Evaluation Study on the Application Effect of Intelligent Construction Technology in the Construction Process

Abstract

This paper aims to evaluate and analyze the application effect of intelligent construction technology in the process of building construction because of the complexity and uncertainty of the technology itself. It also aims to promote the digital application of intelligent construction technologies throughout the life cycle of building construction and sustainable construction. Combining questionnaires and the Delphi, an indicator system for the evaluation of the application effect of intelligent construction technology was constructed, which contained twenty indicators, and then a cloud matter-element model was subsequently established by combining the cloud model with the matter element theory and took a practical engineering project as an example, so as to determine the application effect level of the technology. Results show that the evaluation grade of the effectiveness of the application of intelligent construction technology in this project is three-star and the application of intelligent construction technology in the construction phase is average, but there is a trend towards good development. The evaluation of the application effect of intelligent construction technology provides a direction for the specific application of new construction technology and promotes the transformation and upgrading of the construction industry and sustainable development.

1. Introduction

The long-standing model of rough development, fragmented management, and intensive labor in the construction industry has brought about problems such as low productivity, huge consumption of resources, and serious environmental pollution, and the gap with developed countries is still large [1]. With the continuous maturation of the new generation of information technologies, such as BIM (Building Information Modeling) [2], Internet of Things [3], 3D printing [4], Big Data, Artificial Intelligence [5], etc., the concept of “intelligent construction” was born. According to Allied Market Research [6], the global intelligent construction market size is expected to grow from $60.7 billion in 2019 to $105.8 billion by 2024, with a compound annual growth rate of approximately 11.7%. In recent years, the scale of China’s intelligent construction industry has also shown substantial expansion, exceeding 1 trillion in 2020, accounting for more than 3% of the total output value of the construction industry, as shown in Figure 1. Although China’s intelligent construction continues to progress and demand is strong but is still in the beginning and exploration stage [7], many technical means are quoted from foreign core technologies, and a scientific mechanism for evaluating the effectiveness of application has yet to be formed. Therefore, it is the new theme of future intelligent construction to promote the safe, efficient, economic, green, and sustainable development of intelligent construction and the transformation and upgrading of the construction industry by evaluating the effect of the specific application of intelligent construction technology in the construction process.

At present, the organic integration of BIM, Big Data, Artificial Intelligence, and other new-generation information technology and engineering construction is accelerating the development of intelligent construction [8]. Scholars at home and abroad have carried out in-depth exploration of this and achieved certain research results, mainly focusing on the synergistic development of intelligent construction and building industrialization [9], the cultivation of intelligent construction composite talents [10], and the application of intelligent construction technology [11]. However, the current research on the application of intelligent construction technology is still in its infancy. In the area of intelligent construction technology system construction and theoretical research, Mao Zhibing [12] discussed the intelligent construction technology system in detail and provided an overview of information technologies such as BIM, Internet of Things, Artificial Intelligence, etc. Song and Yang [13] suggested that computer science and other related technologies are the key technologies to realize intelligent construction. Liu Zhansheng [14] proposed that intelligent construction technology involves four modules, namely, intelligent planning and design, intelligent equipment and construction, intelligent facilities and disaster prevention, and intelligent operation and maintenance and service, from the perspective of the whole life cycle, and constructed an overall intelligent construction technology system by comparing the status quo of the application of intelligent construction technology at home and abroad. Pan, Y and Zhang, L [15] provided an in-depth analysis of the activities and characteristics related to Construction Engineering and Management (CEM), as well as the current status and future trends of AI applications in the context of CEM from both scient metrics and qualitative analysis perspectives, with a view of achieving the goal of modeling, predicting, and optimizing the problems in a data-driven manner throughout the life cycle of real complex projects. Mao Chao [16] builds the theoretical framework of intelligent construction on its basis according to the definition method of a multidisciplinary knowledge system and proposes that its core logic is the data unification, data-dependent data-driven information integration of the whole chain of activities, and business synergy logic through the iteration of the BIM model and BOM (Bill of Material). With regard to the specific application of intelligent construction technologies in engineering activities, Cheng and Teizer [17] introduced intelligent management techniques that enable dynamic collection, visual management, and analysis of data related to building safety and operational activity monitoring practices through sensor and positioning technologies. Kong [18] explored three intelligent construction technologies based on BIM and IoT technologies for concrete mixing monitoring, tower crane safety monitoring, and site visualization management. Rossi A [19] et al. constructed intelligent construction machinery to assess its operational status in real time by installing intelligent sensing devices on the construction machinery. Wang Fei [20], based on BIM technology and using the Internet of Things, cloud computing, Big Data, and other new-generation information technology, constructed a deeply integrated technical system of geological over-forecasting, digital survey and design, intelligent construction, etc., and carried out research on the digital handover technology of the Sichuan-Tibet Railway Tunnel Project. Dos Santos, FC [21] et al. analyzed the problems and challenges of managing building information during the construction phase of a project at this stage from the point of view of managing building performance information and proposed a tool that can validate the building performance requirements of different stakeholders and support the integration of this information into BIM. Malgit, Berk [22] et al. proposed to find the lowest-cost design alternative in an integrated manner by using structural analysis, derivative design, and BIM tools in the design phase to find the minimum weight structure among the generated design alternatives with a view of achieving minimum weight truss design.

In the research on the evaluation of the application of intelligent construction technology, with the wide application of intelligent construction technology in engineering projects, some scholars have cast their research perspectives on the evaluation of the application of intelligent construction technology at the project or enterprise level. Among them, the research results of BIM technology application evaluation are the most abundant. Zhao Bin [23] constructed a BIM application benefit evaluation index system from the perspective of owner-driven from the two aspects of application inputs and outputs and analyzed the status quo of domestic owner-driven BIM application, mode, and benefit evaluation. Wang Meihua [24] constructed a measurement model and implementation method for the BIM technology application capability of design enterprises from the perspective of organizational technology absorption theory and capability maturity theory. Tiangang Chu [25] established a BIM capability maturity model for construction companies from four levels of schedule control, quality control, cost control, and safety control for the application of BIM technology in construction companies. Wu Yumo [26] constructed an evaluation index system for the application effect of BIM in intelligent building construction based on the application of BIM technology in actual projects and combined the fuzzy comprehensive evaluation-set value statistical analysis method to conduct a comprehensive evaluation of the application. Yang Yumo [27] took the whole life cycle of the project as a starting point to establish an intelligent construction management system for assembly buildings based on BIM and conducted an evaluation of the effectiveness of the intelligent construction management system for assembly buildings.

