
Juan ChiachioUniversity of Granada | UGR · Department of Structural Mechanics and Hydraulic Engineering
Juan Chiachio
PhD in Mechanical Engineering
About
106
Publications
25,132
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1,546
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Introduction
Expertise in Mechanical and Structural Engineering with strong emphasis in Bayesian methods, prognostics and structural health monitoring. I collaborate with world-class scientific institutions like CALTECH and NASA.
Additional affiliations
Education
October 2014 - November 2014
University of Granada
Field of study
- Structural Engineering
January 2008 - June 2011
October 2001 - September 2007
Publications
Publications (106)
This work presents an efficient computational framework for prognostics by combining the particle filter-based prognostics principles with the technique of Subset Simulation, first developed in S.K. Au and J.L. Beck [Probabilistic Engrg. Mech., 16 (2001), pp. 263-277], which has been named PFP-SubSim. The idea behind PFP-SubSim algorithm is to spli...
Estimating deterministic single-valued damage parameters when evaluating the actual health state of a material has a limited meaning if one considers not only the existence of measurement errors, but also that the model chosen to represent the damage behavior is just an idealization of reality. This paper proposes a multilevel Bayesian inverse prob...
A Bayesian approach is presented for selecting the most probable model class among a set of damage mechanics models for fatigue damage progression in composites. Candidate models, that are first parameterized through a Global Sensitivity Analysis, are ranked based on estimated probabilities that measure the extent of agreement of their predictions...
A new Approximate Bayesian Computation (ABC) algorithm for Bayesian updating
of model parameters is proposed in this paper, which combines the ABC
principles with the technique of Subset Simulation for efficient rare-event
simulation, first developed in S.K. Au and J.L. Beck [1]. It has been named
ABC- SubSim. The idea is to choose the nested decre...
a b s t r a c t As a response to the rampant increase in research activity within reliability in the past few decades, and to the lack of a conclusive framework for composite applications, this article attempts to identify the most relevant reliability topics to composite materials and provide a selective review. Available reliability assessment me...
Digital twins (DTs) have revolutionised digitalisation practices across various domains, including the Architecture, Engineering, Construction and Operations (AECO) sector. However, DTs often face challenges related to data scarcity, especially in AECO, where tests are costly and difficult to scale. Historical data in this domain are often limited,...
This paper presents a deep learning approach for predicting rail corrugation based on on-board rolling-stock vertical acceleration and forward velocity measurements using One-Dimensional Convolutional Neural Networks (CNN-1D). The model's performance is examined in a 1:10 scale railway system at two different forward velocities. During both the tra...
A new physics-guided Bayesian recurrent neural network is proposed in this manuscript. This hybrid algorithm benefits from the knowledge in physics-based models, the capability of recurrent neural networks to handle sequential data, and the flexibility of Bayesian methods to quantify the uncertainty. The introduction of physics in the forward pass...
In response to escalating global environmental challenges, developed countries have embarked on an ecological transition across a range of sectors. Among these, the construction industry plays a key role due to its extensive use of raw materials and energy resources. In particular, research into sustainable construction materials, here named eco-ma...
Composite structures are highly valued for their strength-to-weight ratio, durability, and versatility, making them ideal for a variety of applications, including aerospace, automotive, and infrastructure. However, potential damage scenarios like impact, fatigue, and corrosion can lead to premature failure and pose a threat to safety. This highligh...
Progress in machine learning algorithms and automatic training methods in the last decade have empowered scientists and researchers in many disciplines to build classification and regression models by leveraging historical data. However, in some fields, the lack of data and interpretability of such models, which are oen treated as black boxes, hin...
Monitoring a structure’s health is important to avoid catastrophic failures and reduce operating costs by applying the Condition Based Maintenance (CBM) strategy. CBM can reduce the inspections, but cannot replace them because of the probability of failure or error of CBM. Reinforcement learning (RL) is an artificial intelligence technique for opti...
