Analysis of Bridge Condition Rating Data Using Neural Networks

1997 ◽  
Vol 12 (6) ◽  
pp. 419-429 ◽  
Author(s):  
Jacques Cattan ◽  
Jamshid Mohammadi
Keyword(s):  
Neural Networks ◽  
Rating Data ◽  
Condition Rating ◽  
Bridge Condition Rating

scholarly journals Forecast Model of TV Show Rating Based on Convolutional Neural Network

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Lingfeng Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Prediction Model ◽  
Convolutional Neural Network ◽  
Network Structure ◽  
Rating Data ◽  
Tv Program ◽  
Neural Network Structure ◽  
Rating Prediction ◽  
Tv Show

The TV show rating analysis and prediction system can collect and transmit information more quickly and quickly upload the information to the database. The convolutional neural network is a multilayer neural network structure that simulates the operating mechanism of biological vision systems. It is a neural network composed of multiple convolutional layers and downsampling layers sequentially connected. It can obtain useful feature descriptions from original data and is an effective method to extract features from data. At present, convolutional neural networks have become a research hotspot in speech recognition, image recognition and classification, natural language processing, and other fields and have been widely and successfully applied in these fields. Therefore, this paper introduces the convolutional neural network structure to predict the TV program rating data. First, it briefly introduces artificial neural networks and deep learning methods and focuses on the algorithm principles of convolutional neural networks and support vector machines. Then, we improve the convolutional neural network to fit the TV program rating data and finally apply the two prediction models to the TV program rating data prediction. We improve the convolutional neural network TV program rating prediction model and combine the advantages of the convolutional neural network to extract effective features and good classification and prediction capabilities to improve the prediction accuracy. Through simulation comparison, we verify the feasibility and effectiveness of the TV program rating prediction model given in this article.

Estimation of Infrastructure Transition Probabilities from Condition Rating Data

1995 ◽  
Vol 1 (2) ◽  
pp. 120-125 ◽  
Author(s):  
Samer Madanat ◽  
Rabi Mishalani ◽  
Wan Hashim Wan Ibrahim
Keyword(s):  
Transition Probabilities ◽  
Rating Data ◽  
Condition Rating

scholarly journals Knowledge-Based Fuzzy Expert System to the Condition Assessment of Historic Road Bridges

2021 ◽  
Vol 11 (3) ◽  
pp. 1021
Author(s):  
Katarina Rogulj ◽  
Jelena Kilić Pamuković ◽  
Nikša Jajac
Keyword(s):  
Expert System ◽  
Geometric Mean ◽  
Condition Assessment ◽  
Analytic Hierarchy ◽  
Centroid Method ◽  
Assessment Index ◽  
Knowledge Based ◽  
Condition Rating ◽  
Bridge Condition Assessment ◽  
Bridge Condition Rating

A systematic methodology for condition assessment of the historic road bridges was needed because of the poor and inadequate condition of bridges which cannot satisfy everyday-day dynamic loads and deteriorations due to the aging process. Thus in this study, a new expert system based on the knowledge approach has been proposed to develop a systematic procedure for condition assessment of these bridges using fuzzy logic and sets of α-cuts. Each bridge is divided into three components: superstructure, substructure, and equipment, and each component is divided into relevant elements. These elements are evaluated by an expert and their ratings are fuzzified according to defined fuzzy sets, their membership functions, and linguistic values. Furthermore, fuzzy structural importance is given to ratings of each element. Combinations of these two values are calculated to obtain a fuzzy rating of the component using the Fuzzy Weighted Geometric Mean (FWGM). Finally, for the defuzzification of the component rating, the centroid method is proposed. The Analytic Hierarchy Process (AHP) is used for comparison of the components. The bridge condition rating is achieved by summering all the components ratings multiplied by their relative importance, and it is presented as a value of the Historic Road Bridge Condition Assessment Index (HRBCAI). The validation is conducted on the bridges built until the end of the Austro-Hungarian Monarchy in Split-Dalmatia County, Croatia.

scholarly journals PENILAIAN KONDISI JEMBATAN MENGGUNAKAN BRIDGE MANAGEMENT SYSTEM (BMS) DAN BRIDGE CONDITION RATING (BCR)

2020 ◽  
Vol 3 (1) ◽  
pp. 443-451
Author(s):  
Wilhman Harywijaya ◽  
Mochammad Afifuddin ◽  
Muhammad Isya
Keyword(s):  
Management Control ◽  
Bridge Management ◽  
Well Performance ◽  
The Road ◽  
Condition Rating ◽  
Bcr Method ◽  
Bridge Damage ◽  
Bridge Inspections ◽  
On The Road ◽  
Bridge Condition Rating

The bridge assesment and checking is an effort to gain well performance and vouch the decline condition of the bridge to ensure the bridges could be restored back under it stability basedits performance. Nowadays, the bridge inspections were carried out independently by the Perencanaan dan Pengawasan Jalan Nasional (P2JN) by using the Bridge Management control System (BMS). Referring to the latest bridge indicators, the bridge checks are carried out by tender system and consultants. The aim of this study is to check the bridge damage by isnpecting the condition value and damage code in the field by using BMS and Bridge Condition Rating (BCR) in order to obtain the accuracy in bridge maintenance. This research was conducted on 4 Bridges on the road Kr. Raya-BTS. Banda Aceh and Lambaro-BTS. Pidie, those are Kr. Angan bridge (STA. 013 + 400), Kr. Inong bridge (STA. 040 + 600), Kr. Geunapet A bridge (STA. 063 + 700), and Kr. Geunapet B bridge (STA. 063 + 700). Based on the BMS method, the writer found that Kr. Angan bridge was in 0 condition value,  the condition value for Kr. Inong bridge was  2,  the condition value for Kr. Geunapet A bridge was 0 and Kr. Geunapet B bridge was 0. Based on the results by using BCR method, it can be seen that the condition value for Kr. Angan bridge was 5.36, 5,52 for Kr. Inong bridge, 5,28 for the Kr. Geunapet A and 5,28 for Kr. Geunapet B. In the method of BMS the assessment of the condition of the bridge from good to bad starts from a small to large values namely from 0 to 5, while in the method of crack the assessment of conditions starting from bad to good is 7 to 1. Furthermore, referring to the  BMS and BCR comparison, it could be concluded that both BMS and BCR needed the same proposed treatment in form of regular and periodic maintenance. For some components of the bridge handling is needed in the form of repairs such as repairing cracks in the concrete, as well as repairs to the expansion joint. While in the BCR method there was no assessment to handle floor drainage systems, backrests and safety building.

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