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  1. 1. i A TECHNICAL SEMINAR REPORT ON STRUCTURAL HEALTH MONITORING OF BRIDGES USING ARTIFICIAL NEURAL NETWORK. Submitted in partial fulfillment for the award of the degree of BACHELOR OF TECHNOLOGY in CIVIL ENGINEERING Submitted by J.SREEJA 19B81A01B2 Under the Guidance of Mr. T.MANOJ Assistant Professor Department of Civil Engineering CVR COLLEGE OF ENGINEERING ACCREDITED BY NBA, AICTE & Affiliated to JNTUH Vastunagar, Mangalpalli (V) Ibrahimpatnam (M), R.R. District, PIN – 501 510
  2. 2. ii CVR COLLEGE OF ENGINEERING ACCREDITED BY NBA, AICTE &Affiliated to JNTUH Vastunagar, Mangalpalli (V), Ibrahimpatnam (M), R.R. District, PIN – 501 510 Web:, email: Department of Civil Engineering CERTIFICATE This is to certify that the seminar report titled “STRUCTURAL HEALTH MONITORING OF BRIDGES USING ARTIFICIAL NEURAL NETWORK. ” is a bonafide work done as Technical Seminar and submitted by J.SREEJA 19B81A01B2 in partial fulfillment of requirement for the award of Bachelor of Technology degree in Civil Engineering, CVR College of Engineering, Ibrahimpatnam. This work has not been submitted to any university or institution for the award of any degree or diploma. Technical Seminar Supervisor Head of the Department Mr. T.MANOJ Dr. T. Muralidhara Rao Assistant Professor
  3. 3. iii ACKNOWLEDGEMENT I owe a great many thanks to a great many people who helped and supported me during the Technical Seminar work. I express my earnest gratitude to my technical seminar supervisor, Mr. T.MANOJ, Assistant Professor, Department of Civil Engineering, for his constant support, encouragement and guidance. I am grateful for his cooperation and his valuable suggestions. I express my thanks to my technical seminar coordinators, Mr. M. Ashok Kumar (Assistant Professor) and Mr. K. Mahesh (Assistant Professor), Department of Civil Engineering for the encouragement and support given to me. I also express my thanks to Dr. T. Muralidhara Rao, Head of the Department of Civil Engineering for the encouragement and support given to me. I express my gratitude to Dr. Ram Mohan Reddy, Principal, CVR college of engineering for the encouragement. Finally, I express my gratitude to all other members who are involved either directly or indirectly for the successful completion of this seminar.
  4. 4. iv DECLARATION I, the undersigned, declare that the seminar entitled “STRUCTURAL HEALTH MONITORING OF BRIDGES USING ARTIFICIAL NEURAL NETWORK. ”, being submitted in partial fulfillment for the award of Bachelor of Technology Degree in Civil Engineering, affiliated to Jawaharlal Nehru Technological University Hyderabad, is the work carried out by me. J.SREEJA 19B81A01B2
  5. 5. v ABSTRACT The condition of a bridge is critical in quality evaluations and justifying the significant costs incurred by maintaining and repairing bridge infrastructures. Using bridge management systems, the department of transportation in the United States is currently supervising the construction and renovations of thousands of bridges. The inability to obtain funding for the current infrastructures, such that they comply with the requirements identified as part of maintenance, repair, and rehabilitation (MR&R), makes such bridge management systems critical. Bridge management systems facilitate decision making about handling bridge deterioration using an efficient model that accurately predicts bridge condition ratings. The accuracy of this model can facilitate MR&R planning and is used to confirm funds allocated to repair and maintain the bridge network management system. In this study, an artificial neural network (ANN) model is developed to improve the bridge management system (BMS) by improving the prediction accuracy of the deterioration of bridge decks, superstructures, and substructures. A large dataset of historical bridge condition assessment data was used to train and test the proposed ANN models for the deck, superstructure, and substructure components, and the accuracy of these models was 90%, 90%, and 89% on the testing set, respectively. Keywords: bridge deterioration; bridge condition; artificial neural network; machine learning; condition prediction.
