5.2.2. Hot Issues and Timing Progress

By selecting co-occurrence as an analysis type and keywords as the analysis unit, with the minimum number of occurrences of a keyword as six, 314 of 7443 keywords meet the thresholds. These keywords and their average publication year are shown in Figure 5, and the ten keywords with the most occurrences are shown in Table 3. From the results, "design" is the keyword that occurred earlier at the average publication year of 2015. Then, "model", "construction", and "simulation" occurred frequently near 2016. Scholars were beginning to seek more digital technology and simulation methods in construction research. Then in 2017, "behavior" became the most occurrent keyword, which suggests that behavior research in the CEM field is fresh and hot in recent years. At this period, "performance", "management", "system", "strength", and "impact" are also important keywords, but performance and management occurred much more. It means that researchers have paid more attention to the people-oriented perspective and cared more about the management of the behavior of individuals, such as performance and strength.

Although keywords related to neuropsychology such as risk perception, stress, satisfaction, self-efficacy, stress, attitude, injury, and health are also present in the figure, they are all with little occurrence. This clearly reflects that the application of neuropsychology in construction engineering management and its behavior research is still very insufficient. To sum up, current research on construction engineering management emphasized the analysis of behavior and has shown a tendency to focus on behavioral subjects, but the related theories and methods of neuropsychology are still very lacking in this field. For some research, although neuropsychological theories and methods have been applied, construction engineering management plays a role as a mere research context. However, in interdisciplinary research, the comparative balance between different disciplines is the premise to accomplish complementary advantages and knowledge exchange for interdisciplinary research.


**Table 3.** Keywords and timing progress examples.

#### *5.3. Generalization: Complex Correlation Mechanism*

Construction engineering management studies provide the means and methods to examine the physical world and organizational management to improve the validity and efficiency of management in the construction industry. Although relevant research is very limited, scholars have explored employing neuropsychology in the behavioral research of construction engineering management. To better understand and reveal behavior mechanisms in CEM, we provide a recap in Table 4 with the examples of CEM behavioral studies and their key methods and findings in the last five years. The main content of CEM behavioral studies includes hazard recognition, construction equipment-related accidents and safety management, mental fatigue, physical fatigue, spatial and work memory, building inspection, engineering information formats, operations and performances, working skills, and others. The research design of CEM behavioral studies is mainly based on experiments with different simulated conditions, such as various task settings and scenarios. The methods are composed of data collection technology and analysis methodology. Neuropsychological methods are usually used for data collection such as NIRS, eye-tracking devices, and wearable EEG systems. Some self-report methods may also be added for subjective data collection, such as questionnaire surveys or cognitive mapping. Information technology and statistical methods are normally taken for data analysis, such as some deep learning or machine learning of information technology, linear discriminant analysis, or MANOVA analysis of statistical methods.

Based on the review of relevant literature findings, this paper tries to establish a behavioral correlation mechanism in CEM from the perspective of neuropsychology. Although this mechanism may be difficult to cover in all research results, it can still intuitively demonstrate how neuropsychological mechanisms affect human behavior and ultimately play a role in CEM. As Figure 6 shows, current neuropsychological research on behavior mainly starts from visual behavior, and the main content of CEM is "risk identification" and "performance evaluation and prediction". For example, Wang et al. (2017) found that EEG signal properties such as frequency, power spectrum density, and spatial distribution can effectively reflect the workers' perceived risk level [15]. Shi et al. (2020) found that stressful training has a strong impact on neural connectivity and gaze movement patterns, which further affect final performance [44]. Some research conclusions clearly point out specific indicators and their positive or negative effects. For example, Shi, Du, & Ragan (2020) found a positive relationship between visual attention (fixation time) and spatial memory [26]. However, many studies only found correlations between certain neuropsychological causes and behavioral outcomes. It is difficult to explain more detailed action paths, degrees, and valence, especially when the complex nervous system of the brain is involved.


**Table 4.** Current research and main findings.

**Figure 6.** Complex behavioral associations. "+" indicates enhancing or positive promotion, "−" indicates weakening or negative inhibition, "unsigned" paths represent correlation only.

Eye-tracking research on visual behavior has successfully found that specific eyemovement indicators have an indicative effect on certain perceptions and cognitive behaviors, resulting in improved or weakened effects. For example, the distribution and number of fixation points can positively enhance risk perception, to identify whether the participant can keep a high level of safety at work. Moreover, gaze time and pupil dilation can positively promote the participant's memory of space and task content, respectively, and then positively promote the acceptance or building work of construction, and supremely help predict behavioral performance. It is worth noting the cognitive state of the participant may not be the result of neuropsychological effects, but the cause of different neuropsychological manifestations. For instance, mental fatigue will negatively affect workers' visual attention.