In the context of other intelligent construction technology evaluations, Yang [28] et al. explored the research direction of evaluation methods in a Big Data environment from six aspects based on the characteristics of Big Data such as data loss, data noise, and visualization. Li Xiangong [29] et al. established an evaluation index system for the evaluation level of a coal mine IoT application based on the three-level principle of the IoT system and constructed an evaluation model for the evaluation level of a coal mine IoT application using a fuzzy comprehensive evaluation method. Zhang, Jiawei [30] established a tripartite deductive framework with the government, owner, and general contractor as the main stakeholders, analyzed the influence relationship between intelligent construction technology and the three parties in the adoption process, and proposed that the high adoption cost is the key obstacle factor in the adoption of intelligent construction technology. Lu, Chunfang [31] et al., by analyzing the connotation and characteristics of the intelligent construction of railroad engineering, establishing the key technology support system of intelligent construction based on a new generation of information technology, and constructing an evaluation index system of each stage from the aspects of technology and function, finally proposed that China’s technical support system for intelligent construction of railroad engineering is still in the stage of deepening the research on basic technology and preliminary applications. Ye Shiqi [32] introduced CMM/CMMI (Capability Maturity Model for Software/Capability Maturity Model Integration) ideas into the field of cloud computing, constructed a cloud computing capability maturity model, and initially established a more complete cloud computing capability evaluation system to evaluate and study the key capabilities of cloud computing from multiple perspectives. A summary of the acronyms in this paper is shown in Appendix A.

By analyzing the existing research results, it is found that most of the current basic theoretical research is aimed at the intelligent construction technology system, theoretical framework, and the evaluation of the application of a single intelligent construction technology, but few scholars are involved in research on the evaluation of the application of intelligent construction technology in the construction phase. Through literature research, it is known that intelligent construction has the characteristics of intelligent design, high efficiency, dynamics, information management, and environmental protection and energy saving in the construction stage. The construction stage, as an important stage of the whole building construction, can be divided into the construction preparation stage, the construction process stage, and the completion stage, and this paper mainly focuses on evaluating and researching the application of intelligent construction technology in the construction process. Evaluating the application of intelligent construction technology in the construction stage helps to improve the development level of intelligent construction technology application, improve the construction efficiency and quality, and reduce the waste of resources and environmental pollution; at the same time, the evaluation study also provides a reference for the evaluation of the application effect of intelligent construction technology in the completion stage of the construction project by each unit.

This study provides a comprehensive overview of issues related to the effectiveness of the application of intelligent construction technology in the construction phase. In Section 2, the evaluation indicator system is constructed, the method of determining the weights of the indicators is introduced, and the evaluation model is established. In Section 3, the case is analyzed. In Section 4, the degree of affiliation of the indicators at each level is calculated. In Section 5, the effect of the application of information technology in the case is discussed. Finally, the findings of this paper as well as the direction of future research are summarized in Section 6.

2. Materials and Methods

The research methodology adopted in this study focuses on collecting and analyzing numerical data, and the research design is carried out in three stages: (1) constructing the evaluation indicator system, (2) determining the weights of the evaluation indicators, and (3) establishing the evaluation model. The construction of the evaluation indicator system in this study was carried out using a number of recognized research methods, including a literature review, policy research, and expert interviews. The workflow of determining the weights of evaluation indicators includes the G1 method to calculate subjective weights, the entropy weight method to calculate objective weights, and a combination assignment to calculate comprehensive weights. Finally, the cloud matter-element model is established to conduct the evaluation study.

2.1. Evaluation Indicator System Construction

This paper combines the characteristics of the application of intelligent construction technology in the construction stage and constructs the evaluation indicator system of the application effect of intelligent construction technology according to the following steps. Firstly, the dimensions of evaluation indicators of intelligent construction technology are determined by studying domestic and foreign literature and relevant regulations and policies. Secondly, the evaluation indicators are identified and summarized, and the evaluation indicator system of the application effect of intelligent construction technology is formed preliminarily. Finally, the final evaluation indicator system of the application effect of intelligent construction technology in the construction stage is formed through expert consultation.

2.1.1. Evaluation Indicator Dimension Selection

By searching the related literature with the keywords of “intelligent construction technology application”, “intelligent construction evaluation”, “intelligent construction application effect”, and “intelligent construction site evaluation” in the databases such as China Knowledge, Wan Fang, Web of Science, Engineering Village, etc., this study selects more than 100 domestic and foreign academic papers and excellent master and doctoral dissertations with high relevance to the research content to focus on and summarize. Meanwhile, on the official website of the Ministry of Housing and Urban-Rural Development, with the keyword “intelligent construction”, the search time limit was up to 1 September 2023, and a total of thirty-two relevant pieces of information and documents were retrieved. By combing and analyzing the existing research results and relevant policy documents, the dimensional division of intelligent construction application evaluation research in the construction phase in domestic and international journal papers is summarized in

Table 1. Intelligent construction application evaluation dimension classification.

Serial Number	Dimension	Literature Sources
One	Schedule Management	[33,34,35,36]
Two	Cost Management	[33,34,35,36]
Three	Quality Management	[33,34,35,36,37]
Four	Safety Management	[33,34,35,36,37,38,39]
Five	People Management	[34,37,38,40]
Six	Material Management	[34,37,38]
Seven	Machinery Management	[34,37,38]
Eight	Method Management	[40,41]
Nine	Environmental Management	[34,39,40]
Ten	Information Management	[33,38]

.

As can be seen from Table 1, the existing research mainly divides the dimensions of intelligent construction evaluation from the above ten aspects, which lays the foundation for the selection of dimensions for evaluating the application effect of intelligent construction technology in the construction stage. In October 2022, the Ministry of Housing and Urban-Rural Development announced a list of twenty-four pilot cities for intelligent construction in China, in which the expected goal of strengthening the digital control of all elements of the project, such as quality, safety, schedule, and cost, and forming a new type of construction method that is highly efficient, high-quality, low-consumption, and low-emission, was explicitly stated. At the same time, according to the project management theory of the construction site involved in schedule management, quality control, cost control, change control, safety management, information management, contract management, organizational coordination, and sustainable development theory, one can set a specific goal for the construction management process of personnel, materials, machinery, method, and the environment of the five major factors of production management content so as to achieve a comprehensive analysis of the specific application of intelligent construction technology. Therefore, this paper refers to the division of the evaluation dimensions of intelligent construction by existing studies in Table 1, and after discussing with experts, the dimensions with the same meaning are merged to determine that the evaluation dimensions of the application effect of intelligent construction technology are six dimensions: intelligent schedule management, intelligent cost management, intelligent quality management, intelligent safety management, intelligent collaborative management, and intelligent environmental management.

2.1.2. Determination of the Evaluation Indicator System

Based on the selected six dimensions of schedule management, cost management, quality management, safety management, collaborative management, and environmental management, this paper further identifies and summarizes the evaluation indicators through literature research as well as in-depth interviews with experts, and preliminarily forms twenty-two evaluation indicators for the application effect of intelligent construction technology. In order to reduce the irrationality of the selected evaluation indicators due to personal subjectivity, the Delphi method was used to screen the twenty-two preliminary evaluation indicators. A total of 6 experts in related fields were selected by the Delphi method. The average age of the experts is 48.5 years old; 66.7% of the experts have a “graduate degree or above”; 83.3% have “10 years or above”; and 66.7% have a “senior title”. The basic information of the experts is shown in

Table 2. Basic information on experts.

Serial Number	Highest Degree	Years of Experience	Highest Professional Title	Age	Genders
One	Graduate students and above	Ten years and above	high level	52	man
Two	Graduate students and above	Ten years and above	middle level	58	man
Three	Graduate students and above	Ten years and above	high level	49	man
Four	Graduate students and above	Six to ten years	middle level	41	woman
Five	undergraduate	Ten years and above	high level	45	man
Six	undergraduate	Ten years and above	high level	46	man

.