Health indicators are indices that act as intermediary links between raw SHM data and prognostic models. An efficient HI should satisfy prognostic requirements such as monotonicity, trendability, and prognosability in such a way that it can be effectively used as an input in a prognostic model for remaining useful life estimation. However, discover...
A health indicator (HI) is a valuable index demonstrating the health level of an engineering system or structure, which is a direct intermediate connection between raw signals collected by structural health monitoring (SHM) methods and prognostic models for remaining useful life estimation. An appropriate HI should conform to prognostic criteria, i...
Sensing is the cornerstone of any functional structural health monitoring technology, with sensor number and placement being a key aspect for reliable monitoring. We introduce for the first time a robust methodology for optimal sensor configuration based on the value of information that accounts for (1) uncertainties from updatable and nonupdatable...
Carbon fiber reinforced polymer composites present excellent mechanical properties, however, their behaviour under fatigue and the interaction between the different failure modes is not yet well understood. This uncertainty, or lack of knowledge, is the reason why they are still not extensively used in the aerospace industry, where safety is critic...
A new tool for seismic design is presented, called Yield Displacement Charts (YDC). As with its predecessors, the Yield Point Spectra (YPS) and the Yield Frequency Spectra (YFS), the YDC concept takes advantage of the simple features of yield displacement (uy), to use uy in a performance-based design instead of a force-based period-dependent approa...
This work presents a digital twin framework for structural engineering. The digital twin is conceptualised and mathematically idealised within the context of structural integrity, and includes the main attributes to behave as a functional digital twin, namely simulation, learning, and management. The manuscript makes special emphasis on the autonom...
The interest in the use of composite materials in thin-walled structures has grown over the last decades due to their well-known superior mechanical performance and reduced weight when compared with traditional materials. Notwithstanding, composite structures are susceptible to damage during manufacturing and to fatigue degradation during service,...
Modelling generic size feature of delamination, like area or length, has long been considered in the literature for damage prognosis in composites through specific models describing damage state evolution with load cycles or time. However, the delamination shape has never been considered, despite that it contains more damage information like the de...
A new methodological concept is presented for seismic design, called Yield Displacement Charts (YDC). As with its predecessors, the Yield Point Spectra (YPS) and the Yield Frequency Spectra (YFS), the YDC takes advantage of the simple features of yield displacement (uy), to use uy in a performance-based design versus a force-based period-dependent...
The accurate modeling of engineering systems and processes using Petri nets often results in complex graph representations that are computationally intensive, limiting the potential of this modeling tool in real life applications. This paper presents a methodology to properly define the optimal structure and properties of a reduced Petri net that m...
The increasing mechanical and economical demands in modern systems and structures are forcing an inevitable need for joining dissimilar materials, thus creating the challenge of establishing a process to inspect and monitor dissimilar joints. Condition monitoring is a necessity to ensure that the structures are being safely used and to extend their...
Modern machine learning algorithms excel in a great variety of tasks, but at the same time, it is also known that those complex models need to deal with uncertainty from different sources. Consequently, understanding if the model is indeed making accurate predictions or simply guessing at random is not trivial, and measuring the confidence bounds b...
This paper provides a new approximate Bayesian computation (ABC) algorithm with reduced hyper-parameter scaling and its application to nonlinear structural model calibration problems. The algorithm initially takes the ABC-SubSim algorithm structure and sequentially estimates the algorithm hyper-parameter by autonomous adaptation following a Markov...
The17 Sustainable Development Goals (SDGs) established by the United Nations Agenda 2030 constitute a global blueprint agenda and instrument for peace and prosperity worldwide. Artificial intelligence and other digital technologies that have emerged in the last years, are being currently applied in virtually every area of society, economy and the e...
The United Nations Agenda 2030 established 17 Sustainable Development Goals (SDGs) as a guideline to guarantee a sustainable worldwide development. Recent advances in artificial intelligence and other digital technologies have already changed several areas of modern society, and they could be very useful to reach these sustainable goals. In this pa...
This paper proposes the use of a physics-based Bayesian framework for the localization and identification of damage in composite beam structures using ultrasonic guided-waves. The methodology relies on a transient wave propagation model based on wave and finite element scheme that efficiently provides time-domain signals that are compared with the...