In addition, the results in the figure also show the effect of stress on eye movement. When participants are under high stress, their eye movements will improve, and their visual attention will be more concentrated in the vertical direction. It will directly affect the acquiring of visual information. Relevant research shows that in pipeline maintenance work of construction engineering, when the pressure load for the staff is large, the work's precision will reduce. The conclusion in the diagram also shows the influence of information presentation (such as the renderings of construction) on visual attention. As 3D presentations influence the fixation time of visual attention, there is research that found that 3D presentations, such as 3D images, BIM models, 3D printing and virtual reality technologies, are better than 2D planes in rendering effect.

In general, conducting behavioral research in CEM from the perspective of neuropsychology reflects a multidisciplinary research trend. The research in this field involves three complex dimensions: the brain and nervous system, the construction engineering management system, and individual and behavioral research (see Figure 7). For the research field, research between the brain and nervous system and construction engineering, or between the brain and nervous system and individual behavior, is mainly exploring the internal causes of external response. Research between behavior and CEM is mainly focused on the impact on performance. The essence is the result of interaction among the internal

neuropsychological mechanisms, the external behavioral mechanisms, and the construction engineering management mechanism. Therefore, the core of this multidisciplinary field is the effect paths among three mechanisms. As for the science of construction engineering management, knowledge from the four disciplines of psychology, neuroscience, management, and architecture should be absorbed. Moreover, the intersection between four disciplines needs to be noticed with an interaction in mind of combining the subjective and objective, the physical and mental, and the micro and macro.

**Figure 7.** Interdisciplinary interrelationship.

#### **6. Conclusions and Prospection**

This paper aimed to reveal the behavioral causes, behavioral performance, and the action paths of the relevant individuals in CEM. Although the research on CEM and its behavior based on neuropsychological theories and methods is still insufficient in volume and incomplete in content, the predictability of its development prospects is substantively existing. The current method has become increasingly rich, including experiments, surveys and observational studies, modeling and simulations, theory building, and case studies and their various subtypes. Moreover, some researchers are often using more than one method, which is both an opportunity and a challenge for construction engineering and management research [70]. Introducing new theories and new technology methods for any scientific research needs to ensure rigorous verification at every step. The derived results need both academics and practice to test, and then such research can be advanced with the help of new knowledge. Finally, related research can filter to daily use, to achieve the goal of providing welfare to society.

In conclusion, this paper attempted to sort out the behavioral research in construction engineering management from the perspective of neuropsychology: on the one hand, to strengthen the understanding of neuropsychological theories, techniques, and methods, and on the other hand, to explore the more refined internal relationship between construction engineering management and behavior from the perspective of micro-individuals. Based on this knowledge, this paper tried to summarize the specific methods and conclusions that can be used for reference in applying neuropsychology in the research and practice of construction engineering management.

The contribution of this paper is the carrying out of a qualitative sorting and quantitative analysis based on the common goal of multidisciplinary crossover and integration. The results can provide effective references for revealing the existing conclusions, available methods, and future trends in this disciplinary field. In detail, this review from the perspective of neuropsychology, focused on the working mechanism of the human brain and nervous system, individual behavior, and construction engineering, is close to the needs of CEM practice. Starting from the human body, it can assist managers to formulate more refined and humanized measures to improve the efficiency and safety of CEM. Meanwhile, a more accurate analysis of the working mechanism between the brain and behavior provided the reference for improving benefits of CEM. To some extent, it can also avoid ineffectiveness and inefficiency in CEM and promotes the development of sustainable construction production and consumption. Lastly, the review of human factors' methods and research also clarifies the human behavioral mechanism, which has considerable reference for designing and building a people-oriented livable city.

**Author Contributions:** Conceptualization, Y.L. and J.L.; methodology, Y.L.; formal analysis, J.L.; data curation, R.L.; writing—original draft preparation, J.L. and J.H.; writing—review and editing, J.L. and J.H.; visualization, J.L. and M.Y.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by [the Natural Science Foundation of China] grant numbers [42171219] and [the Natural Science Foundation of Fujian Province] grant numbers [2020J01011].

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** No data, models, or code were generated or used during the study. The electrode lead system on the left of Figure 2 is adapted from Wang et al. (2017), and the other part of Figure 2 and other figures in this study are drawn by the authors.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

