## 4 Results of Content Analysis
### Application Fields and Hot Topics on ANN in CM  
Twelve application fields are identified, as shown in Table 4. Cost is the most common application field of ANN (23.21%), followed by performance (16.07%) and safety (11.60%). Cost is the main criterion for project feasibility study and early project decision-making. Cost overruns can lead to project cancellations [62]. The highest degree of attention (23.21%) demonstrates that ANN has made progress in solving traditional cost management problems. Performance is critical to construction [63]. A total of 16.07% of studies focused on performance measurement and improvement, which is the second hottest topic after cost. According to [PERSON] and [PERSON] [64], more than 1.3 million people suffer from occupational accidents in the construction industry annually. Although safety management systems have been introduced in recent decades, occupational safety is still poor [65]. Table 4 (11.60%) indicates that safety is a major concern in the construction  
Figure 5: Methods/algorithms integrated with ANN in selected articles. in industry. With the application of information and sensor technology, the availability of an extensive training dataset makes ANN a promising approach in construction safety management [65]. In addition to the above three fields, quality and resources also received significant attention, and both accounted for 8.04%. All application fields and specific topics are shown in Table 4.  
Upon further analysis, 31 specific topics about ANN in CM can be extracted from the above 11 fields. Table 4 shows all ANN topics in CM in the selected articles and the percentage of articles on each topic. It implies that cost estimation is the most widely discussed topic (11.61%), followed by project performance evaluation (9.82%) and worker safety behavior assessment (8.93%). For construction cost estimation, ANN is a representative method for early construction cost estimation by identifying cost influencing factors and establishing a prediction model based on historical data [47]. [PERSON], [PERSON], and [PERSON] [12] presented an original approach of building construction cost predictive models based on ensembles of some MLPNNs. [PERSON] and [PERSON] [66] used advanced machine learning concepts to create innovative construction cost estimation models, including an unsupervised deep Boltzmann machine learning approach and a soft-max layer three-layer BPNN. Some studies compared multiple models to find the best one, for example, comparing ANN with multiple regression analysis [67] and comparing different types of ANN [68]. ANN is recognized as one of the most common AI techniques for parametric cost modeling, so determining model parameters is the key stage of model development [69]. The selected literature has been surveyed to identify the drivers in the cost field, and Table 5 presents important variables in main topics. The application of ANN in project performance evaluation consists of project success evaluation and project productivity evaluation. [PERSON] et al. [77] developed and applied EFHNN to evaluate project success by fusing hybrid neural network, FL and GA. For evaluation of project productivity, ANN is mainly used for construction operations and the prediction of construction worker productivity. [PERSON] et al. [78] combined simulation and an ANN technique to produce more reliable prediction results for earthworks productivity. To improve the predictive performance of labor productivity, [PERSON] and [PERSON] [79] proposed using Bayesian regularization to optimize BPNN. The performance of FNN was compared with RBFNN in modeling the productivity of masonry crews by [PERSON] et al. [80]. ANN applications in worker safety behavior assessment include the fall prevention inspection for working at a height, unsafe behavior, and posture detection. High-altitude fall accidents are the main cause of fatalities on construction sites. [PERSON] et al. [81] proposed using a smartphone as a data collection tool to detect and identify near-miss