## 1 Introduction  
The modern era is characterized by rapid technological developments, resulting in the development of a new economy at a global level, where the most critical asset is data. The era of \"big data\" has given rise to the need to analyze it and extract the valuable hidden knowledge it contains [1]. More generally, the maximization of the production process in the modern era in sectors such as construction, and especially as the Industry 4.0 standard, promotes big data and requires the widespread use of cyber-physical systems that monitor and supervise physical processes, taking autonomous and decentralized optimal decisions [2].  
The decisions in question are based on information collection and analysis procedures, which come from the continuous flow of data, giving an increasingly accurate picture of the system's effectiveness in production processes. This fact implies requirements for constant collection and analysis of large-scale data from heterogeneous sources.  
The visualization of information and its diagnosis as to whether it is accurate, incomplete or inaccurate (veracity), determining its final value is a highly complex and demanding process, especially when real-time decision-making is required [3]. Large-scale data are considered as data that grow at high-speed rates as information arrives from multiple sources at high speed (velocity), which implies a change in the ways of collecting and storing this data (volume). Accordingly, various unstructured or semi-structured data forms are included, characterized by variability, as they change meaning or status over time and the environment in which they are found.  
Intelligent large-scale data analysis systems based on artificial intelligence methods have the potential to provide machine-readable formats suitable for handling complex tasks by demonstrating logic, experiential learning and optimal decision-making capabilities without human intervention.  
Artificial intelligence is an umbrella term to describe a machine's ability to mimic human cognitive functions, such as problem-solving, pattern recognition, learning and adapting to a dynamic, ever-changing environment [3]. Computational intelligence (CI) is the primary subdivision of artificial intelligence (AI) and deals with the theory, design, implementation and development of physiologically and linguistically inspired computational paradigms [4].  
This paper's remaining sections are structured as follows: Section 2 presents the basic concept of computational intelligence (CI) which is the theory, design, application and development of biologically and linguistically motivated computational paradigms, including machine learning, neural networks, fuzzy logic and evolutionary computation. The applications of CI in civil engineering research domains and specifically in Architectural Compositions, Building Technologies and Materials, Geotechnical Engineering, Structural Construction, Computer Programming and Mathematics, Mechanics Engineering, Transportation Engineering, and Hydraulics and Water Resources Engineering are described in Section 3. Section 4 presents the context of Industry 4.0 and how it can extend the applications of civil engineering science to future research. Finally, Section 5 presents the conclusions of the research.