Modern engineering faces challenges in ensuring technical standards for service, durability, and sustainability. Political, administrative, and budgetary factors, coupled with climate change, pose tasks to structural integrity, affecting industries and economies. Marine infrastructures represent a strategic asset of a country as they handle a large part of the economic exchanges. This article analyzes five essential factors that play a fundamental role in the performance analysis of coastal structures: chloride-induced corrosion, degradation models, maintenance strategies, monitoring, and climate change. We start with reinforcement corrosion, which is considered as the main cause of distress, particularly in coastal zones, for the long-term behavior of structures. Additional pressure from the influences of climate change is becoming evident and extreme, leading to a reduction in capacity. To guarantee the lifespan of infrastructures, degradation models contribute by estimating the long-term performance of the asset as a strategic piece to the development of effective maintenance solutions. Artificial Neural Networks (ANNs) have gained recent prominence in this field due to their ability to learn intricate patterns from historical data, making them valuable instruments for predicting structural deterioration. Additionally, quantifying the condition of the structure from monitoring data plays a crucial part in providing information on the current situation of the structure. Finally, this review summarizes the challenges associated with the maintenance of aging marine structures considering aspects such as corrosion, monitoring, and the future challenges this area will face due to climate change.

Bridges deteriorate due to different causes such as ageing and climatic conditions. As a consequence, repair actions are considered more expensive than maintenance. Therefore, the prediction of maintenance at the bridge network level is particularly complex due to the considerable level of heterogeneity spanning various bridge types and functions. Consequently, in this article an analytical deterioration model called ‘two-step cluster analysis’ is presented and applied to predict the results of a homogeneous data set. This model had been used in different fields including civil engineering but not for superstructure deterioration forecasting. Variables that interfere with the performance of the structure were considered, such as geometry, volume of traffic, among others. The results show that deterioration drop rapidly if repair treatments were not performed. Variations of up to 20.9% at 50 years between the degradation of the database and the analysis of groups, demonstrates the importance of grouping within the analysis of the database, since the late maintenance can affect the life of the bridge. Preventive actions could significantly mitigate the deterioration process. The applied methodology benefits from current bridge inspection practice and produces a probabilistic rating that is consistent with the Markov approach for model deterioration and grouping variables.

Modern engineering faces challenges in ensuring technical standards for service, durability, and sustainability. Political, administrative, and budgetary factors, coupled with climate change, pose tasks to structural integrity, affecting industries and economies. Marine infrastructures represent a strategic asset of the country as it handles a large part of the economic exchanges. This article analyzes five essential factors that play a fundamental role in the performance analysis of coastal structures: Chloride-induced corrosion, degradation models, maintenance strategies, monitoring, and climate change. Starting with reinforcement corrosion, considered as the main cause of distress, particularly in coastal zones for the long-term behavior of structures. Additional pressure from the influences of climate change is becoming evident and extreme, leading to reduction in capacity. To guarantee the lifespan, degradation models contribute by estimating the long-term performance of the asset as a strategic piece to the development of effective maintenance solutions. Additionally, quantifying the condition of the structure from monitoring data, plays a crucial part that provides information on the current situation of the structure. Finally, this review summarizes the challenges associated with the maintenance of aging marine structures considering aspects such as corrosion, monitoring, and the future challenges this area will face due to climate change.

Reinforced concrete (RC) structures are well-known for their high durability; however, they remain vulnerable to natural hazards and extreme events that can impact their performance over time. In aggressive environments, there is a high likelihood of increased maintenance, rehabilitation, and repair actions that constitute a significant portion of the total lifecycle spending. Monitoring systems have been implemented during the last decades to collect periodically or continuously essential data about the durability performance of the structures in real operation. However, the effectiveness of these systems is impacted by sensor efficacy, influenced in turn by environmental factors, sensor durability, and power outages, leading to intermittent or permanent data gaps. This study proposes a methodology to address the problem of missing data of a Structural Health Monitoring (SHM) system, specifically aiming to provide more accurate and continuous information from concrete resistivity and temperature sensors to support the early detection of corrosion. The proposed methodology was applied to a repaired RC structure with over fourteen years of data, where significant gaps in the measurements were present. The approach combines several techniques to fill these gaps: deep machine learning for air temperature, generalized linear models for concrete temperature, and pattern recognition for concrete resistivity. To the best of the authors’ knowledge, this is the first time a methodology has been proposed for imputing missing data from resistivity sensors in SHM systems, which are increasingly being implemented. This approach is innovative and offers potential benefits for SHM system managers, providing more information on long-term sensor data that could aid in early corrosion detection and maintenance planning. The application of the proposed methodology to a real case study indicated a successful imputation of 43.4% of missing data although some challenges persist for sensors located in areas characterized by high measurements variability.