Juan Chiachío

1.7k total citations
57 papers, 1.3k citations indexed

About

Juan Chiachío is a scholar working on Civil and Structural Engineering, Mechanics of Materials and Mechanical Engineering. According to data from OpenAlex, Juan Chiachío has authored 57 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Civil and Structural Engineering, 20 papers in Mechanics of Materials and 17 papers in Mechanical Engineering. Recurrent topics in Juan Chiachío's work include Structural Health Monitoring Techniques (25 papers), Probabilistic and Robust Engineering Design (15 papers) and Ultrasonics and Acoustic Wave Propagation (12 papers). Juan Chiachío is often cited by papers focused on Structural Health Monitoring Techniques (25 papers), Probabilistic and Robust Engineering Design (15 papers) and Ultrasonics and Acoustic Wave Propagation (12 papers). Juan Chiachío collaborates with scholars based in Spain, United Kingdom and United States. Juan Chiachío's co-authors include Manuel Chiachío, Guillermo Rus, Sergio Cantero‐Chinchilla, Dimitrios Chronopoulos, I.A. Jones, James L. Beck, Juan Fernández, Kai Goebel, Shankar Sankararaman and Abhinav Saxena and has published in prestigious journals such as Journal of Biomechanics, Sensors and Information Sciences.

In The Last Decade

Juan Chiachío

55 papers receiving 1.2k citations

Peers

Juan Chiachío
Manuel Chiachío Spain
Yoojeong Noh South Korea
María Nogal Netherlands
Efstratios Nikolaidis United States
Ning‐Cong Xiao China
Vagelis Plevris Qatar
Gholamreza Ghodrati Amiri Iran
Reza Mikaeil Iran
Taotao Zhou China
Fateme Dinmohammadi United Kingdom
Manuel Chiachío Spain
Juan Chiachío
Citations per year, relative to Juan Chiachío Juan Chiachío (= 1×) peers Manuel Chiachío

Countries citing papers authored by Juan Chiachío

Since Specialization
Citations

This map shows the geographic impact of Juan Chiachío's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Juan Chiachío with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Juan Chiachío more than expected).

Fields of papers citing papers by Juan Chiachío

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Juan Chiachío. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Juan Chiachío. The network helps show where Juan Chiachío may publish in the future.