Among them, by calculating the arithmetic mean of the cognition accuracy (Ca) and the criteria for judgment (Cs), one can obtain the authoritative degree of experts (C) so as to assess the relevant expert’s level of understanding of the field and the credibility of the specific consulting results as well as the level of availability and the results of the more credible boundaries for C ≥ 0.7. The indicator familiarity level assignments are shown in

Table 3. Indicator familiarity level score.

Cognition Accuracy (Ca)	Value of a Score
Very familiar	1.0
Familiar	0.8
General	0.6
Unfamiliar	0.4
Very unfamiliar	0.2

and the expert judgment basis assignments are shown in

Table 4. Indicator judgmental basis score.

Criteria for Judgment (Cs)	Degree of Influence	Value of a Score
Practical experience	High	0.5
	Medium	0.4
	Low	0.3
Theoretical analysis	High	0.3
	Medium	0.2
	Low	0.1
Knowledge of domestic and international counterparts	High	0.1
	Medium	0.1
	Low	0.1
Personal intuition	High	0.1
	Medium	0.1
	Low	0.1

.

The expert authority coefficient for both rounds of consultation in this survey was maintained above 0.90, with very high expert authority, as shown in

Table 5. Expert authority factor status.

Value\Round	First Round			Second Round
	Familiarity Level	Level of Judgment	Authority Factor	Familiarity Level	Level of Judgment	Authority Factor
Average value	0.917	0.925	0.921	0.950	0.942	0.946
Standard deviation	0.098	0.043	0.071	0.087	0.064	0.076

.

The hierarchy of importance of all indicators and their corresponding scores are shown in

Table 6. Indicator importance score.

Evaluation Project	Hierarchy	Value of a Score
Significance	Very important	5
	Important	4
	General importance	3
	Unimportant	2
	Very unimportant	1

.

In the results of the first round of expert Delphi consulting, except for the indicator “Cost forecast” in the cost management dimension, the average values of the importance scores of the other indicators are more than 3 and the coefficient of variation CV < 20%, so the above indicator is excluded, and the rest of the indicators are added to the second round of expert Delphi consulting. Moreover, Kendall’s coefficient passed the consistency test (p < 0.05) and the harmonization was very good. In the second round of expert Delphi consulting results, except for the indicator “construction location information” in the progress management dimension, the importance scores of the other indicators have an average value of more than 3 and a coefficient of variation CV < 20%. Through discussion with the experts, considering that in the intelligent progress management dimension, the component location information and the construction progress information are both indicators for evaluating real-time progress dynamics, the two are combined into “Dynamic tracking of construction progress”, and the combined indicators are added to the final indicator system. Kendall’s coefficient was tested for consistency (p < 0.05), and the harmonization was very good. Finally, twenty evaluation indicators of the application effect of intelligent construction technology were determined so as to construct the evaluation indicator system of the application effect of intelligent construction technology, as shown in

Table 7. Evaluation index system of intelligent construction application effect.

Standardized Layer	Indicators Layer	Description of Indicators
Intelligent Schedule Management L1	Dynamic tracking of construction progress L11	Real-time tracking of construction progress through 4D visualization models
	Dynamic management of actual versus planned progress L12	Dynamic control of actual and planned progress through VR (Virtual Reality) and BIM technology
	Analysis of rate of completion of target progress L13	Summarize and analyze the completion of target progress through the cloud platform
Intelligent Cost Management L2	Cost refinement management L21	Utilizing Big Data technology to process financial data for rapid forecasting and cost management
	Costing and Programming L22	Use Big Data technology to analyze cost information and develop cost plans accordingly
	5D Cost Control L23	Using BIM technology to build 5D model for dynamic cost control
Intelligent Quality Management L3	Construction quality monitoring L31	Monitoring and categorizing quality data through cloud computing technology
	Visual construction simulation L32	Visual construction simulation using BIM technology
	Quality Intelligence Check L33	Real-time inspection of construction quality information using IoT technology
	Analysis of quality issues L34	Analyze the quality problems during the construction process through the cloud platform
Intelligent Security Management L4	Safety Monitoring and Early Warning of Hazards L41	Monitoring of hazards and automatic alarms using BIM technology
	Operator safety training L42	Use of virtual reality technology for accident simulation and safety education of personnel
	Information security management L43	Using Big Data and BIM Technology to Manage Site Information Security
	Analysis of safety hazards L44	Using Big Data Technology to Analyze Construction Safety Hazards and Goal Accomplishment
Intelligent Environmental Management L5	Remote monitoring of dust and noise L51	Utilizing IoT technology to monitor dust and noise generated at the site and automatically intervening in exceeding indicators through Artificial Intelligence technology
	Real-time monitoring of energy consumption L52	Monitoring the energy consumption of construction sites through the Internet of Things and BIM technology, and realizing sustainable management through information collection and statistical analysis
	Construction waste monitoring L53	Monitoring of various construction wastes on site through IoT technology
Intelligent Collaborative Management L6	Informatization management platform L61	Collecting on-site information through IoT and sensing technologies and integrating all information in a platform-based management approach
	Three-dimensional communication platform L62	Build a three-dimensional communication platform through BIM technology to facilitate communication and coordination among all parties
	4D Simulation Model L63	By linking with on-site construction information through 4D visualization technology and constructing real-time simulation models, it effectively realizes dynamic and integrated management of the construction site

.

2.2. Determination of Evaluation Indicator Weights

This study uses a combination of subjective and objective assignment methods to assign weights to the evaluation indicators at each level. The main steps are as follows: (1) The G1 method of calculating the weights of indicators does not require consistency tests, and the calculation is simple and convenient, so this paper chooses the G1 method to calculate the subjective weights of indicators. (2) The entropy weight method calculates weights without the limitation of the number of indicators, has a wide range of applications, and is simple to calculate, so this paper adopts the entropy weight method to calculate the objective weights of indicators. (3) We calculate the comprehensive weights of the evaluation indicators at each level using the Lagrange extreme value method. The use of combined assignments to calculate the weights not only avoids subjective assignments relying too much on expert opinion but also avoids the disadvantage of strong objectivity of the indicators.

2.2.1. G1 Method

The G1 method [42] is a subjective weighting method proposed by improving the hierarchical analysis method. The method determines the weights of indicators based on a specific algorithm by ranking the importance of evaluation indicators. The calculation is simple and convenient, highly operational, and not limited by the number of indicators. The computational flow of the method is shown in Figure 2.

The specific calculation steps are as follows:

Step 1: Determination of the ordinal relationship of evaluation indicators. Assuming that the indicator set L = {L₁, L₂, …, L_n} is n indicators at the same level (n ≥ 2), the expert selects the most important indicator according to the degree of importance between different indicators, which is recorded as L₁′, and selects the most important indicator among the remaining (n−1) indicators, which is recorded as L₂′ and repeats this operation for the n indicators. We repeat this operation to rank the importance degree of n indicators and finally obtain the set of evaluation indicators after determining the order relationship, which is noted as L′ = {L₁′, L₂′, …, L_n′}.

