# Robust Simulation of Cyber-Physical Systems for Environmental Monitoring on Construction Sites
###### Abstract  
Environmental monitoring is a crucial part of environmental management on construction sites. With the increasing integration of environmental-monitoring systems and cyber-physical systems (CPS), the environmental-monitoring cyber-physical system (E-CPS) has been developed, but it still suffers from uncertainty problems and a lack of robustness. In this study, ontology is utilized to establish an E-CPS model that can realize the integration and interaction of physical space, cyberspace, and social space, and the E-CPS model contains perception, transportation, fusion, and decision-making layers. Three uncertainty scenarios are then identified in four layers of the E-CPS to address the current E-CPS shortcomings. The proposed E-CPS model is applied in a construction project, and simulation experiments are then conducted on construction sites. The results show that the abnormal-data-recognition algorithm based on spatiotemporal correlation, whose detection rate is stable around 96%, improves the system's anti-interference ability against anomalous data entering the perception layer and the transportation layer. This algorithm ensures the accuracy of environmental monitoring for early warning. The sensory data-fusion results based on the belief function method vary from 52.16 to 52.50, with a decrease rate reduced to 0.65%. Finally, the decision-fusion algorithm based on the improved Dempster-Shafer (D-S) evidence theory achieves robust performance. This study could enhance the robustness of the E-CPS in uncertainty conditions and aid the project managers to make decisions and take targeted measures according to the environmental monitoring results and experts' decisions. c 2022 2  
has become a significant direction of studies and a growing concern for the construction industry. Based on previous studies on CPS and environmental monitoring [7; 8; 9], the environmental-monitoring cyber-physical system (E-CPS) can be applied on construction sites using multi-source sensors to gain real-time environmental information, and dynamically perceive the environmental changes on construction sites. An E-CPS framework has four main layers: perception, transportation, fusion, and decision-making layers. The existing studies on E-CPS mainly focus on the improvements of the perception layer and the transportation layer, in order to quickly and accurately obtain monitoring data. However, they ignore the environmental changes' impact on the decision-making layer of the E-CPS. Therefore, the decision-making model and controller can be performed to accurately achieve integrated construction management. Due to the complexity of the construction sites, the limited accuracy of sensor measurement, and the interference of environmental changes, E-CPS faces several uncertainty challenges from the perception layer to the decision-making layer. These challenges mainly derive from three aspects: the imprecision and incompleteness of monitoring data, the non-conventionality of environmental assessment, and the inconsistency of human irrational behavior and understanding [10; 11]. Robustness, as an extension of the uncertainty theory, emphasizes the measurement and control of data. It also improves the reliability of systems [12]. The robustness of the E-CPS has two characteristics (reliability and adaptability). It is crucial to consider the interaction between CPS and the environment on construction sites [13]. More precisely, a robust E-CPS can stably achieve decision-making when monitoring noisy or damaged data. In addition, it can automatically detect unexpected events and make advanced adjustments to adapt to the environmental changes. Several studies on the influencing factors in robustness, robustness design, and robustness measurement [14; 15; 16; 17] exist. Some researchers consider CPS as technical systems, and they explored the robustness of software and operating systems [18; 19].