## 1 Introduction  
Construction projects are vital for the economy of developing countries, and deep foundation pit projects are an important component of the construction sector. Deep foundation pit projects are characterized by complex project management, substantial technical difficulties, and environmental influences, resulting in complex construction risks. Many deep foundation pit construction accidents have occurred worldwide in the past decade [1, 2], causing property losses and casualties. Therefore, the objective evaluation of construction risks of deep foundation pit projects and the science-based formulation of response measures are research hotspots in civil engineering and management. Deep foundation pit engineering refers to projects with an excavation depth of 5 or more meters. Projects with depths of less than 5 m are considered deep foundation pit projects if the geological and environmental conditions are complex, and underground pipelines are involved [3]. The main components of foundation pit engineering include an engineering survey, design of supporting structures and construction, earthwork excavation and backfilling, groundwater control, information development, and environmental protection [4]. Risk factor identification is a key aspect of risk research. Deep foundation pit projects are characterized by many types of construction methods, complex construction technology, and high technical integration. Traditional risk identification methods typically focus on single aspects of the construction risk factors of deep foundation pit projects, such as risk loss [5] or risk sources [6]. This approach is one-sided and prone to subjectivity. A work breakdown structure-risk breakdown structure (WBS-RBS) matrix considers the entire construction process and decomposes and classifies various risk factors involved in construction projects. Therefore, using the WBS-RBS for construction risk identification in deep foundation pit projects can substantially improve risk identification accuracy [7]. In addition, this method minimizes risk omission and reduces the subjectivity of risk identification [8]. The weight calculation of the risk indicator is the second key step in risk research. Many researchers used the analytic hierarchy process (AHP) [9] or the entropy weight method [10] to calculate the weights, but these two methods are not suitable for calculating the weight of the risk indicators of deep foundation pit projects. The AHP uses the risk assessment data of deep foundation pit construction, and although the data are easy to interpret, the results are influenced by expert opinion. The entropy weight method has high calculation accuracy but relatively low interpretability because it does not make full use of the evaluation data. The projection pursuit method (PP) obtained the objective weights from the structural characteristics of the risk evaluation data, which avoids the adverse effects of expert opinion and ensures the interpretability of the calculation results [11]. The calculation of the risk level is the third key step of risk research. Researchers have used fuzzy comprehensive evaluation (FCE) [12], the gray clustering method (GCM) [13], backpropagation neural networks (BPNNs) [14], and support vector machines (SVMs) [15]. Traditional risk assessment methods, such as the FCE and the GCM, cannot predict the risk evolution dynamically. The BPNN or SVM methods require many sample data, which is a problem in risk assessment. It should be emphasized that none of these four methods use both qualitative and quantitative indicators. In contrast, set pair analysis (SPA) uses qualitative and quantitative indicators and the concepts of identity, difference, and opposition to describe the uncertain relationship of risks. The partial connection number and set pair potential of multiple connection numbers can reveal the evolution of the risk factors [16]. However, it is difficult to objectively describe the uncertainty of the data near the index grade interval in the traditional SPA.