  7. 7. 1 CHAPTER 1 INTRODUCTION 1.1. Introduction to Structural health monitoring Structural health monitoring (SHM) has the potential to transform the bridge engineering industry by providing stakeholders with additional information to inform decisions about the design, operation, and management of bridges throughout the structures lifespans. This chapter gives guidance on SHM for engineers who design, build, operate, and maintain bridges. There remain numerous technical challenges to overcome when deploying SHM systems; however the most important issues to consider are how to decide what information is required, and then how to develop a strategy to deliver this information in a form that is easy to interpret and can inform decision making. The structural health monitoring process includes installing sensors, data acquisition, data transfer, and diagnostics through which the structure's safety, strength, integrity, and performance are monitored. If overloading or any other defects are observed, proper correction measures are suggested. Fig 1.1 Introduction to SHM 1.1.1. Purpose of Structural Health Monitoring 1. Improve performance (safety and functionality) of existing structures.
  8. 8. 2 2. The placement of sensors during construction works enables observers to assess the structure's condition and specify its remaining life span. 3. Evaluate the integrity of a structure after earthquakes. 4. Structural monitoring and assessment are essential for on-time and cost-effective maintenance. So, it reduces construction work and increases maintenance activities. 5. The SHM process collects data on the realistic performance of structures. This data can help design better structures in the future. 6. Shift towards a performance-based design philosophy. Fig 1.2 bridge health monitoring 1.1.2. Components of Structural Health Monitoring System The structural health monitoring system consists of several components which are presented schematically in figure and discussed below: i. Structure The critical structures like bridges, tunnels, dams, and wind turbines are mostly monitored as they are a vital part of the national infrastructure. ii. Data Acquisition System Data acquisition addresses the number and type of sensors, how to activate sensors, and techniques to save data. The placement of sensors should not alter the behavior of the structure. This can be achieved by considering the placement of wiring, boxing, etc., at the design stage.
  9. 9. 3 Fig 1.3 components Sensors need to be appropriate and robust and serve their function adequately for a specified duration. Each sensor may evaluate a particular aspect of the structure. They measure strain, deflection, rotation, temperature, corrosion, prestressing, etc. Several types of sensors are available, such as those provided in Table-1, to be employed, but fiber optic sensors are the latest ones suitable for infrastructure. Table-1: Measurements of Structural Response Using Various Sensors Measurement of structural aspects Measuring device Measuring device output Reasons Loads Load cells Assess the magnitude and distribution of forces on the structure. To check whether the loads are expected and how they are distributed on the structure. Deformation Transducers Deflection To ensure whether deflection is within the tolerable limit or not. Otherwise, repair and rehabilitation may be needed. Strain Strain gauges Magnitude and variation of strains To check the safety and integrity of the structure.
  10. 10. 4 Temperature Thermistors, thermocouples, integrated temperature circuits Temperature and temperature variations Temperature variation causes thermal expansion, and repeated cycles can damage structures. It affects strains. Acceleration Accelerometer Acceleration of structure under loads, especially in a seismic prone area. To examine how the structure resists acceleration and subsequent loads. Wind speed and pressure Anemometer Measure speed and pressure at various locations. Wind load can govern the design of long-span bridges and tall buildings. Acoustic emission Microphone Measure noises and determine the location of the noise using triangulation. To detect the breakage of elements in a structure and determine its location. It is highly applicable in prestressed members and cable- stayed bridges. Video monitoring Internet camera technology Record videos of extreme conditions of the structure Record extreme loadings and detect overloaded trucks on bridges. iii. Data Transfer The transfer of data can be done through a wire which is common and cost-effective but may not be practical for large structures. Alternatively, wireless communication can be considered, which is suitable for large structures, but it is slower and more expensive than the wired method. Telephone lines are another option to transfer data from site to the offsite offices. These data transfer techniques eliminate the need to visit the field for collecting and transferring data. Fig 1.4 data transfer
  11. 11. 5 iv. Digital Processing After the data is transferred, digital processing is carried out to eliminate unwanted effects such as noises. It should be carried out before the information is stored. Digital processing will make the interpretation of data easier, faster, and more accurate. v. Storage of Data The processed data can be stored for a long time and retrieved in the future for analysis and interpretation. vi. Data Diagnostics Diagnostic processes involve the conversion of abstract data to useful information about the structure's condition and its responses to loads. So, the final data obtained from structural health monitoring should be detailed and physical, based on which rational and knowledge-based engineering decisions can be made. The methodology used for the diagnostics process is dependent on the structure type, location and types of sensors, monitoring purpose, and structural response under consideration. 1.1.3. Structural Health Monitoring Testing Categories Testing categories of the structural health monitoring system can be classified as follows: Based on a timescale of monitoring: 1. Continuous testing 2. Periodic testing Based on the manner the response is invoked in the structure: 3. Static load 4. Dynamic load 5. Ambient vibrations 1.1.4. Advantages of Structural Health Monitoring  Improved understanding of field structural behavior.  Detect damages at an early age of problem initiation.  Reduced inspection and repair time.  Encourage the use of innovative materials.  Help to develop rational management and maintenance strategies. 1.1.5. Disadvantages  High installation costs.