Step 2: Determining the relative importance of indicators. The experts make a judgment on the level of importance between neighboring indicators L′_j−1 and L′_j according to

Table 8. Reference table for r_j assignment.

rj	rj Description of the Assignment
1.0	Indicators L′j−1 and L′j are equally important
1.2	Indicator L′j−1 is slightly more important than L′j
1.4	Indicator L′j−1 is significantly more important than L′j
1.6	Indicator L′j−1 is strongly more important than L′j
1.8	Extreme importance of indicator L′j−1 over L′j
1.1 1.3 1.5 1.7	The ratio of indicator L′j−1 to L′j is between two levels of significance

, and it is expressed by r_j:

$$r_j=\frac{{W^{\prime }}_{j-1}}{{W^{\prime }}_j},j=2,3,\dots \dots ,n$$

(1)

where W′_j and W′_j−1 denote the weights of neighboring evaluation indicators L′_j and L′_j−1, respectively, and r_j denotes the relative importance ratio between neighboring evaluation indicators L′_j−1 and L′_j. The assignment of r_j can be referred to in Table 8.

Step 3: Calculation of indicator weights. According to the value of r_j in Table 8, the weights of indicators at each level are calculated separately, where the weight value W′_j of the nth indicator is denoted as:

$${W^{\prime }}_j={\left(1+{\displaystyle \sum _{j=2}^n{\displaystyle \prod _{i=j}^n{r}_i}}\right)}^{-1}$$

(2)

$${W^{\prime }}_{j-1}=r_j{W^{\prime }}_j,(j=2,3,\dots \dots ,n)$$

(3)

2.2.2. Entropy Weight Method

The entropy weight method [43] is an objective assignment method that determines the weight of each evaluation indicator according to the degree of its influence on the system as a whole, and it can determine the degree of variability of a certain indicator. The greater the weight of the indicator, the more information it contains and the greater the degree of variability, and conversely, the lesser the degree of variability. The computational flow of the method is shown in Figure 3:

The specific calculation steps are as follows:

Step 1: Raw data acquisition and processing. Firstly, the data were graded according to five levels: extremely important, important, average, unimportant, and extremely unimportant, with corresponding scores of 5, 4, 3, 2, and 1. Then relevant experts and m intelligent construction technicians with project work experience were invited to score the indicators in turn. The scoring results were summarized to obtain the evaluation matrix R = (r_ij)_mn.

$$R={(r_{ij})}_{m\times n}={\left[\begin{array}{cccc}{r}_{11}& r_{12}& \dots & r_{1n}\\ r_{21}& r_{22}& \dots & r_{2n}\\ \dots & \dots & \dots & \dots \\ r_{m1}& r_{m2}& \dots & r_{mn}\end{array}\right]}_{m\times n}$$

(4)

where r_ij denotes the evaluation value of the ith expert for the jth indicator. m denotes the number of evaluation objects and n denotes the number of evaluation indicators.

Step 2: Calculating the proportion of each indicator score P_ij. Due to the different professional backgrounds of the scoring experts, there will be different perceptions of different evaluation indicators when scoring, so in order to eliminate this effect, one needs to normalize the evaluation matrix.

$$P_{ij}=\frac{r_{ij}}{{\displaystyle \sum _{i=1}^m{r}_{ij}}}$$

(5)

Step 3: Calculating the information entropy e_j for each evaluation metric.

$$e_j=-\frac{{\displaystyle \sum _{i=1}^m{p}_{ij}\ln (p_{ij})}}{\ln (m)}$$

(6)

where e_j denotes the information entropy of the jth indicator.

Step 4: Calculating the entropy weight of each evaluation indicator. The coefficient of variation d_j is first calculated from the information entropy of the indicators.

$$d_j=1-e_j$$

(7)

$${W_j}^{″}=\frac{d_j}{{\displaystyle \sum _{j=1}^n{d}_j}}$$

(8)

where W_j″ denotes the entropy weight of the jth evaluation index.

2.2.3. Portfolio Weights

This paper adopts the Lagrange polar method to determine the coefficients of the two kinds of weights to obtain the comprehensive weights, so as to ensure the accuracy of the results of the weight calculation of the evaluation indicators. The calculation formula is as follows:

$$\left\{\begin{array}{l}{W}_j=\alpha {W_j}^{\prime }+\beta {W_j}^{″}(j=1,2,\dots \dots ,n)\\ {\alpha }^2+{\beta }^2=1(\alpha >0,\beta >0)\end{array}\right.$$

(9)

where α is the subjective weighting coefficient and β is the objective weighting coefficient. It is solved using the Lagrange polarization method with the following formula:

$$\left\{\begin{array}{l}{\alpha }^{\prime }=\frac{{\displaystyle \sum _{i=1}^m{\displaystyle \sum _{j=1}^n{W_j}^{\prime }{r}_{ij}}}}{\sqrt{({\displaystyle \sum _{i=1}^m{\displaystyle \sum _{j=1}^n{W_j}^{\prime }{r}_{ij}{)}^2+({\displaystyle \sum _{i=1}^m{\displaystyle \sum _{j=1}^n{W_j}^{″}{r}_{ij}{)}^2}}}}}}\\ {\beta }^{\prime }=\frac{{\displaystyle \sum _{i=1}^m{\displaystyle \sum _{j=1}^n{W_j}^{″}{r}_{ij}}}}{\sqrt{({\displaystyle \sum _{i=1}^m{\displaystyle \sum _{j=1}^n{W_j}^{\prime }{r}_{ij}{)}^2+({\displaystyle \sum _{i=1}^m{\displaystyle \sum _{j=1}^n{W_j}^{″}{r}_{ij}{)}^2}}}}}}\end{array}\right.$$

(10)

$$\left\{\begin{array}{l}\alpha =\frac{{\alpha }^{\prime }}{{\alpha }^{\prime }+{\beta }^{\prime }}\\ \beta =\frac{{\beta }^{\prime }}{{\alpha }^{\prime }+{\beta }^{\prime }}\end{array}\right.$$

(11)

Through the calculation of the above formula to determine the two weights of the pending coefficient α = 0.504148, β = 0.495852, and finally obtain the weights of the indicators at each level, the weights of the indicators at each level are shown in

Table 9. Indicator weights at each level.