This paper presents OptiSens, a computational platform in Python and Matlab, that provides optimal sensor and actuator configurations for structural health monitoring applications using ultrasonic guided-waves. This software formulates a convex entropy-based objective function, which aims at minimizing the uncertainty while maximizing the expected...
The deterioration of the built heritage is becoming a pressing issue in many countries. The assessment of such a degradation at large (building) scale is key for maintenance priorisation and decision making. This paper proposes a straightforward yet rigorous method to asses and predict the surface recession in heritage buildings. The method is base...
Engineering practice commonly requires the calibration of complex numerical models based on experimental data, which is typically carried-out as a trial and error process whose success is highly influenced by human errors. The Bayesian procedure is a robust methodology to solve this problem which also allows quantification of the uncertainties. How...
The number and position of sensors and actuators are key decision variables that dictate the performance of any structural health monitoring system. This paper proposes choosing them optimally by using an objective function that combines a measure of parameter uncertainty, the expected information entropy, along with the cost of both sensors and ac...
Structural Health Monitoring (SHM) is an emerging discipline that aims at improving the management of the life cycle of industrial components. The scope of this chapter is to present the integration of nonlinear ultrasonics with the Bayesian inverse problem as an appropriate tool to estimate the updated health state of a component taking into accou...
Condition-based maintenance critically relies on efficient and reliable structural health monitoring systems, where the number, position and type of sensors are determined according to rational and principled criteria. This paper proposes the use of the value of information and the relative expected information gain as optimality criteria to determ...
This paper presents a prognostics methodology to deal with complex degradation processes for which condition monitoring data constitute the only available source of physical information. The proposed methodology is general, but here it is illustrated and tested using a case study about fatigue crack propagation in metallic structures. The prognosti...
Bayesian methods for inverse problems offer higher robustness to noise and uncertainty than deterministic, yet accurate, inference methods. Both types of techniques typically focus on finding optimal model parameters that minimize an objective function, which compares model output with some acquired data. However, uncertainties coming from differen...
Damage identification is one of the main objectives of a successful SHM system. It includes damage detection, localization, and assessment. Achieving the three objectives with minimal number of transducers is of great value for the industrial community in order to move the technology from research into real-life applications.
This paper proposes a...
The rampant growth of offshore wind energy in Europe and overseas is calling for advanced operation and maintenance simulation tools for optimal infrastructure asset management. The operational downtimes due to random failures, repair times or adverse weather conditions, greatly reduce the availability of the wind farm and therefore increase the Le...
The prediction of water table height in unconfined layered porous media is a difficult modelling problem that typically requires numerical simulation. This paper proposes an analytical model to approximate the exact solution based on a steady-state Dupuit–Forchheimer analysis. The key contribution in relation to a similar model in the literature re...
SHM methods for damage detection and localization in plate-like structures have typically relied on signal post-processing techniques applied to ultrasonic guided-waves. The time of flight is one of these signals features which has been extensively used by the SHM community for damage localization. One approach for obtaining the time of flight is b...
This article provides a computational framework to model self-adaptive expert systems using the Petri net (PN) formalism. Self-adaptive expert systems are understood as expert systems with the ability to autonomously learn from external inputs, like monitoring data. To this end, the Bayesian learning principles are investigated and also combined wi...
Advanced PHM techniques have the potential to substantially reduce railway track maintenance costs while increasing safety and availability. However, there is still a significant lack of knowledge and experience in relation to suitable PHM models and algorithms within the context of railway track geometry degradation. This paper proposes a Bayesian...
Structural health monitoring based on ultrasonics typically involves complex data analysis. Ultrasound monitoring based on Lamb waves techniques are extensively used nowadays due to their efficiency in exploring large areas with relatively small attenuation. In recent years, baseline based methods have been developed to identify structural damage b...