  12. 12. 6  Vulnerable to ambient noise corruption.  Vulnerable to earthquake conditions.  Challenges with the application of SHM like building accessibility, manipulating the huge amount of data generated by sensors, environmental conditions, etc.  The size and complexity of large structures need a great number of sensing points to be installed. 1.2. ARTIFICIAL NEURAL NETWORK Neural networks, also known as artificial neural networks (ANNs) or simulated neural networks (SNNs), are a subset of machine learning and are at the heart of deep learning algorithms. Their name and structure are inspired by the human brain, mimicking the way that biological neurons signal to one another. Artificial neural networks (ANNs) are comprised of a node layers, containing an input layer, one or more hidden layers, and an output layer. Each node, or artificial neuron, connects to another and has an associated weight and threshold. If the output of any individual node is above the specified threshold value, that node is activated, sending data to the next layer of the network. Otherwise, no data is passed along to the next layer of the network. Fig 1.5 artificial neural networks
  13. 13. 7 Neural networks rely on training data to learn and improve their accuracy over time. However, once these learning algorithms are fine-tuned for accuracy, they are powerful tools in computer science and artificial intelligence, allowing us to classify and cluster data at a high velocity. Tasks in speech recognition or image recognition can take minutes versus hours when compared to the manual identification by human experts. One of the most well-known neural networks is Google’s search algorithm. 1.2.1 Artificial Intelligence into Civil Engineering Artificial Intelligence (AI) is a specialized system that can recognize intelligent entities, make decision-making easier, faster, and more efficient. Artificial intelligence is concerned with the roboticization of intelligent behaviour that thinks and acts the same way people do. Artificial intelligence is a broad concept that has become firmly ingrained in our daily lives. It is built on the collaboration of numerous fields, including computer science, cybernetics, information theory, psychology, and neurophysiology, among others. As a result, artificial intelligence is a discipline of science concerned with the study, design, and implementation of time-saving technologies. AI is concerned with machines that carry out tasks. Artificial Intelligence is mostly used in civil engineering applications like construction management, building materials, hydraulic optimization, geotechnical and transportation engineering, and is also useful in developing robots and automated systems. AI models in civil engineering can be used for accurate, cheaper, and less disruptive construction projects. In modern structures, artificial intelligence is being utilised to plan the routing of electrical and plumbing systems. Artificial intelligence (AI) is being used to track real-time interactions between personnel, machinery, and items on the job site and supervisors for potential safety hazards, construction errors, and productivity concerns. Simulated intelligence makes it simpler for those who engage with the development business by making it more sensible. It gives more open doors in a structural design by making it an appealing field of work. On a fundamental level, AI is a technology that enables machines to execute tasks commonly undertaken by humans. In this technology, machines are programmed to examine data streams to discover new information, and/or perform routine operations. From a civil engineering perspective, AI can enable civil engineers to uncover new knowledge hidden in our systems (i.e., in the form of new theories, finding patterns that
  14. 14. 8 may help establish hypotheses, and so forth) and/or perform computationally structured or heuristic processes (e.g., design beams, identify commuter patterns, and so forth). A key point to remember is that although civil engineers often develop automated spreadsheets, such spreadsheets are built to follow existing codal provisions or established prior engineering judgment. On the other hand, automation via AI is not bound by the availability of existing codal procedures. 1.2.2 Advantages of AI in Civil Engineering  AI is used to Prevent Cost Overruns.  AI is used to Reduce the Risk of Accidents.  AI empowers contractors and project managers to monitor and prioritize risks on the job site and allows the team to focus on them.  AI is used for Efficient Project Planning.  AI is used to Increase Productivity on Job Sites