Standardized Layer	Portfolio Weights	Indicator Layer	G1 Method Weights	Entropy Weights	Portfolio Weights
Intelligent Schedule Management L1	0.1166	Dynamic tracking of construction progress L11	0.0429	0.0436	0.0432
		Dynamic management of actual versus planned progress L12	0.0472	0.0511	0.0492
		Analysis of rate of completion of target progress L13	0.0330	0.0401	0.0365
Intelligent Cost Management L2	0.1399	Cost refinement management L21	0.0351	0.0341	0.0346
		Costing and Programming L22	0.0547	0.0772	0.0659
		5D Cost Control L23	0.0456	0.0401	0.0429
Intelligent Quality Management L3	0.1333	Construction quality monitoring L31	0.0269	0.0545	0.0406
		Visual construction simulation L32	0.0350	0.0733	0.0540
		Quality Intelligence Check L33	0.0350	0.0807	0.0576
		Analysis of quality issues L34	0.0385	0.0832	0.0607
Intelligent Security Management L4	0.2257	Safety Monitoring and Early Warning of Hazards L41	0.0444	0.0338	0.0391
		Operator safety training L42	0.0488	0.0436	0.0462
		Information security management L43	0.0644	0.0511	0.0578
		Analysis of safety hazards L44	0.0537	0.0442	0.0490
Intelligent Environmental Management L5	0.2093	Remote monitoring of dust and noise L51	0.0747	0.0215	0.0484
		Real-time monitoring of energy consumption L52	0.0897	0.0917	0.0907
		Construction waste monitoring L53	0.0680	0.0158	0.0421
Intelligent Collaborative Management L6	0.1752	Informatization management platform L61	0.0554	0.0338	0.0447
		Three-dimensional communication platform L62	0.0609	0.0611	0.0610
		4D Simulation Model L63	0.0462	0.0255	0.0359

:

2.3. Construction of the Evaluation Model

Whether the established evaluation model is reasonable or not is crucial to the accuracy of the evaluation results. This paper combines the characteristics of the application of intelligent construction technology in the construction process and constructs the evaluation model according to the following steps: (1) The introduction of matter element theory allows for the quantitative transformation of evaluation metrics through eigenvalues. (2) It is presented that the cloud model can solve the problem of randomness and ambiguity occurring in the transformation process by using its numerical features. (3) This study combines cloud modeling and matter-element theory to construct a cloud matter-element evaluation model for comprehensively evaluating the application effect of intelligent construction technology in the construction phase.

2.3.1. Cloud Matter-Element Theory

In matter element theory, a matter element is denoted as R = (N, C, V), where V is a measure of the nature of the matter. The matter element theory can realize the quantitative transformation of qualitative indexes by analyzing the objects, characteristics, and quantitative values one by one, but the quantitative values in the process of quantitative evaluation due to the randomness and ambiguity of the problem mean its own value cannot be accurately determined [44], resulting in the accuracy of the final evaluation results not able to be guaranteed. Therefore, introducing the cloud model into the matter element theory can take randomness and ambiguity into account and convert uncertainty into certainty through expectation (E_x), entropy (E_n), and hyper entropy (H_e) in order to improve the accuracy of evaluation results. The expression is as follows:

$$R=\left[\begin{array}{c}{R}_1\\ R_2\\ ⋮\\ R_n\end{array}\right]=\left[\begin{array}{ccc}N& C_1& V_1\\ & C_2& V_2\\ & ⋮& ⋮\\ & C_n& V_n\end{array}\right]=\left[\begin{array}{ccccc}N& C_1& E_{x1}& E_{n1}& H_{e1}\\ & C_2& E_{x2}& E_{n2}& H_{e2}\\ & ⋮& & ⋮& \\ & C_n& E_{xn}& E_{nn}& H_{en}\end{array}\right]$$

(12)

In the cloud matter-element model, the application effect level of the evaluation indices is expressed by a fixed interval [C_min, C_max] with reference to the score of each level. E_x is the middle value of the interval and E_n is calculated according to the “3E_n” rule of the cloud model, with the following formula:

$$E_x=\frac{C_{\max }+C_{\min }}{2}$$

(13)

$$E_n=\frac{C_{\max }-C_{\min }}{6}$$

(14)

$$H_e=s$$

(15)

where s is a constant that can be adjusted according to the degree of ambiguity of each indicator.

2.3.2. Construction of Cloud Matter-Element Model

By constructing a cloud matter-element model, the specific application of intelligent construction technology in the construction phase will be evaluated and analyzed. In this paper, the evaluation model will be constructed according to the following process (Figure 4):

The specific calculation steps are as follows:

Step 1: Division of evaluation criteria

At present, the evaluation standard system of intelligent construction has not been formed in China, but policy documents related to intelligent construction evaluation have been issued in all provinces and cities. The relevant evaluation criteria focus on three aspects: BIM models, intelligent site construction and evaluation, and a few intelligent devices. This paper combines literature research and seeks the opinions of relevant experts to classify the indicators into five levels, in order of one-star (very poor effect), two-star (poor effect), three-star (average effect), four-star (good effect), and five-star (excellent effect) levels. In order to reflect the differences between the levels, the golden ratio model is used to determine the score interval in the domain [0, 1], and the center point of the domain 0.5 is the Ex of the evaluation level “three-star”. According to the principle of “3En”, the smaller value of the neighboring clouds En and He is 0.618 times the larger value [45]. The parameters of the standard cloud model for each evaluation level are determined by Equations (13) and (14) and are shown in

Table 10. Evaluation level and numerical characteristics of smart construction application effect.

Hierarchy	One-Star	Two-Star	Three-Star	Four-Star	Five-Star
score range	[0,0.309]	[0.117,0.501]	[0.407,0.593]	[0.499,0.883]	[0.691,1]
digital identity	(0,0.103,0.0131)	(0.309,0.064,0.0081)	(0.500,0.031,0.0050)	(0.691,0.064,0.0081)	(1,0.103,0.0131)

.

After determining the digital characteristics of each evaluation level, MATLAB programming is used to establish a standard cloud diagram for evaluating the effect of intelligent construction application, and in order to improve the accuracy and avoid the error generated by the large randomness, the number of clouds drops D is set to be 1000, as shown in Figure 5:

Step 2: Calculation of the degree of affiliation.

(1) Calculation of indicator-level affiliation. Considering each evaluation indicator value xi as a cloud droplet, the affiliation degree of each indicator value xi corresponding to each level is calculated by MATLAB 2016b with the following expression:

$$K(x_i)=\exp \left[-\frac{{(x_i-E_x)}^2}{2{({E_n}^{\prime })}^2}\right]$$

(16)

where E_n′ is a normal random number jointly determined by the expectation E_x and standard deviation H_e.

(2) Calculation of standardized layer affiliation. The criterion layer affiliation K_j(L_p) can be determined by weighting the calculated indicator layer affiliations:

$$K_j\left(L_p\right)={\displaystyle \sum _{i=1}^n{w}_{pi}{K}_j}(M_{pi})$$

(17)

where K_j(L_p) is the degree of affiliation of the pth criterion object element to the jth application effect level. K_j(M_pi) is the degree of affiliation of the score value of the ith evaluation indicator in the pth criterion object element to the jth application effect level.

(3) Calculation of target layer affiliation. Weighting the calculated criterion layer affiliation yields the target layer affiliation K_j(L):

$$K_j(L)={\displaystyle \sum _{p=1}^n{w}_p{K}_j(L_p)}$$

(18)

Step 3: Determination of evaluation ratings.

According to the affiliation degree of the evaluation indexes calculated in the above formula, the application effect level of the standardized layer, indicator layer, and target layer can be judged based on the principle of maximum affiliation, and the formula is as follows:

$$K(N)=\max K_j(N)$$

(19)

3. Case Study

In order to verify the usability and reasonableness of the proposed methodology and the constructed evaluation model, this paper introduces a case study related to an industrial park and provides a series of elaborations on the basic overview of the project as well as the application of intelligent construction technology during the construction process of the project, including the management of personnel, safety, quality, progress, video monitoring, and environment.

