Neurocomputing in Civil Infrastructure

Amezquita-Sanchez, Juan P.; Valtierra-Rodríguez, Martín; Aldwaik, Mais; Adeli, Hojjat

doi:10.24200/sci.2016.2301

Cited by 74 publications

(80 citation statements)

References 55 publications

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“…Limongelli et al [27] have explored a noble experimental method for the early detection of damage in deteriorated bridge structure. Amezquita-Sanchez et al [28] have conducted a literature survey on the implementation of Artificial Neural Networks in the era of civil engineering for structural system credentials problem. Jena and Parhi [29][30] have carried out numerical along with FEA and experimental studies to determine the responses of different types of beam structures subjected to moving load.…”

Section: *Corresponding Authormentioning

confidence: 99%

Fault Detection in Cracked Structure under Moving Load using RNNs based Approach

Jena¹,

Parhi

2019

Scientia Iranica

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The current work is based on the development of an inverse approach in the domain of Recurrent Neural Networks (RNNs) to identify and quantify cracks on a multi-cracked cantilever beam structure subjected to transit mass. At first, the responses of the multicracked structure subjected to transit load are determined using fourth order Runge-Kutta numerical method and finite element analysis (FEA) has been executed using ANSYS software to authenticate the employed numerical method. The existences and positions of cracks are identified from the measured dynamic excitation of the structure. The crack severities are found out by FEA as forward problem. The modified Elman's Recurrent Neural Networks (ERNNs) approach has been implemented as inverse problem to predict the locations and severities of cracks in the structure by applying Levenberg-Marquardt (LM) back propagation algorithm. The present analogy has been carried out in a supervised manner to check the convergence of the proposed algorithm. The proposed ERNNs method converge good results with those of theory and FEA.

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Section: *Corresponding Authormentioning

confidence: 99%

Fault Detection in Cracked Structure under Moving Load using RNNs based Approach

Jena¹,

Parhi

2019

Scientia Iranica

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“…type or severity) can be used. Machine learning, together with pattern recognition and deep learning, are branches of the so-called Artificial Intelligence (AI) techniques that can be defined as the ability of a machine to mimic intelligent human behaviour, seeking to use humaninspired algorithms to solve problems (Penadés et al 2016, Amezquita-Sanchez et al 2016. All the aforementioned techniques aim to process recorded data in the most effective way in order to retrieve directly, or using numerical or analytical models, indicators of the structural performance that canbe used, together with assigned performance goals, to assess the structural performance.…”

Section: Performance Assessmentmentioning

confidence: 99%

Lifecycle Management, Monitoring and Assessment for Safe Large-Scale Infrastructures: Challenges and Needs

Limongelli

Previtali

Cantini

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Many European infrastructures dating back to '50 and '60 of the last century like bridges and viaducts are approaching the end of their design lifetime. In most European countries costs related to maintenance of infrastructures reach a quite high percentage of the construction budget and additional costs in terms of traffic delay are due to downtime related to the inspection and maintenance interventions. In the last 30 years, the rate of deterioration of these infrastructures has increased due to increased traffic loads, climate change related events and man-made hazards. A sustainable approach to infrastructures management over their lifecycle plays a key role in reducing the impact of mobility on safety (over 50 000 fatalities in EU per year) and the impact of greenhouse gases emission related to fossil fuels. The events related to the recent collapse of the Morandi bridge in Italy tragically highlighted the sheer need to improve resilience of aging transport infrastructures, in order to increase the safety for people and goods and to reduce losses of functionality and the related consequences. In this focus Structural Health Monitoring (SHM) is one of the key strategies with a great potential to provide a new approach to performance assessment and maintenance over the life cycle for an efficient, safe, resilient and sustainable management of the infrastructures. In this paper research efforts, needs and challenges in terms of performance monitoring, assessment and standardization are described and discussed.

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“…Since the first journal article on the civil engineering applications of neural networks (NNs) three decades ago by Adeli and Yesh [11], the penetration of neurocomputing into civil engineering has steadily increased. A review of civil engineering applications of neural networks up to 2000 is presented by Adeli [12] and from then up to 2016 by Amezquita-Sanchez et al [13]. In recent years, a number of authors have used NNs and other artificial intelligence (AI) approaches to predict the mechanical properties of HPC.…”

Section: Introductionmentioning

confidence: 99%

A Sensitivity and Robustness Analysis of GPR and ANN for High-Performance Concrete Compressive Strength Prediction Using a Monte Carlo Simulation

et al. 2020

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This study aims to analyze the sensitivity and robustness of two Artificial Intelligence (AI) techniques, namely Gaussian Process Regression (GPR) with five different kernels (Matern32, Matern52, Exponential, Squared Exponential, and Rational Quadratic) and an Artificial Neural Network (ANN) using a Monte Carlo simulation for prediction of High-Performance Concrete (HPC) compressive strength. To this purpose, 1030 samples were collected, including eight input parameters (contents of cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregates, fine aggregates, and concrete age) and an output parameter (the compressive strength) to generate the training and testing datasets. The proposed AI models were validated using several standard criteria, namely coefficient of determination (R2), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). To analyze the sensitivity and robustness of the models, Monte Carlo simulations were performed with 500 runs. The results showed that the GPR using the Matern32 kernel function outperforms others. In addition, the sensitivity analysis showed that the content of cement and the testing age of the HPC were the most sensitive and important factors for the prediction of HPC compressive strength. In short, this study might help in selecting suitable AI models and appropriate input parameters for accurate and quick estimation of the HPC compressive strength.

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Neurocomputing in Civil Infrastructure

Cited by 74 publications

References 55 publications

Fault Detection in Cracked Structure under Moving Load using RNNs based Approach

Fault Detection in Cracked Structure under Moving Load using RNNs based Approach

Lifecycle Management, Monitoring and Assessment for Safe Large-Scale Infrastructures: Challenges and Needs

A Sensitivity and Robustness Analysis of GPR and ANN for High-Performance Concrete Compressive Strength Prediction Using a Monte Carlo Simulation

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