  15. 15. 9 CHAPTER 2 LITERATURE REVIEW Literatures collected for this present seminar is presented below: SN O. Title of the paper Authors Name of the Publisher and Year of Publication Tests conducted/ Modelling Resultsand Discussions Important Conclusions  1.  Developing Bridge Dete rioration M odels Using an Artificial Neural Net work Essam A lthaqaf And Eddie Chou  Marinella Fossetti and Mi G. Chorz epa 2022   ANN models were developed to predict the condition ratings of three different bridge components, i.e., the bridge deck, superstructure, and substructure.  TensorFlow and Keras were used to implement the ANN models, and these models were trained and tested on Google’s Colab platform.  the data used to formulate the deterioration models were sourced from the National Bridge Inventory (NBI) d atabase.  For deck model, input layer containi ng 11 neurons, 7 hidden layers and an output layer 1 neuron obtained the overall smallest MAE value (0.10)a nd the highest R2 value(0.8 9). For superstructur e and substruct ure models had an input layer 11 neurons, six hidden layers & output layer 1 neuron obt ained the lowest MAE value(0.10 & 0.11) and the highest R2 value((0.89 & 0.88) ANN architecture to obtain optimal accuracy for a given number of hidden layers and neurons. the accuracy of the final model trained was f ound to be greater than 89% on the NBI dataset
  16. 16. 10 SN O. Title of the paper Authors Name of the Publisher and Year of Publication Tests conducted/ Modelling Resultsand Discussions Important Conclusions 2.  The Health Mo nitoring of Bridges using Artif cial Neural Net works.  Pei-Ling Liu and Shyh- Jang Sun Journal of Mechani cs 2011  The longitudinal e longations at the bottom of the 9 zones are used as the input features of the monitoring system. The amplitude of a curve can by represented by the maximum or minimum of the curve. Because of geometrical sym metry, o. ne may develop an ANN system using the 18 extreme elongations as the input vector. Based on observat ions, the 9 damage zones are divided into five groups: (1L), (1M, 2L), (1R, 2M, 3L), (2R, 3M) and (3R). The extreme elongations of the undamag ed bridge, meas ured in advance, are used as a baseline for comparison. For Single Zone Damag ed, 163 test cases, errors of the output ratios are less than 5%. For Two Zone Damag ed, 75 cases, errors of the output ratios are les s than 10%. The longitudinal e longations of a bridge subjec ted to a moving truck can reflect the damage condition of the bridge The monitoring system devel oped in this study can detect the locations and levels of damages accu rately. The system is effective even in the case of multiple damages or partial damage.
  17. 17. 11 SNO. Title of the paper Authors Name of the Publisher and Year of Publication Tests conducted/ Modelling Resultsand Discussions Important Conclusions  3.  Archetypal Use of Artif icial Intellig ence for Br idge Struct ural Monit oring Bernardi no Chiai a and Vale rio De B iagi  DISEG, De partment of Structural 2020  Generation of a synthetic dataset then the training of learning tools to determine whether a structur e is damaged and whe re the damage is located . Evaluation of disp lacements and vib ration frequencies of un damaged and damaged stru ctural members onto wh ich a moving load is acting was first performed on a simply supporte d beam. A set of 30k simulations w ith moving load were performed u sing Matlab. The analys es showed a large vari ety of trends and dependenci es on the accurac y of the esti mated frequ encies and measured r otation A confusio n matrix is pl oted. The testing data set was constituted by 1455 undamaged and 1545 damaged be ams, for a total of 3000 struct ures. A contour plot of the error for the conside red ranges of variables is made. the finer the evaluation of the freque ncies, the mo re precise the a mount of damage. Tests on a simply sup ported beam subjected to damage prov ided interesti ng insights in to the possibilit y of impleme nting a hybri d method.