3.1. Project Overview

This paper takes an industrial park in Hebei Province as an example to conduct an empirical study of the Block A and Block B projects, which are constructed at the northeast corner of the intersection of Zhao Wang Street and Nuwa Huang Road in the west district of a certain city, and this project is developed and constructed by a certain city’s urban development investment group. Block A is a radio and television center with five floors above ground and two floors below ground. Block B is the entrepreneurship center of the press building with six floors above ground and two floors below ground, and two floors of an underground garage. Block A has a total construction area of 38,983 m², of which 12,950 m² is underground and 26,033 m² is above ground, and the structure is a reinforced concrete frame shear wall structure. The total building area of Block B is 41,394.32 m², with an aboveground building area of 15,610.79 m² and an underground building area of 25,783.53 m², and the building structure is a reinforced concrete structure under the ground and a steel frame structure above the ground.

3.2. Application of Intelligent Construction Technologies

The project is centered around the site personnel, machinery, materials, work methods, and environment of the five major factors of production; the comprehensive use of “BIM, Internet of Things, Mobile Internet, VR technology, Big data, Cloud computing, Artificial intelligence” information technology; the digitalization, networking, and intelligence in the safety, quality, progress, cost, and greenness; and the five goals of the management and control of the landing program to achieve project optimization of the whole situation and protect the quality of the project, safety, progress, costs, and other management objectives of smooth realization. The project controls the construction site in seven aspects, namely, personnel management, safety management, quality management, progress management, monitoring management, environmental management, and green construction management, through the construction of an intelligent construction cloud platform in which the specific application of information technology in each part is as follows:

(1) In terms of personnel management: (1) Real-name management: We established an information base of construction personnel, conducted real-time statistics on personnel induction and safety training, and reported them to the cloud computing center. (2) Personnel positioning management: Based on the real-name system of personnel, it interacts with the intelligent positioning terminal by wearing a helmet with a built-in intelligent chip, obtains real-time personnel location information, and dynamically displays the personnel’s action track. (3) One card for personnel: Uniform card for project staff, which can be used for access control, supermarkets, consumption, etc., to facilitate energy saving at the site.

(2) In terms of safety management: (1) Establish a VR safety education experience room. Utilizing VR equipment and intelligent platforms to simulate the real scenes and dangerous situations of site construction in a three-dimensional dynamic form, to realize the purpose of construction safety education and training exercises; (2) Tower crane safety monitoring: real-time monitoring of the tower crane operation, once predicted dangerous behavior automatically cut off the control’s known command.

(3) In terms of quality management: quality and safety inspection management. Real-time tracking of project quality and safety; once problems are found, timely rectification and upload rectification information and progress to the cloud. At the same time, quality problems are analyzed to grasp the trend and completion of the problem, which facilitates inspection management.

(4) In terms of progress management: (1) BIM technology progress monitoring. The BIM model enables online playback of 4D construction simulation to view the construction progress; (2) Progress plan management. By searching the plan map and setting intermediate targets, it ensures that the progress targets can be accomplished on schedule, and at the same time, it monitors the whole process of construction so that the progress plan can be modified directly if there are any relevant problems, and delays in the schedule can be avoided as much as possible.

(5) In terms of video surveillance management: remote viewing of real-time surveillance video through different terminals, support for synchronized playback of multi-channel video footage from multiple perspectives to grasp the scene situation of variable-speed playback and drag-and-drop playback. At the same time, through the photo series or video frame extraction, the longer construction process is compressed in a shorter time for video playback.

(6) In terms of environmental management: installing dust and noise monitoring equipment, real-time monitoring of environmental data, and video-assisted real-time recording; when the value exceeds the standard, it is to carry out early warning and leave a record through intelligent equipment, automatic remote linkage control, and monitoring and management of integrated management.

(7) In terms of green construction management: (1) Intelligent water and electricity monitoring and management. Remote monitoring of water and electricity consumption on the construction site and statistics of relevant data, while setting the threshold value, can automatically determine the abnormal situation. (2) Intelligent streetlight practical application. The construction site is unified to use a high-power LED light source or solar panels, which saves 60% of energy compared with traditional streetlamps.

Through the analysis of the application of intelligent construction technology of the project, it can be seen that the intelligent construction technology of the project involves the above seven aspects, but the overall investment is not strong enough, the scope of involvement is not comprehensive enough, and it has not yet formed the synergistic management of multiple aspects of the construction site. However, intelligent construction technology has been computerized in the two aspects of the environment and green construction of the project, respectively, and the situation on the site is associated with the intelligent devices so that the application on the site is digitalized, and the implementation effect is relatively good. Since China has not yet formed a unified evaluation standard for intelligent construction, it is impossible to accurately determine which level of indicators has the greatest impact on the application effect of intelligent construction technology; therefore, this paper analyzes the application of intelligent construction technology in the case by constructing an evaluation model, which can provide a reference for the construction unit to assess the application of intelligent construction technology in the process of construction, and at the same time, it can provide a reference for the development of the improvement measures of the next engineering project.

4. Results

Based on the twenty evaluation indicators identified in the previous section and the specific application of the project’s intelligent construction technology in the above seven aspects, this study invited ten experts to carry out an evaluation, including three professors and associate professors of universities engaged in related research, five general managers of building construction enterprises, and two consulting experts of construction associations, with the total number of experts engaging in the theory and practice accounting for basically half each, and with the experts being relatively rich in both theoretical and practical experience. The scoring values of the indicators and their combined weights are shown in

Table 11. Indicator weights and values.

Standardized Layer	Portfolio Weights	Indicator Layer	Portfolio Weights	Rating Value
Intelligent Schedule Management L1	0.1166	Dynamic tracking of construction progress L11	0.0432	0.5510
		Dynamic management of actual versus planned progress L12	0.0492	0.5540
		Analysis of rate of completion of target progress L13	0.0365	0.5580
Intelligent Cost Management L2	0.1399	Cost refinement management L21	0.0346	0.5530
		Costing and Programming L22	0.0659	0.5590
		5D Cost Control L23	0.0429	0.5520
Intelligent Quality Management L3	0.1333	Construction quality monitoring L31	0.0406	0.5550
		Visual construction simulation L32	0.0540	0.5650
		Quality Intelligence Check L33	0.0576	0.5600
		Analysis of quality issues L34	0.0607	0.5610
Intelligent Security Management L4	0.2257	Safety Monitoring and Early Warning of Hazards L41	0.0391	0.5620
		Operator safety training L42	0.0462	0.5670
		Information security management L43	0.0578	0.5480
		Analysis of safety hazards L44	0.0490	0.5570
Intelligent Environmental Management L5	0.2093	Remote monitoring of dust and noise L51	0.0484	0.5810
		Real-time monitoring of energy consumption L52	0.0907	0.5750
		Construction waste monitoring L53	0.0421	0.5530
Intelligent Collaborative Management L6	0.1752	Informatization management platform L61	0.0447	0.5500
		Three-dimensional communication platform L62	0.0610	0.5520
		4D Simulation Model L63	0.0359	0.5350

, the results of the indicator layer affiliation calculations are shown in

Table 12. Degree of affiliation of the indicator layer.

