# Author: Jojok Widodo Soetjipto Tri Joko Wahyu Adi, ST., MT., Ph.D - PowerPoint PPT Presentation

## The 4 th International Conference on Rehabilitation and Maintenance in Civil Engineering (ICRMCE-2018) Solo-Indonesia in July 11-12, 2018 Author: Jojok Widodo Soetjipto Tri Joko Wahyu Adi, ST., MT., Ph.D Prof. Dr. Ir. Nadjadji Anwar, M.Sc Do

1. The 4 th International Conference on Rehabilitation and Maintenance in Civil Engineering (ICRMCE-2018) Solo-Indonesia in July 11-12, 2018 Author: Jojok Widodo Soetjipto Tri Joko Wahyu Adi, ST., MT., Ph.D Prof. Dr. Ir. Nadjadji Anwar, M.Sc

2. Do you think they have no inspection for maintenance????

3. National bridge condition at 2012 District Background: 54.000 16.509 Amount of Bridge 18.491 89.000 National Province National 392 269 Damage: 32.5% 390 Length of Bridge 1.050 km Collapse: 1.5% Province District

4. 1. Data collecting: BMS from Directorate of Bridge, Data Directorate General of Bina Collec- Marga, the Ministry of Public ting Works and Housing. 2. Data mining: to find the pattern Data of data Mining dynamic 3. Model DB: to predict the bayesian probability of an event based on previous event

5. 1. Use 3.166 bridges (reinforced- Failure concrete girder bridge) from Condition 2013-2015: 80% (for modeling) 5 0% and 20% (for calibration) 4 20% Fail 2. The assessment BMS consists of: 3 40% structure, damage, volume, function, and influence → 2 60% Moderate Bridge has range value 0-5 1 80% 3. For further analysis, this scale Good 100% 0 must change to state condition BMS scale Condition Good Probability (G: Good, M: Moderate, F: Fail) State Condition ratting

6. 1. 1. 𝑞(𝑧) = ׬ 𝑞(𝜄)𝑞 𝑧 𝜄 𝑒𝜄 Deck 2. Make the DAG (Direct Acyclic Graph) 3. Estimate CPT (Conditional Probability Table) 4. Dynamic Bayesian Network (DBN) Beam Abutm Bridge ent

7. A graphical model makes a probabilistic relationship among variables

8. the CPT is arranged in several stages: Deck Probability Step1: Verifying the I-BMS data G 0.632 especially Reinforced-Concrete M 0.357 Bridge with spans of 10 to 25 metres F 0.011 Step 2: Giving random numbers on each bridge data and then sorting its Deck G M F data to divide into 2 groups, i.e. 80% data for the model and 20% G 0.873 0.707 0.753 for data testing. Step 3: CPT is calculated based on the Beam M 0.123 0.289 0.141 80% data model using the F 0.003 0.004 0.106 formulas (1) and (2)

9. The DBN consists 4 of several parts of the BNs, each of it represent ing a 1 system in a slice of time

10. DBN model is simulated using GeNIe 2.1 software

11. The graph of condition • probability of bridge and its component based on I-BMS The result of the simulation are: • 1. Probability of Bridge is strongly influenced by the probability of Beam and Abutment 2. Probability of Deck has a very small effect on the probability of Bridge

12. Bridge Deck Beam Abutment Bridge (data) To validate the (model) Year 0 1 st 2 nd 0 1 st 2 nd 0 1 st 2 nd 0 1 st 2 nd 0 1 st 2 nd model and Bridge calculate the 1 G G G G G G G M M G M M G G G model accuracy 2 G G G G G G G G G G G G G G G is used a 3 G G G M M M G G G M M M M M M 4 M M M G M M G G G G M M G G G “match/ no 5 G M M G M M G G G G M M G G G match” 6 M M M M M M M M M M M M M M M approach. 7 M G F G G G M G G M G M M M M 8 G G G G M M F F F F F F F F F No Match 9 M M G M M M G G G M M M M M M 10 G G G G G G G G G G G G G G G 11 M G M G G G G G G G G G G G G