  18. 18. 12 SN O. Title of the paper Authors Name of the Publisher and Year of Publication Tests conducted/ Modelling Resultsand Discussions Important Conclusions  4.  Estimating Bridge Dete rioration Age Using Artifi cial Neural Networks  Aseel H ussein a nd Abid Ab u Tair Internationa l Journal of Engineer ing and Tec hnology Feb 2019  Feedforward Neur al Network is selected. dataset has huge information about the 400 bridges with 60 years of defect histories four important parameters to design a network- Learning Algorith m, Number of hidden layers ,Number of Neurons, Trans fer Function. Factorial des ign of (24) is used on ANN design para meters with the defects age as the target. measure the difference between targ ets (true age) and outputs (ANN age) ten neurons is better for ANN outcome. The neural network results reveal that most of the models had similar range in the similarity and differences . Three validation methods were taken to measure the performanc e of ANN, Mean Square Error.  5. Applicatio n Researc h of Neural Network Algorithm in Bridge Health Sta tus Monit oring Syst em   Zhang Q ingchun ,Lu Ying hu ,Gan Ha oyu ,Wang Ji nya Internationa l Conferenc e on Robots & In telligent Sy stem (ICRI S) 2019  Sensor nodes collect bridge operation parameters. The measured data are uploaded to OneNET Internet of Things cloud service platform through GPRS and EDP protocol provided by Internet of Things cloud platform Android APP The monitoring s oftware is developed by Python language. The interface is mainly based on TabWidg et tab layout, UI design is carried out through QT Designer, and UI files in XML format are generate d. PyQtgraph is used to draw icons to make data viewing mor e intuitive. the accuracy is above 80%, so 80% is set as the threshold. When the health status is more than 80%, the bridge is judged to be healthy. when the maintenance st atus is more than 70%, the notice is sent to inform the staff to take measures for maintenanc e.
  19. 19. 13 SN O. Title of the paper Authors Name of the Publisher and Year of Publication Tests conducted/ Modelling Resultsand Discussions Important Conclusions  6. Artificial Neural Ne twork Model of Bridge De terioration   Ying- Hua Hua ng ASCE 2010  records of 942 decks with 2,300 inspection record s that have been treated with concrete overlay were studied. Two null hypotheses were tested. Records of the condition of 1,241 decks with no maintenance were used to identify and avoid the influences of maintenance on the condition of decks. for data set, any number of hidden layers greater than 5 does not yield better results. the average training classif ication rate increases when the number of hidden neuron s increases the number of neurons increases. The best average testing classification r ate obtained, 75.3 9% study develops an ANN predicti on model for deck deterior ation by adopting BP- MLP classifier. These results indicate the potential of the ANN model with BP- MLP classifie r as a prediction too l. The 11 factors identified as statistically significant  7. Artificial Neural Net work Model for Bridge Deteriorati on and Assessment Gasser Galal Al i,Amr El sayegh, Rayan H . Assaad   Laval (Greate r Montreal)  2019 Software used is the programming language, Cran R,used to train neural networks Data Collection fr om the US. Department of Transportation, Federal Highway Administration (F HWA 2018) properties that are relevant to the condition. Several NN configurati ons were tested, the residuals of those configur ations were investigated, a nd comparisons w ith the LM were performe d, in order to select the best model. It can be seen that as the complexity of the NN increases. investigated s everal NN that output the condition of bridge elements base d on selected para meters. The develope d model was compared with a LM & found that both methods have almost the same acc uracy. However, the accuracy of the NN Good
  20. 20. 14 SN O. Title of the paper Authors Name of the Publisher and Year of Publication Tests conducted/ Modelling Resultsand Discussions Important Conclusions  8. Structural health mon itoring of bridges: a model- free ANN- based appr oach to damage detection  A. C. Neves, I. Gonza ´lez2 , J. Lean der1 Springer 2017  The first stage of damage identifica tion uses methods which provide a qualitative indicat ion of the presence of damage in the structure. A ROC curve is a two-dimensional graphic in which the true positive rate (TPr) is plotted against the false positive rate (FPr) for a given threshold. Root Mean Squared Error (RMSE) is one way of evaluating the performance of the trained &Com pared to the simple Mean Absolut e Error the sensors situated closer to the geometric middle of the bridge seem to be associated with networks that yield a better separation bet ween structural state s the use of past recorded deck accelerations in the bridge as input to an Artificial Neural Network that, after being properly trained, is able to predict forthcomi ng accelerations the statistical evaluation of the prediction errors of the network by means of a Gaussian Process, after which one can select the detection threshold in regard to a Damage Index.
  21. 21. 15 SN O. Title of the paper Authors Name of the Publisher and Year of Publication Tests conducted/ Modelling Resultsand Discussions Important Conclusions  9.  Application of Neural Networks in Bridge Health Pred iction based on Accelera tion and Displac ement Data Domai n Reni Suryanit a and Azlan Adna  IMECS 2013  The best performances of BPNN depend on the selection of suitable initial weight, learning rate, momentum, networks architect ure model and activation function. The architecture model for this system has n number of input neurons, one and two hidden layers with n neurons and an output. output layer is the level of a bridge health condition due to an earthquake, to implement the ANN models, and these models were trained and tested on Google’s Colab platform. The bridge health syste m used several sens ors to detect the behavio r of a bridge su ch as bridge d eformation and damage The sensors con nected to data logger and sent the informa tion data such as displaceme nt and acceler ation to the local se rver. The architecture of neural network me thod in this study used one and two hidden layers. models with one hidden layer for accelerati on data domain had the discrepan cy of the MSE validation. The compari son of acceleratio n and displace ment data domain for one and two hidden layers’ mode l has been conclud ed based on MSE mean value, regres sion mean value and CPU time of the network mod el. Both compar isons show the MSE mean value decreas es since the epoch increases.