Indicator Layer	Degree of Affiliation					Effect Level
	One-Star	Two-Star	Three-Star	Four-Star	Five-Star
L11	0.0000	0.0000	0.2524	0.0966	0.0000	3-star
L12	0.0000	0.0000	0.2208	0.1016	0.0000	3-star
L13	0.0000	0.0000	0.1790	0.1147	0.0000	3-star
L21	0.0000	0.0000	0.2269	0.0977	0.0000	3-star
L22	0.0000	0.0000	0.1661	0.1197	0.0000	3-star
L23	0.0000	0.0000	0.2425	0.0930	0.0000	3-star
L31	0.0000	0.0000	0.2060	0.1051	0.0000	3-star
L32	0.0000	0.0000	0.1141	0.1418	0.0000	4-star
L33	0.0000	0.0000	0.1551	0.1224	0.0000	3-star
L34	0.0000	0.0000	0.1456	0.1235	0.0000	3-star
L41	0.0000	0.0000	0.1406	0.1319	0.0000	3-star
L42	0.0000	0.0000	0.0993	0.1524	0.0000	4-star
L43	0.0000	0.0000	0.3043	0.0819	0.0000	3-star
L44	0.0000	0.0000	0.1863	0.1147	0.0000	3-star
L51	0.0000	0.0000	0.0334	0.2291	0.0000	4-star
L52	0.0000	0.0000	0.0555	0.1926	0.0000	4-star
L53	0.0000	0.0000	0.2308	0.0981	0.0000	3-star
L61	0.0000	0.0000	0.2698	0.0870	0.0000	3-star
L62	0.0000	0.0000	0.2468	0.0971	0.0000	3-star
L63	0.0000	0.0019	0.5305	0.0525	0.0000	3-star

, and the results of the guideline and target layer affiliation calculations are shown in

Table 13. Criterion tier affiliation and application effectiveness ratings.

Standardized Layers	Degree of Affiliation						Application Effect Level
	One-Star	Two-Star	Three-Star	Four-Star	Five-Star	maxKj(N)
L1	0.0000	0.0000	0.0283	0.0134	0.0000	0.0283	3-star
L2	0.0000	0.0000	0.0292	0.0153	0.0000	0.0292	3-star
L3	0.0000	0.0000	0.0323	0.0265	0.0000	0.0323	3-star
L4	0.0000	0.0000	0.0368	0.0226	0.0000	0.0368	3-star
L5	0.0000	0.0000	0.0164	0.0327	0.0000	0.0327	4-star
L6	0.0000	0.0001	0.0462	0.0117	0.0000	0.0462	3-star
Target Layers	0.0000	0.0000	0.0315	0.0212	0.0000	0.0315	3-star

.

The evaluation value of each indicator in the indicator layer is regarded as a cloud droplet, and the affiliation degree of each indicator in this indicator layer is calculated using Formula (16) and MATLAB 2016b software. Moreover, 1500 iterations of tests are conducted to obtain the median, and the affiliation degree of each indicator about the different levels of application effect is finally obtained, as shown in Table 12.

Through Equations (17) and (18) and Table 12, the affiliation degree of the standardized layer and the target layer can be calculated, respectively. Using the principle of the maximum affiliation degree to determine the level of the intelligent construction technology application effect of the project, the calculation results are shown in Table 13.

5. Discussion

According to the calculation results of the affiliation degree of the standardized layer and the target layer in Table 13, it can be seen that the affiliation degree of the application effect of intelligent construction technology of this project for each evaluation level is 0.0000, 0.0000, 0.0315, 0.0212, and 0.0000, and according to the principle of the maximum affiliation, the evaluation level of the application effect of the intelligent construction technology of this project is most relevant to the three-star level. Therefore, the evaluation grade of the application effect of intelligent construction technology is three-star, indicating that the application effect of intelligent construction technology in the construction phase of the project is average, but there is a trend toward good development. Through the analysis, relevant information can be found. China’s intelligent construction started late, many technologies are quoted from foreign core technologies and projects for China’s first batch of intelligent construction pilot city-related projects, the project development of the sustainability of the project is not enough, and participation in the questionnaire survey of all parties involved in the exchange of the project in the evaluation of the effectiveness of the application of intelligent construction technology rating was generally good, so this paper builds a model of the results obtained in line with the reality of the situation. By analyzing the results of the hierarchical affiliation calculations, the following design optimization information can be derived:

(1) Indicator importance analysis:

The combined weight refers to the comprehensive weight obtained by considering the mutual influence among the evaluation indicators and combining the Lagrangian polar method, which reflects the weight of the evaluation indicators and their influence degree and provides the priority order of the indicators, and the sorting result is the sorting of the evaluation indicators in the whole evaluation system of the application effect of intelligent construction technology. Therefore, the best way to promote the application of new-generation information technology in the construction phase is to improve the evaluation indexes that have a greater weight in the whole evaluation system, so as to increase the value of other evaluation indices and promote the intelligent transformation of the construction industry. As can be seen from the weights of the indicators in Table 9, the combined weights of the first-level indicators are ordered as L₄ > L₅ > L₆ > L₂ > L₃ > L₁, of which the one with the highest combined weight is intelligent safety management L₄ and the lowest is intelligent progress management L₁. Because safety in the construction process is fundamental for all engineering activities, intelligent safety management for the application of intelligent construction technology provides basic protection and support and is an important cornerstone of the application of intelligent construction technology, so construction companies need to pay more attention to all aspects of intelligent safety management in the construction process. The lowest weighting of intelligent progress management L₁ does not mean that intelligent progress management is unimportant; rather, it is just of weaker importance compared to the other first-level evaluation indexes, but it is still an important goal in the construction process to realize accelerated construction progress and reduce wastage of resources. In the second-level evaluation index, energy consumption real-time monitoring L₅₂, cost calculation and plan preparation L₂₂, a three-dimensional communication platform L₆₂, and quality problem analysis L₃₄ have the top four comprehensive weights in the whole evaluation index system and belong to intelligent environment management L₅, intelligent cost management L₂, intelligent collaborative management L₆, and intelligent quality management L₃. Therefore, in the process of applying intelligent construction technology during the construction phase, in addition to the need to pay attention to safety management, environment management, cost management, collaborative management, and quality management are also equally important, and one cannot be avoided without the other.

(2) Indicator affiliation analysis:

The grade of the application of intelligent construction technology in progress management is three-star, and the trend toward four-star is 0.0134, which indicates that the application of intelligent construction technology in progress management is generally effective, but there is a trend toward good development. Further analysis of the affiliation degree of the secondary indicators of intelligent progress management in Table 12 shows that the three secondary indicators of dynamic tracking of the construction progress L₁₁, dynamic management of the actual and planned progress L₁₂, and analysis of the progress target completion rate L₁₃ have only reached the three-star level, in which the affiliation degree of the analysis of progress target completion rate L₁₃ at the four-star level is 0.1147, and the tendency of the development towards the four-star level is relatively high. This indicates that the application effect of 4D visualization and BIM technology in progress management is general and has not been deeply integrated into the schedule control of the project.

