The maritime industry, while indispensable to global trade, is a significant contributor to greenhouse gas (GHG) emissions, accounting for approximately 3% of global emissions. As international regulatory bodies, particularly the International Maritime Organization (IMO), push for ambitious decarbonization targets, hydrogen-based technologies have emerged as promising alternatives to conventional fossil fuels. This review critically examines the potential of hydrogen fuels—including hydrogen fuel cells (HFCs) and hydrogen internal combustion engines (H2ICEs)—for maritime applications. It provides a comprehensive analysis of hydrogen production methods, storage technologies, onboard propulsion systems, and the associated techno-economic and regulatory challenges. A detailed life cycle assessment (LCA) compares the environmental impacts of hydrogen-powered vessels with conventional diesel engines, revealing significant benefits particularly when green or blue hydrogen sources are utilized. Despite notable hurdles—such as high production and retrofitting costs, storage limitations, and infrastructure gaps—hydrogen holds considerable promise in aligning maritime operations with global sustainability goals. The study underscores the importance of coordinated government policies, technological innovation, and international collaboration to realize hydrogen’s potential in decarbonizing the marine sector.

A concrete mix design aims to obtain the optimal proportions of concrete ingredients, including cement, water, sand, coarse aggregates, and admixtures. Neural networks (NNs) have been widely applied in research to predict concrete strength, and the concrete ingredients and concrete strength metrics have been used as the input and output parameters, respectively. The objective of this study is to use the 3-day concrete strength as the prior information in the NN training to reduce overfitting and improve the 28-day concrete strength prediction capability. This study is unique because the 3-day concrete strength was not used as another input parameter in the NN training; instead, it was used as data for the initial weights and biases of the connection node in the hidden layer during the NN training. Accordingly, a prior information-based NN model (PI-NNM) was developed to obtain a 28-day concrete strength prediction model. According to the tests with data subsets, the PI-NNM showed a better prediction capability than the conventional NN model, which uses only input parameters for the concrete prediction. Moreover, an adjusted PI-NNM was applied to the actual concrete production; the results showed a high prediction capability for the 28-day concrete strength.

Concrete stands as the most widely used construction material globally due to its versatility, encompassing applications ranging from pavement, multifloor structures, and bridges to dams. However, these concrete structures endure structural stress and require close monitoring to prevent accidents and ensure sustainability throughout their complete life cycle. In recent years, artificial intelligence (AI) and computer vision (CV) have demonstrated considerable potential in diverse applications within construction engineering, including structural health monitoring (SHM) and inspection processes such as crack and damage detection, as well as rebar exposure. While it is undeniable that CV and deep learning models are transforming the construction industry by offering robust solutions for complex scenarios, there remain numerous challenges pertinent to their applications that require attention. This paper aims to systematically and critically review the literature of the past decade on the application of deep learning models in the construction industry for SHM purposes in concrete structures. The review delves into proposed methodologies and technologies while identifying opportunities and challenges associated with these applications in practice. Additionally, the paper provides insights to bridge the gap between theory and application.

Construction accidents continue to threaten worker safety despite advances in management systems. Existing research catalogs accident attributes but rarely explains how triggers like human error, equipment failure, or procedural lapses interact with project types and tasks. This limits recognition of high-risk scenarios and hampers targeted prevention. To address this, a two-step framework combining Multiple Correspondence Analysis (MCA) and Association Rule Mining (ARM) is proposed. Using the Korean Construction Safety Management Integrated Information (CSI) database, MCA reduces dimensionality and clusters similar accident cases, while ARM extracts context-specific rules linking accident types, causes, and activities. The analysis reveals the following key patterns: (i) worker negligence during setup or formwork often leads to tool-related cuts; (ii) poor judgment or inadequate waste removal during excavation heightens hit or stuck incidents; and (iii) negligence frequently triggers hit and fall accidents during transportation, dismantling, and finishing. By mapping causes to operational risk factors, the framework supports actionable guidance for daily risk assessments. Safety professionals can align planned tasks with identified risks, enabling proactive interventions such as focused training, stricter supervision, and engineering controls. Thus, the MCA–ARM method establishes a data-driven foundation for improving safety decision-making and reducing construction accidents.

Construction accidents continue to threaten worker safety despite advances in management systems. Existing research catalogs accident attributes but rarely explains how triggers like human error, equipment failure, or procedural lapses interact with project types and tasks. This limits recognition of high-risk scenarios and hampers targeted prevention. To address this, a two-step framework combining Multiple Correspondence Analysis (MCA) and Association Rule Mining (ARM) is proposed. Using the Korean Construction Safety Management Integrated Information (CSI) database, MCA reduces dimensionality and clusters similar accident cases, while ARM extracts context-specific rules linking accident types, causes, and activities. The analysis reveals key patterns: (i) worker negligence during setup or formwork often leads to tool-related cuts; (ii) poor judgment or inadequate waste removal during excavation heightens hit or stuck incidents; and (iii) negligence frequently triggers hit and fall accidents during transportation, dismantling, and finishing. By mapping causes to operational risk factors, the framework supports actionable guidance for daily risk assessments. Safety professionals can align planned tasks with identified risks, enabling proactive interventions such as focused training, stricter supervision, and engineering controls. Thus, the MCA–ARM method establishes a data-driven foundation for improving safety decision-making and reducing construction accidents.