Co-authorship network of co-authors of Juan Chiachío

This figure shows the co-authorship network connecting the top 25 collaborators of Juan Chiachío. A scholar is included among the top collaborators of Juan Chiachío based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Juan Chiachío. Juan Chiachío is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
García‐Macías, Enrique, et al.. (2025). OSP-SAP: A MATLAB graphical user interface for optimal sensor placement using SAP2000. SoftwareX. 31. 102216–102216.
2.
Melero, Francisco Javier, et al.. (2024). Generative Adversarial Networks for Improved Model Training in the Context of the Digital Twin. Structural Control and Health Monitoring. 2024(1). 2 indexed citations
3.
Chiachío, Juan, et al.. (2024). Predicting Rail Corrugation Based on Convolutional Neural Networks Using Vehicle’s Acceleration Measurements. Sensors. 24(14). 4627–4627. 1 indexed citations
4.
Chiachío, Manuel, et al.. (2024). A computer-based simulation methodology of the predetermined maintenance scheme of an irradiation facility. Computers & Industrial Engineering. 198. 110671–110671.
5.
Hernández‐Montes, Enrique, et al.. (2023). Bayesian structural parameter identification from ambient vibration in cultural heritage buildings: The case of the San Jerónimo monastery in Granada, Spain. Engineering Structures. 284. 115924–115924. 3 indexed citations
6.
Fernández, Juan, Matteo Corbetta, Chetan S. Kulkarni, Juan Chiachío, & Manuel Chiachío. (2023). Training of physics-informed Bayesian neural networks with ABC-SS for prognostic of Li-ion batteries. Computers in Industry. 155. 104058–104058. 11 indexed citations
7.
Fernández, Juan, Juan Chiachío, José A. Cabezas, Manuel Chiachío, & Chetan S. Kulkarni. (2023). Physics-guided recurrent neural network trained with approximate Bayesian computation: A case study on structural response prognostics. Reliability Engineering & System Safety. 243. 109822–109822. 24 indexed citations
8.
Fernández, Juan, Juan Chiachío, Manuel Chiachío, José A. Cabezas, & Matteo Corbetta. (2023). Physics-guided Bayesian neural networks by ABC-SS: Application to reinforced concrete columns. Engineering Applications of Artificial Intelligence. 119. 105790–105790. 21 indexed citations
9.
Puertas, Esther, et al.. (2023). A Quantitative Group Decision-Making Methodology for Structural Eco-Materials Selection Based on Qualitative Sustainability Attributes. Applied Sciences. 13(22). 12310–12310. 4 indexed citations
10.
Chiachío, Juan, et al.. (2023). DEVELOPING HEALTH INDICATORS FOR COMPOSITE STRUCTURES BASED ON A TWO-STAGE SEMI-SUPERVISED MACHINE LEARNING MODEL USING ACOUSTIC EMISSION DATA. Research Repository (Delft University of Technology). 923–934. 4 indexed citations
11.
Chiachío, Juan, et al.. (2022). Intelligent health indicator construction for prognostics of composite structures utilizing a semi-supervised deep neural network and SHM data. Engineering Applications of Artificial Intelligence. 117. 105502–105502. 55 indexed citations
12.
Alonso, Sergio, Rosana Montes, Daniel Molina, et al.. (2021). Ordering Artificial Intelligence Based Recommendations to Tackle the SDGs with a Decision-Making Model Based on Surveys. Sustainability. 13(11). 6038–6038. 12 indexed citations
13.
Palomares, Iván, Eugenio Martínez‐Cámara, Rosana Montes, et al.. (2021). A panoramic view and swot analysis of artificial intelligence for achieving the sustainable development goals by 2030: progress and prospects. Applied Intelligence. 51(9). 6497–6527. 130 indexed citations
14.
Fernández, Juan, et al.. (2021). Uncertainty quantification in Neural Networks by Approximate Bayesian Computation: Application to fatigue in composite materials. Engineering Applications of Artificial Intelligence. 107. 104511–104511. 39 indexed citations
15.
Chiachío, Juan, et al.. (2020). Probabilistic identification of surface recession patterns in heritage buildings based on digital photogrammetry. Journal of Building Engineering. 34. 101922–101922. 21 indexed citations
16.
Cantero‐Chinchilla, Sergio, James L. Beck, Juan Chiachío, Manuel Chiachío, & Dimitrios Chronopoulos. (2020). OptiSens—Convex optimization of sensor and actuator placement for ultrasonic guided-wave based structural health monitoring. SoftwareX. 13. 100643–100643. 1 indexed citations
17.
Cantero‐Chinchilla, Sergio, James L. Beck, Manuel Chiachío, et al.. (2020). Optimal sensor and actuator placement for structural health monitoring via an efficient convex cost-benefit optimization. Mechanical Systems and Signal Processing. 144. 106901–106901. 39 indexed citations
18.
Cantero‐Chinchilla, Sergio, Juan Chiachío, Manuel Chiachío, et al.. (2018). Lamb Wave-based Damage Indicator for Plate-Like Structures. PHM Society European Conference. 4(1). 2 indexed citations
19.
Chiachío, Manuel, et al.. (2017). Integration of prognostics at a system level: a Petri net approach. Annual Conference of the PHM Society. 9(1). 3 indexed citations
20.
Chiachío, Juan, Manuel Chiachío, Abhinav Saxena, Guillermo Rus, & Kai Goebel. (2014). A Model-Based Prognostics Framework to Predict Fatigue Damage Evolution and Reliability in Composites. PHM Society European Conference. 2(1). 11 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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