13. Bridge Deck Beam Abutment Bridge (data) (model) Year 0 1 st 2 nd 0 1 st 2 nd 0 1 st 2 nd 0 1 st 2 nd 0 1 st 2 nd Bridge 12 G G M G G G G G G G G G G G G 13 G G G M G G G G G M G G M M M 14 G G G G G G G G G G G G G G G 15 M G M G G M G G M G G M G G M 16 G G G G G G G G G G G G G G G 17 G G G G G G G G G G G G G G G 18 G G G G G G G G G G G G G G G 19 G M M G G G G G G G G G G G G 20 G G G G G G G G G G G G G G G Percentage of Accuracy (%) 100% 80% 80%

14. Scenario Deck Beam Abutment Scenario intended G G G to study the effect 1 M G G of behavior changes F G G of bridge component G G G 2 G M G conditions. G F G G G G 3 G G M G G F

15. Deck is changed from G to F 1 1 1 Good (Deck=F) Good (Deck=M) Good (Deck=G;Beam=G;Abutment=G) Moderate (Deck=M) Moderate (Deck=F) Moderate (Deck=G;Beam=G;Abutment=G) Fair (Deck=F) Fair (Deck=M) 0.8 0.8 0.8 Fair (Deck=G;Beam=G;Abutment=G) 0.6 0.6 0.6 Probability Probability Probability 0.4 0.4 0.4 0.2 0.2 0.2 0 0 0 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 Years Years Years The Bridge condition is still “Good” even though the condition of the Deck is “Moderate”. The “Fail” Deck condition can change the bridge condition to be “Moderate”.

16. Beam is changed from G to F 1 1 1 Good (Beam=F) Good (Deck=G;Beam=G;Abutment=G) Moderate (Beam=F) Moderate (Deck=G;Beam=G;Abutment=G) Fair (Beam=F) 0.8 0.8 0.8 Fair (Deck=G;Beam=G;Abutment=G) 0.6 0.6 0.6 Probability Probability Probability Good (Beam=M) Moderate (Beam=M) Fair (Beam=M) 0.4 0.4 0.4 0.2 0.2 0.2 0 0 0 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 Years Years Years The Bridge condition is strongly influenced by the Beam condition even though the deck and abutment conditions are “Good”. This indicates that the effect of the Beam condition on the Bridge condition is very dominant.

17. Abutment is changed from G to F 1 1 1 Good (Deck=G;Beam=G;Abutment=G) Moderate (Deck=G;Beam=G;Abutment=G) 0.8 0.8 0.8 Fair (Deck=G;Beam=G;Abutment=G) 0.6 0.6 0.6 Probability Probability Probability Good (Abutment=M) Good (Abutment=F) Moderate (Abutment=M) Moderate (Abutment=F) Fair (Abutment=M) Fair (Abutment=F) 0.4 0.4 0.4 0.2 0.2 0.2 0 0 0 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 Years Years Years Bridge condition strongly influenced by Abutment condition also Abutment condition changed to “Fail”, has an anomaly condition in the second year and later. This anomaly condition is estimated due to limited data changes in the Fail's Bridge condition.

18. 2. Recommendation : 1. Conclusion : a. The Dynamic Bayesian Updating Approach a. The Dynamic Bayesian can also be used as a guide for the Updating Approach can be maintenance and operation strategy of the used to assess the Bridge bridge. condition accurately. b. To prevent the sudden collapse of the b. Each bridge component bridge, should pay very serious attention to contributes to determining the damage protection of abutments and the Bridge condition that beams. the effect on the Bridge condition is provided by, c. The model can also be used as an early from largest to smallest, warning system to prevent bridge failure, the Abutment, Beam and even though the model accuracy still needs Deck. to be improved.