  22. 22. 16 CHAPTER 3 OBJECTIVES  To measure the extent of structure deterioration.  To predict structure's failure in advance.  To apply techniques of neural network in structural health monitoring.  To analyse the efficiency of ANN model in predicting the failure of structure.  To enhance the conventional technique of damage detection by introducing ANN techniques.
  23. 23. 17 CHAPTER 4 METHODOLOGY 4.1 ARCHITECTURE OF NEURAL NETWORK An ANN model uses different mathematical layers to learn various features in the data being processed. Typically, an ANN model comprises thousands to tens of thousands of manmade neurons arranged in layers as units. The input layer is designed to receive multiple types of data and extract various features in the data. The extracted information is then passed to a hidden layer. The feature learning and transformation processes occur in a hidden layer, and the output layer converts the processed information to its original format to facilitate easy interpretation. However, due to the exponential increase in computational power over the last few years, ANN models have evolved to incorporate multiple hidden layers. In addition, many design optimizations, e.g., architectural variations, and preprocessing techniques are being incorporated into these models. Consequently, these advanced ANN models can produce highly accurate results; thus, ANN researchers can improve their models by performing a variety of simulations based on combining a set of hyperparameters, e.g., the total number of layers (i.e., up to 14 hidden layers) and neurons (i.e., up to 512 neurons), activation functions, biases, and weights. In this study, TensorFlow and Keras were used to implement the ANN models, and these models were trained and tested on Google’s Colab platform, which has the computational power of several graphical processing units. As a result, the optimal model that can provide the most valuable results can be realized.  It is used to determine both the input size and output size.  The aim is to identify the optimal number of hidden layers and the optimal number of neurons in each hidden layer.  This evaluation is conducted after executing various systematic variations to identify the optimal number of hidden layers and the optimal number of neurons in each hidden layer.  Here, the goal was to select the ANN model with the optimal architecture.  The various networks affiliated with the deck, superstructure, and substructure models are trained and validated, where various nodes in the hidden layers are executed.  Two factors are considered for determining the architecture of neural network:
  24. 24. 18  The MAE metric was used to determine the prediction accuracy of the models. Essentially, MAE represents the mean (average) deviation of the predicted values from observation values.  The second set of results is related to the coefficient of determination, i.e., the R2 value, which represents a comparison of the mutual relationship between the predicted and real values.  The model with the smallest mean absolute error (MAE) and highest coefficient of determination (R2 ) simulate to determine the verification set in order to identify which deck, superstructure, and substructure models demonstrated the best performance.
  25. 25. 19 CHAPTER 5 CASE STUDY In this case study an overpass of Highway A21 “Torino-Piacenza-Brescia” in the Northwest of Italy is considered. Here, there are several overpasses that are coeval and have the same structural scheme, details, and techniques. The design of such infrastructures was performed by Dott. Ing. Gervaso. 5.1 DETERMINATION OF OPTIMAL MODEL First, a 32-neuron ANN model with a single hidden layer was subjected to the training and validation processes. Then, this model was modified to include two hidden layers with 16 and 32 neurons, respectively. In the subsequent configuration, the number of neurons in both hidden layers was increased by 32 and 256 neurons in each layer. Then, a third hidden layer was introduced, where the number of neurons in the first, second, and third hidden layers was 16, 32, and 64, respectively. In addition, the model with three hidden layers was subjected to training and validation, and the resulting number of neurons in the respective layers was 32, 256, and 512 neurons. Then, a configuration with four hidden layers was subjected to training and validation with 32, 256, 512, and 512 neurons, respectively. Fig 1.6 overpass of highway A21
  26. 26. 20 In the subsequent step, it is attempted to evaluate the effect of varying the number of layers. Here, the total number of layers was increased to 5, 6, 7, 8, 9, and 14, where the combinations of neurons were 32, 256, 512, 512, and 512, respectively. All the models with hidden layer counts beyond four layers were subjected to training and validation using 512 neurons. The architectures of the different models and their respective validation are detailed below. table 1. 2 model architecture The model with seven hidden layers obtained the smallest MAE value of 0.10 for the bridge deck model. In another case, the model with six hidden layers obtained MAE values of 0.10 and 0.11 for the bridge superstructure and substructure models, respectively Similar to the MAE analysis, the highest R2 value for the bridge deck model was obtained by the model with seven hidden layers (0.89). In addition, the highest R2 value for both the superstructure and substructure models was obtained by the models with six hidden layers (0.89 and 0.88). The deck model with an input layer containing 11 neurons, seven hidden layers with 32, 256, 512, 512, 512, 512, and 512 neurons, respectively, and an output layer with one neuron and a linear activation function obtained the overall smallest MAE value and the highest R2 value. In addition, the superstructure and substructure models had an input layer containing 11 neurons, six hidden layers with 32, 256, 512, 512, 512, and 512 neurons, respectively, and an output layer with a single neuron and a linear activation function obtained the lowest MAE value and the highest R2 value.
