Structural health monitoring (SHM) of civil engineering structures has been widely developed to increase safety and to provide cost-effective maintenance programmes. Although the current approaches of SHM systems using traditional single-point sensors – such as electric strain sensors, accelerometers and global positioning system-based sensors – have appropriate measurement precision for SHM purposes, they present challenges when deployed in real-scale applications, given the limited number of possible points to assess structural behaviour and the harsh environmental conditions during operation. When it comes to reinforced-concrete structures, the development of health monitoring and damage identification presents further challenges, since this type of structure is affected by a variety of chemical, physical and mechanical degradation processes, has a heterogeneous composition and shows non-linear behaviour. On the other hand, fibre optic (FO) technology can provide integrated sensing along with extensive measurement lengths of high sensitivity, durability and stability, which makes it ideal for the SHM of concrete structures. In this paper, FO sensing principles and the different types of FO sensors for civil structure applications are briefly described and a state-of-the-art review of SHM applied to concrete structures using FO sensors in recent decades is presented.

The increasing demand for civil infrastructures, the aging of existing assets, and the strengthening of safety and liability laws have led to the inclusion of structural health monitoring (SHM) techniques into the structural management process. With the latest developments in the sensors field and computational power, real-scale SHM systems’ deployment has become logistically and economically feasible. However, it is still challenging to perform a quantitative evaluation of the structural condition based on measured data. The paper addresses recent efforts to associate measured observations with an identification of local stiffness reduction as a global parameter for damage onset and growth. It proposes a hybrid methodology for model updating and damage identification. The proposed methodology is built on data feature extraction using the principal component analysis (PCA), finite element (FE) simulation, and Monte Carlo simulation to quantify the extent of local damage of a 60-year-old prestressed concrete bridge. The methodology allows a sensor-specific quantification of the local stiffness reduction and makes it possible to focus succeeding bridge inspection, recalculation, and repair works on these areas. Even more, the monitoring in combination with the FE model and proposed methodology provides continuous information on developing stiffness reduction and the acuteness of rehabilitation measures.

The ability to track the structural condition of existing structures is one of the main concerns of bridge owners and operators. In the context of bridge maintenance programs, visual inspection predominates nowadays as the primary source of information. Yet, visual inspections alone are insufficient to satisfy the current needs for safety assessment. From this perspective, extensive research on structural health monitoring has been developed in recent decades. However, the transfer rate from laboratory experiments to real-case applications is still unsatisfactory. This paper addresses the main limitations that slow the deployment and the acceptance of real-size structural health monitoring systems (SHM) and presents a novel real-time analysis algorithm based on random variable correlation for condition monitoring. The proposed algorithm was designed to respond automatically to detect unexpected events, such as local structural failure, within a multitude of random dynamic loads. The results are part of a project on SHM, where a high sensor-count monitoring system based on long-gauge fiber Bragg grating sensors (LGFBG) was installed on a prestressed concrete bridge in Neckarsulm, Germany. The authors also present the data management system developed to handle a large amount of data, and demonstrate the results from one of the implemented post-processing methods, the principal component analysis (PCA). The results showed that the deployed SHM system successfully translates the massive raw data into meaningful information. The proposed real-time analysis algorithm delivers a reliable notification system that allows bridge managers to track unexpected events as a basis for decision-making.

Natural Ventilation Effectiveness (NVE) is a performance metric that quantifies when outdoor airflows can be used as a cooling strategy to achieve indoor thermal comfort. Based on standard ventilation threshold and building energy simulation (BES) models, the NVE relates available and required airflows to quantify the usefulness of natural ventilation (NV) through design and building evaluation. Since wind is a significant driving force for ventilation, wind pressure coefficients ($C_p$) represent a critical boundary condition when assessing building airflows. Therefore, this paper investigates the impact of different $C_p$ sources on wind-driven NVE results to see how sensitive the metric is to this variable. For that, an experimental house and a measurement period were used to develop and calibrate the initial BES model. Four $C_p$ sources are considered: an analytical model from the BES software (i), surface-averaged $C_p$ values for building windows that were calculated with Computational Fluid Dynamics (CFD) simulations using OpenFOAM through a cloud-based platform (ii_a,b,c), and two databases—AIVC (iii) and Tokyo Polytechnic University (TPU) (iv). The results show a variance among the $C_p$ sources, which directly impacts airflow predictions; however, its effect on the performance metric was relatively small. The variation in the NVE outcomes with different $C_p$’s was 3% at most, and the assessed building could be naturally ventilated around 75% of the investigated time on the first floor and 60% in the ground floor spaces.

Um edifício de alto desempenho deve atender aos requisitos de conforto ambiental e eficiência energética. As possíveis soluções incluem estratégias passivas como a ventilação natural (VN), que pode proporcionar conforto térmico e economia de energia. No entanto, seu desempenho depende do projeto do edifício e da interação com as características ambientais locais. Neste estudo, avalia-se o Potencial de Ventilação Natural (PVN) das cidades do Vale do Mucuri – MG, a partir de arquivos climáticos disponíveis. O cálculo do PVN foi implementado como um componente do plugin Grasshopper, uma plataforma de parametrização do modelador 3D Rhinoceros. Esse ambiente representa uma nova maneira de pensar o projeto, integrando análises climáticas e simulações termo energéticas à representação gráfica. O PVN baseia-se nos dados de temperatura do ar (bulbo seco e ponto de orvalho) e velocidade do vento. Ele pode ser interpretado como o número de horas/ano em que as condições climáticas externas corroboram para o uso da VN como estratégia de condicionamento de um edifício. Com base nesse dado quantitativo, projetistas podem verificar de maneira genérica se, e quando, a ventilação natural poderia ser adotada a fim de melhorar as condições de conforto dos edifícios numa determinada região.