How does data from structural health monitoring influence maintenance decisions?

SHM provides real-time data on structural conditions, allowing for risk-based decision-making. Instead of relying solely on time-based inspections, SHM uses predictive data to enhance public safety and reduce life cycle costs during construction and maintenance. SHM methods involve cognitive, technical, and decision-making processes. Human-in-the-loop (HITL) factors can introduce errors and unreliable results. Researchers explore techniques to enhance trustworthy and explainable SHM of bridges. Well-positioned sensors provide essential data for structural control and damage detection. Data-driven smart SHM systems may have higher initial costs but prove cost-effective in the long run by optimizing maintenance activities.

A predictive model and a feature engineering method would be applied to recognize the most important feature(s) for decision-making.

Data from structural health monitoring provides real-time insights into the condition of infrastructure, allowing for proactive maintenance decisions. By identifying changes in structural behavior, such as cracks or deformations, maintenance interventions can be prioritized and scheduled efficiently, ultimately extending the lifespan of the structure and minimizing risks to public safety.

Structural Health Monitoring (SHM) data significantly influences maintenance decisions by providing real-time insights into the condition of infrastructure. SHM data transforms maintenance from a reactive to a proactive process, optimizing the performance and safety of structures while minimizing costs and downtime.

Data is essential for making informed decisions on structural maintenance. However, the challenge remains that, regardless of how sophisticated your tools are—whether they are algorithms, software, sensors, or digital twins—the reliability and effectiveness of Structural Health Monitoring (SHM) depend on having adequate data. This data must be sufficient to draw meaningful insights and to accurately calibrate your models. I see this as the primary challenge to overcome in bridge maintenance.

The outcome of the Structural Health Monitoring analysis will provide information on the pH level of the concrete, the level and rate of corrosion in the rebar, as well as details on any cracks, delamination, and deformation. Two international standards used for repair are: 1. EN 1504 ( Ten parts) "Products and systems for the protection and repair of concrete structures," 2. ACI 546R-14, "Guide to Concrete Repair." The process of developing a repair strategy involves the following steps: 1. Reviewing the datasheets and application procedure of repair products from leading manufacturers such as MAPEI, FOSROC, SIKA, Weber, BASF, and H.B. Fuller. 2. Categorizing preventive maintenance work into subcategories based on the level of damage.

A major repair or risk of collapse is one of the worst scenario for a infrastructure operator. In such cases, the operation needs to be stopped immediately which will result in a potential loss of revenue for the operator and impact its users. Predictive maintenance allows to concentrate resources where they are mostly needed and prioritise repairs according to their risks. This proactive data driven approach will need to a more efficient use of resources and undertake maintenance when the issues are still small and easy to solve. Consequentially, this will result in cost savings for the operator and minimise impact and risk for its users.

Ideally one would want to extrapolate some data acquired during the measurements campaign. The key here is to come up with clever correlations between variables in order to calculate how likely it is that our technical system will collapse.

I absolutely agree with the fact that SHM data from periodic regular inspections of the viaduct bearings is crucial for maintaining the life of the viaduct bearings, integrity of the viaduct structure, smoothness of Metro operations above the viaduct. Same analogy goes with monitoring settlement data for the viaduct foundations. For any structure, especially for infrastructure works, predictive and proactive maintenance will prevent costly reactive maintenance costs and public inconvenience during breakdown of operations due to damage to the structure.

O gerenciamento de riscos é de fato um dos pilares para qualquer projeto. Muito além de prevenir acidentes, incidentes e crises, o SHM também abrange a gestão de riscos financeiros, ambientais, riscos de contratos e até mesmo riscos de reputação. Com um sistema eficaz é possível prever futuros reparos e solicitações estruturais, e com isso, ganha-se tempo para planejar físico e financeiramente quais serão as medidas adotadas, custos, mão de obra qualificada e áreas a serem paralisadas. Desta forma conseguimos resultados satisfatórios e manutenções planejadas, sem que nem a empresa e nem os colaboradores corram riscos desnecessários.

Risk management is integral to structural engineering, and SHM data plays a crucial role in this process. By providing real-time insights into the health of structures, engineers can assess risks more accurately and make decisions that mitigate potential failures. For example, if sensors detect an unusual movement in a dam wall, immediate action can be taken to investigate and address the issue, thereby reducing the risk of catastrophic failure and ensuring public safety.

Based on my experience in managing structural risk and uncertainty, we conduct thorough analysis of various factors such as environmental conditions, material properties and potential loads that the structure may face.

Nowadays, researchers use some new methods like indirect structural health monitoring. In this method, they use smartphones installed on typical vehicles like bicycles to collect data from sensors on smartphones such as GPS and IMU sensors to calculate the dynamic response of structures. This method can reduce the cost of the old method in direct structural health monitoring.

Cost and optimization go hand in hand with the above factors, from data analysis to decision making and cost minimization, risk analysis is important in most construction processes, while maintaining quality control during the execution of the work. Minimizing and analyzing the risks associated with the processes that can have a significant impact on the cost of the process to be developed, considering the high risk and the number of variables to be evaluated in a structural system.

In the design phase, the lifecycle of the structure is assumed based on use at that time, most structures faces different kind of loads and uses than that they are designed for. This impacts the lifetime of the structure. SHM helps alot in estimating accurate life time for structures, which support the decisions for rehabilitation, upgrading, or even total replacment

These SHM informations can be helpful for extending life span of the building before it gets into demolition phase. Because the informations help the users of this building decide when it to be repaired or what building to be if they would like to keep the structure for saving some budget. However, if I’ve the decision of implementing the SHM, I will use the SHM information with over 50 years old buildings as a priority.

Es importante tener en cuenta, que, bajo ciertas condiciones de agresividad del suelo y climática, es fundamental evaluar la durabilidad del acero de refuerzo, y, que, si bien es cierto aumentando el recubrimiento se protege el acero de los efectos de la carbonatación y posterior reducción drástica de la vida útil. Esto podría hacer que se reduzca el peralte efectico, y por ello tener que aumentar la cuantía inicial. Esto, económicamente podría significar costos altos en mantenimiento, por lo que optar por buscar una alternativa que reemplace al acero de refuerzo, por materiales inertes a las sales y cloruros, tales como los FRP (Carbono, Vidrio o Basalto), tendría un impacto muy positivo en costo y duración de la estructura.

O monitoramento da integridade estrutural envolve o uso de tecnologias como sensores, análise de dados e modelagem para avaliar a condição das estruturas em tempo real. Esses dados fornecem informações detalhadas sobre o comportamento da estrutura, incluindo tensões, deformações, vibrações e outros indicadores de desempenho. Essas informações em tempo real permitem uma abordagem proativa para a manutenção, reduzindo o risco de falhas catastróficas e prolongando a vida útil das estruturas. Além disso, a análise dos dados ao longo do tempo pode ajudar na identificação de tendências e na implementação de medidas de manutenção preditiva, otimizando ainda mais os programas de manutenção.