  27. 27. 21 A confusion matrix is applied to evaluate the performance of the ANN models in terms of predicting the condition ratings according to FHWA ratings for bridge conditions (FHWA, 1995) of the different bridge components.  Essentially, confusion matrices consist of tabular illustrations of MLP predicting capability  The models were trained and tested on Google’s Colab platform which has the computational power of several graphical processing units. table 1.3 FHWA ratings 5.2 CONSTRUCTION OF CONFUSION MATRIX The computation of functionality variables for algorithms was performed by evaluating the-  true positive (TP-the NN predicts damage on a structure that is really undamaged)  false positive (FP-the NN predicts damage on a structure that, on the contrary, is undamaged)  true negative (TN-the NN predicts no damage on a structure that is really undamaged)  false negative (FN-the NN predicts no damage on a structure that, on the contrary, is damaged)  In addition, accuracy, effectiveness, recall, the F1-score, and the macroaverage are investigated.
  28. 28. 22 Fig 1.7  The total number of true positives TP value represents the number of correct predicted values, i.e., the values lying in the diagonal of the confusion matrix.  The total number of false negatives FN for a class is the sum of values in the corresponding row (excluding the TP).  The total number of false positives FP for class is the sum of values in the corresponding column (excluding the TP).  The total number of true negatives TN for a certain class will be the sum of all columns and rows excluding that class's row and column. Fig 1.8 5.3 ANAYSIS OF MODEL  The parameters like accuracy, effectiveness, recall, the F1-score are calculated using following formulas.
  29. 29. 23 Confusion matrix for different segments of bridge are: table 1.4
  30. 30. 24  All three models obtained recall values that were greater than 81%, which implies that if a particular section of the bridge is characterized by repair issues (TP) and the corresponding model falsely predicts this (FN), then the bridge may suffer some damage conditions, particularly where the issue can affect other sections of the bridge.  The F1-score of all three bridge component models was greater than 85%, which means that the number of FPs and FNs was low. In addition, it signifies that the model has predicted both correct and incorrect predictions. From these results, we conclude that both the recall and precision of the models are significantly high.  The model obtained the highest accuracy (90%) for both the deck and superstructure components, and the model obtained 89% accuracy for the substructure component. These results imply that training was performed accurately with an error rate of approximately 10%.  According to the findings, the age and type of support have a high impact on the deck condition. In addition, the age and type of design have a high impact on the condition of the superstructure. Age and year built have great effects on the condition of the substructure, whereas the average daily traffic has the least impact on bridge condition.
  31. 31. 25 CHAPTER 6 CONCLUSIONS  ANN modelling techniques in AI can incorporate multiple parameters into a model that is both sophisticated and nonlinear. In terms of bridge engineering, an ANN model can be trained and tested on data available in the NBI database in order to predict the deterioration of a bridge.  The most appropriate ANN architecture to obtain optimal accuracy for a given number of hidden layers and neurons.  Finally, with the optimal ANN configuration, the proposed model is trained and tested for various components for the dataset.  Overall, the accuracy of the final model trained for the deck, substructure, and superstructure components was found to be greater than 89% on the NBI dataset. Similarly, the F1-score, recall, and precision values were all greater than 81%. Therefore, we consider that the proposed ANN models have allowed us to identify a strong correlation between the predicted values and actual values in the NBI dataset.  Finally, the proposed ANN models were used to develop a bridge deterioration model to predict deterioration in all bridge systems. Consequently, this model is perceived to assist in furnishing an ideal plan for bridge maintenance, scheduling any bridge with an imminent requirement to earmark appropriate funds for its maintenance and repair.
