  
Test Method ◽  
Mineral Admixture ◽  
Prevention Measures ◽  
Fly Ash Concrete ◽  
Bending Loads ◽  
The Relationship

Through the corrosion test method of different active mineral admixture concrete under bending loads,we studied the C50 concrete specimen under bending loads on corrosion resistance and with the relation to corrosion time about the condition of specimen in saline soil. The results showed that the maximum corrosion effect on concrete with the saline soil in the short-term (1 year) appeared from the sixth to the ninth month (under maximum corrosion loss coefficient), but in other time is smaller. Concrete with gas forming admixture exhibit best corrosion resistance, fly ash concrete come second and normal concrete show worst performance in corrosion resistance, we propose corrosion prevention measures for concrete structures under loads in saline soil region to improve the durability of concrete structures according to the result.

scholarly journals Optimization of Total Production of Refined Sugar From Raw Sugar Raw Materials and Supporting Raw Materials Using the Generate-And-Test Method at PT. DSI Banten

2019 ◽  
Vol 1 (2) ◽  
pp. 29
Author(s):  
Sigit Rahayu ◽  
Andri Budi Kusumah ◽  
Supriyadi Supriyadi ◽  
Wahyu Oktri Widyarto
Keyword(s):  
Artificial Intelligence ◽  
Heuristic Search ◽  
Raw Materials ◽  
Test Method ◽  
Raw Material ◽  
Sugar Production ◽  
Material Type ◽  
Raw Sugar ◽  
Type 3

The search problem is a problem that is commonly applied to systems based on the concept of Artificial Intelligence. One of the well-known heuristic search methods in Artificial Intelligence terminology is Generate and Test. In general, there are no companies operating without raw materials, raw materials in PT. DSI is a type of main and supporting raw material. Refined sugar production at PT. DSI Banten has been experiencing fluctuations in the output of production every day, the data in April 2014 showed from 1-7 consecutively that is 726, 578, 592, 518, 692, 734, 473 tons (PT. DSI, April 2014 ). The purpose of this study is to implement the heuristic search concept with the Generate and Test Algorithm in the search for a combination of the two raw materials to obtain the highest amount of production / output in the form of refined sugar, from the results of this study obtained a system that is able to find the highest amount of sugar production per cuisine, namely in the form of types of supporting raw materials (Limestone CaO, HCL, NaOH) and types of main raw materials (Raw sugar). After conducting research through the heuristic search concept with the GnT method, from 3 types of supporting raw materials (type 1: supplier from PT. SAP, type 2: supplier from PT. MNA, type 3: supplier from PT. CKT) and 3 types of raw material main (raw sugar 1: import from Australia, raw sugar type 2: import from Vietnam, raw sugar type 3: import from Thailand) found an optimization of the two raw materials with the results of supporting material type "3" and main raw material type " 2 "with the amount of 123 tons per cuisine for refined sugar output, the results obtained are able to increase productivity in the refined sugar processing.

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