This paper proposes a paradigm shift to the problem of infrastructure asset management modelling by focusing towards forecasting the future condition of the assets instead of using empirical modelling approaches based on historical data. The proposed prognostics methodology is general but, in this paper, it is applied to the particular problem of r...
SHM methods for damage detection and localisation in plate-like structures have typically relied on post-processing of ultrasonic guided waves (GWs) features. The time-of-flight is one of these features, which has been extensively used by the SHM community. A followed technique to obtain the time of flight is by applying a particular time-frequency...
This paper presents a new model for Petri nets (PNs) which combines PN principles with the foundations of information theory for uncertain knowledge representation. The resulting framework has been named Plausible Petri nets (PPNs). The main feature of PPNs resides in their efficiency to jointly consider the evolution of a discrete event system tog...
Railway track geometry deterioration due to traffic loading is a complex problem with important implications in cost and safety. Without appropriate maintenance, track deterioration can lead to severe speed restrictions or disruptions, and in extreme cases, to train derailment. This paper proposes a physics-based reliability-based prognostics frame...
This paper presents a mathematical framework for modeling prognostics at a system level, by combining the prognostics principles with the Plausible Petri nets (PPNs) formalism, first developed in M. Chiach´ıo et al. [Proceedings of the Future Technologies Conference, San Francisco, (2016), pp. 165-172]. The main feature of the resulting framework r...
Railway track geometry deterioration due to traffic loading is a complex problem with important implications in cost and safety. Without appropriate maintenance, track deterioration can lead to severe speed restrictions or disruptions, and in extreme cases, to train derailment. This paper proposes a physics-based reliability-based prognostics frame...
This paper presents a mathematical framework for modeling prognostics at a system level, by combining the prognostics principles with the Plausible Petri nets (PPNs) formalism, first developed in M. Chiachío et al. [Proceedings of the Future Technologies Conference, San Francisco, (2016), pp. 165-172]. The main feature of the resulting framework re...
A new hybrid approach for Petri nets (PNs) is proposed in this paper by combining the PNs principles with the foundations of information theory for knowledge representation. The resulting PNs have been named Plausible Petri nets (PPNs) mainly because they can handle the evolution of a discrete event system together with uncertain (plausible) inform...
This chapter describes damage prognosis techniques in the context of structural health monitoring for aerospace materials, and illustrates the efficacy of the proposed methods using fatigue data from a graphite-epoxy composite coupon. Prognostics is a core element in health management sciences which aims to predict remaining useful lifetime of the...
The chapter describes the application of prognostic techniques to the domain of structural health and demonstrates the efficacy of the methods using fatigue data from a graphite-epoxy composite coupon. Prognostics denotes the in-situ assessment of the health of a component and the repeated estimation of remaining life, conditional on anticipated fu...
Structural Health Monitoring (SHM) is an emerging discipline that aims at improving the management of the life cycle of industrial components. The scope of this chapter is to present the integration of nonlinear ultrasonics with the Bayesian inverse problem as an appropriate tool to estimate the updated health state of a component taking into accou...
The chapter describes the application of prognostic techniques to the domain of structural health and demonstrates the efficacy of the methods using fatigue data from a graphite-epoxy composite coupon. Prognostics denotes the in-situ assessment of the health of a component and the repeated estimation of remaining life, conditional on anticipated fu...
Estimating a deterministic single value for model parameterswhen reconstructing
the system response has a limited meaning if one considers that the model used
to predict its behaviour is just an idealization of reality, and furthermore, the
existence of measurements errors. To provide a reliable answer, probabilistic
instead of deterministic values...
This paper presents a reliability-based prediction methodology to obtain the remaining useful life of composite materials subjected to fatigue degradation. Degradation phenomena such as stiness reduction and increase in matrix micro-cracks density are sequentially estimated through a Bayesian ltering framework that incorporates information from bot...
An ultrasonic evaluation technique for damage identification of layered CFRP structures is presented. This approach relies on a model-based estimation procedure that combines experimental data and simulation of ultrasonic damage-propagation interactions. The CFPR structure, a [0/90]4s lay-up, has been tested in an immersion through transmission exp...