The effectiveness of the application of intelligent construction technology in cost management is three-star with a trend towards four-star of 0.0153, indicating that the application of intelligent construction technology in cost management is fair but with a trend towards good. Based on further analysis of the affiliation of the secondary indicators of intelligent cost management in Table 12, it can be seen that the effectiveness of the application of the technology in the three areas of cost refinement management L₂₁, cost calculation and planning L₂₂, and 5D cost control L₂₃ belongs to the three-star level. This indicates that the project has not made good use of Big Data technology in the cost management stage to make rapid forecasts of financial data and the use of BIM technology to build 5D models for cost control.

The effectiveness of the application of intelligent construction technology in quality management is three-star and the trend towards four-star is 0.0265, so the application of intelligent construction technology in quality management is average, but there is a trend towards good. Further analysis of the affiliation of the secondary indicators of intelligent quality management in Table 12 shows that the technology application effect of construction quality monitoring L₃₁, quality intelligent inspection L₃₃, and quality problem analysis L₃₄ is 3-star, which indicates that the project does not have a high degree of integration with the application of cloud computing technology, Internet of Things technology, and cloud platform technology in the process of quality management, and the technology application effect is average. The effectiveness of technology application in visual construction simulation L₃₂ is four-star, indicating that the project has performed relatively well in using BIM technology for visual construction simulation, and there is strong support for the application of BIM technology.

The effectiveness of the application of intelligent construction technology in safety management is three-star and the trend towards four-star is 0.0226, so the application of intelligent construction technology in safety management is average, but there is a trend towards good. Further analysis of the affiliation degree of the secondary indicators of intelligent safety management in Table 12 shows that the three secondary indicators of safety monitoring and hazard warning L₄₁, information security management L₄₃, and safety hazard analysis L₄₄ have a three-star rating for the effectiveness of technological application, and the effectiveness of technological application in the area of safety training for operators L₄₂ has a four-star rating. This shows that the use of virtual reality technology in the project to do a relatively good job of personnel safety training, through the use of BIM technology and Big Data technology in the construction process of information security and on-site construction safety monitoring efforts, is relatively small, but in the construction process, safety management is the fundamental of all engineering activities and should be increased in the application of intelligent construction technology for the safety management of the project.

The effectiveness of the application of intelligent construction technology in environmental management is rated four stars, indicating that the application of intelligent construction technology in environmental management is relatively effective. Comprehensive further analysis of the affiliation of the secondary indicators of intelligent environmental management in Table 12 shows that the grade of the effectiveness of the application of intelligent construction technology in the monitoring of construction waste L₅₃ is three-star, indicating that the project is generally effective in the monitoring of on-site construction waste through the use of Internet of Things technology. The application effect ratings of intelligent construction technology in terms of the remote monitoring of dust and noise L₅₁ and real-time monitoring of energy consumption L₅₂ are four-star, which indicates that the project has invested relatively more in the application of Internet of Things technology and BIM technology in environmental management, and the application effect is better.

The effectiveness of the application of intelligent construction technology in collaborative management is three-star and the trend towards four-star is 0.0117, so the effectiveness of the application of intelligent construction technology in collaborative management has a tendency towards good, but in the first level of the indicator, intelligent collaborative management has the lowest affiliation and the worst tendency towards good. This indicates that the project has invested the least in the application of intelligent construction technology in terms of collaborative management, which is also consistent with the late start of the collaborative development of intelligent construction technology in China. Further analysis of the affiliation of the secondary indicators of intelligent collaborative management in Table 12 shows that the application effect of intelligent construction technology in the three aspects of the information technology management platform L₆₁, the three-dimensional communication platform L₆₂, and the 4D simulation model L₆₃ all belong to the three-star level. This indicates that the project should increase the synergistic development of intelligent construction technologies such as IoT and BIM in the construction process, and realize intelligent and synergistic management during the construction phase by building an intelligent platform.

(3) Comparative analysis with previous studies:

As can be seen from the results of the analysis of the two aspects of indicator importance and indicator affiliation, the combination of intelligent safety management has the largest weight, which is an important cornerstone of the application of intelligent construction technology, and construction enterprises need to pay more attention to all aspects of intelligent safety management in the construction process. Meanwhile, Ye Shiqi [32] proposed that the security of cloud computing systems should be ensured before evaluating the ability of cloud computing technology applications and extracted the key indicators of cloud security ability from five aspects of physical security, communication security, application security, storage security, and management security to analyze, which is in line with the results of this paper. This study is relatively well applied in the use of Big Data to realize construction site dust and noise monitoring and energy consumption detection, but there is still much room for development. In previous studies, Chen Ke and Ding Lieyun [7] pointed out that China’s engineering Internet of Things has invested more in on-site environmental indicator monitoring, but more than 88% of the construction activities only produce moderate value, which is in line with the findings of this paper. While this study is relatively good in the use of BIM technology for visual construction simulation, Liu Zhansheng [14] pointed out that construction companies apply BIM technology mainly for error and omission checking and simulation of the construction program in order to achieve the purpose of improving the quality of construction and the efficiency of construction, which is consistent with the results of this paper. Therefore, the rationality of the evaluation model constructed in this paper was verified by comparing it with previous studies.

6. Conclusions

This paper constructs an evaluation indicator system and establishes an evaluation model of both qualitative and quantitative aspects, discusses the application effect of intelligent construction technology in the construction process from three aspects, namely, indicator importance, indicator affiliation, and comparison with previous research, and summarizes the direction of the next research.

The main conclusions are as follows: (1) In terms of the research methodology, this study is the first attempt to construct a cloud matter-element evaluation model using a combination of the G1 method and entropy weight method of empowerment to explore the application effect of intelligent construction technology. (2) In terms of the case study, (1) the application of intelligent construction technology in the construction phase of the project was generally effective, but there is a trend towards good development. (2) In the use of BIM technology for visual construction simulation, the use of Big Data and BIM technology to achieve construction site dust and noise monitoring and energy consumption detection, the use of virtual reality technology for personnel safety training was invested in more, and the application effect is better. (3) The application effect of the use of BIM technology and Big Data technology on the construction process of information security and on-site construction safety monitoring is general.

By analyzing and constructing the evaluation model of the application effect of intelligent construction technology in the construction process, we can effectively guide the application of intelligent construction technology. However, there are still some limitations in this paper, and the research will be continued from the following aspects in the future: (1) In terms of the research perspective, this paper mainly evaluates the application effect of intelligent construction technology in the construction stage, but the application of intelligent construction technology is involved in the whole life cycle. Next, the evaluation of the application effect of intelligent construction technology will be carried out in the decision-making stage, the design stage, and the later operation stage, respectively. (2) In terms of evaluation standards, China’s intelligent construction evaluation standard system has not yet been formed, and the determination of evaluation standards is not comprehensive enough. Future research will summarize the latest policy documents related to intelligent construction evaluation standards at home and abroad to improve the intelligent construction evaluation standard system. (3) With respect to the indicator system, with the development of intelligent construction, intelligent construction technology will be utilized in engineering construction activities more and more, and there are more and more indicators that can reflect the effect of the application of intelligent construction technology. The study will identify the evaluation indicators from more aspects, and at the same time, conduct more case analyses and data collection to form a dynamic database of evaluation indicators of intelligent construction technology applications.