  32. 32. 26 CHAPTER 7 REFERENCES 1. American Society of Civil Engineers (ASCE). Infrastructure Report Card: A Comprehensive Assessment of America’s Infrastructure; ASCE: Reston, VA, USA, 2017. 2. Madanat, S.; Mishalani, R.; Ibrahim, W.H.W. Estimation of infrastructure transition probabilities from condition rating data. J. Infrastruct. Syst. 1995, 1, 120–125. 3. Thompson, P.D.; Small, E.P.; Johnson, M.; Marshall, A.R. The Pontis bridge management system. Struct. Eng. Int. 1998, 8, 303–308. 4. Thompson, P.D. Decision Support Analysis in Ontario’s New Bridge Management System. In Structures 2001: A Structural Engineering Odyssey; ASCE: Reston, VA, USA, 2001; pp. 1–2. 5. Urs, N.; Manthesh, B.S.; Jayaram, H.; Hegde, M.N. Residual life assessment of concrete structures-a review. Int. J. Eng. Tech. Res. 2015, 3, iss3. 6. Kayser, J.R.; Nowak, A.S. Reliability of corroded steel girder bridges. Struct. Saf. 1989, 6, 53–63. 7. Adams, T.M.; Sianipar, P.R.M. Project and network level bridge management. In Transportation Congress, Volumes 1 and 2: Civil Engineers—Key to the World’s Infrastructure; ASCE: Reston, VA, USA, 1995; pp. 1670–1681. 8. Morcous, G.; Rivard, H.; Hanna, A.M. Modeling bridge deterioration using case- based reasoning. J. Infrastruct. Syst. 2002, 8, 86–95. 9. Sanders, D.H.; Zhang, Y.J. Bridge deterioration models for states with small bridge inventories. Transp. Res. Rec. 1994, 1442, 101–109. 10. Jiang, Y.; Saito, M.; Sinha, K.C. Bridge Performance Prediction Model Using the Markov Chain, no. 1180; NASEM: Washington, DC, USA, 1988. 11. Tolliver, D.; Lu, P. Analysis of bridge deterioration rates: A case study of the northern plains region. J. Transp. Res. Forum. 2012, 50. 12. Chase, S.B.; Small, E.P.; Nutakor, C. An in-depth analysis of the national bridge inventory database utilizing data mining, GIS and advanced statistical methods. Transp. Res. Circ. 1999, 498, 1–17. 13. Morcous, G.; Lounis, Z.; Mirza, M.S. Identification of environmental categories for Markovian deterioration models of bridge decks. J. Bridg. Eng. 2003, 8, 353–361.
  33. 33. 27 14. Abdelkader, E.M.; Zayed, T.; Marzouk, M. A computerized hybrid Bayesian-based approach for modelling the deterioration of concrete bridge decks. Struct. Infrastruct. Eng. 2019, 15, 1178–1199. 15. Jiang, Y.; Sinha, K.C. Bridge service life prediction model using the Markov chain. Transp. Res. Rec. 1989, 1223, 24–30. 16. Lee, Y.; Chang, L.M. Econometric model for predicting deterioration of bridge deck expansion joints. Transp. Res. Circ. No. E-C049 2003, No.E-C049, 255–265. 17. Madanat, S.M.; Karlaftis, M.G.; McCarthy, P.S. Probabilistic infrastructure deterioration models with panel data. J. Infrastruct. Syst. 1997, 3, 4–9. 18. Kong, J.S.; Frangopol, D.M. Life-cycle reliability-based maintenance cost optimization of deteriorating structures with emphasis on bridges. J. Struct. Eng. 2003, 129, 818–828. 19. Saydam, D.; Bocchini, P.; Frangopol, D.M. Time-dependent risk associated with deterioration of highway bridge networks. Eng. Struct. 2013, 54, 221–233. 20. Frangopol, D.M.; Bocchini, P. Bridge network performance, maintenance and optimisation under uncertainty: Accomplishments and challenges. Struct. Infrastruct. Eng. 2012, 8, 341–356.