Research on the Benefits of Intelligent Construction Site Applications—A Case Study in Nanjing City

Abstract

In the field of construction engineering, utilizing information technology to empower and upgrade traditional construction methods has become an inevitable trend. Intelligent construction sites aim to enhance project management, quality, and safety levels through three layers—digitalization, networking, and intelligentization—by leveraging advanced information technologies such as Building Information Modeling (BIM), the Internet of Things (IoT), intelligent equipment, and big data. To promote the application of intelligent construction site technology, this paper takes intelligent construction sites as the research object, with the analysis of application benefits as the primary research focus. It systematically examines the definition and connotation of intelligent construction sites, reviews the current research status of intelligent construction sites and benefit evaluation theory, and proposes an intelligent construction site application benefit system across five dimensions: economy, product, organization, management, and strategy. Existing benefit assessment studies predominantly adopt single-dimension evaluation approaches, lacking integrated frameworks that combine quantitative and qualitative analysis. Return on Investment (ROI) and Analytic Hierarchy Process (AHP) analysis models are employed to calculate and evaluate the direct and indirect benefits, respectively. Validation was conducted through an actual project, and the results demonstrate that the application of intelligent construction sites yields an ROI of 102.7% based on discounted cash flow analysis (8% social discount rate), with an expert scoring of 9.42 for indirect benefits. The analysis models indicate positive benefits associated with intelligent construction site implementation. While direct causal attribution requires controlled comparison, the observed improvements are consistent with theoretical expectations and industry benchmarks for intelligent construction site adoption. This study verifies the availability of the evaluation system through its application to an actual project. It is hoped that this research will provide a reference for decision-making regarding the application and promotion of intelligent construction sites.

1. Introduction

Traditional construction sites have long been a significant challenge in the construction industry due to their high accident rates, lack of guaranteed production efficiency, and insufficient application of intelligent technologies [1]. This also reflects the urgent need for digital and modern transformation in the traditional construction industry [2]. Intelligent construction sites represent an emerging concept in the field of construction engineering in recent years, referring to the full utilization of advanced information technologies such as Building Information Modeling (BIM), the Internet of Things (IoT), artificial intelligence, and cloud computing to achieve real-time perception, interconnection, intelligent analysis, collaborative control, and decision support for key elements including “people, machines, materials, methods, and environment” at construction sites [3,4]. The aim is to improve construction efficiency, safety, quality, and environmental management levels. Intelligent construction sites encompass the entire process of construction production, project objective management, and business information flow, and have become an important direction for promoting the transformation and upgrading of the construction industry [5].

Currently, the application of information technology in the construction industry is met with high expectations [6], with hopes of addressing issues such as low production efficiency, high labor intensity, high work hazards, and high carbon emissions in traditional construction [7]. However, intelligent construction site technology has encountered numerous difficulties in practical application [8,9]. The benefits of intelligent construction sites are diverse in composition, encompassing both directly quantifiable economic benefits and indirect benefits that are difficult to quantify, such as those related to management, organization, product, and strategy [10,11]. The benefit uncertainty caused by this diversity has seriously undermined stakeholders’ enthusiasm for adopting intelligent construction sites.

Research on the application benefit analysis of intelligent construction sites is still in its infancy, with most existing literature focusing on the application benefits of BIM technology [12,13,14]. Studies that take intelligent construction sites as the research object for application benefit analysis are particularly scarce, which limits the theoretical development and practical application of such benefit analysis, especially in terms of evaluating and promoting the advancement of intelligent construction sites [15]. Identifying and analyzing the influencing factors of intelligent construction site application benefits is crucial for promoting the development and practical application of this field [16]. Therefore, it is necessary to conduct multidimensional research on the benefits of intelligent construction sites, particularly in terms of benefit manifestation and factor analysis, so that project benefits can be directly expressed, thereby improving the adoption rate of intelligent construction sites.

Based on the above background, this study focuses on the application benefit analysis of intelligent construction sites. It reviews existing research and summarizes the application status and benefit indicators of intelligent construction sites. Relying on actual engineering projects, this study incorporates indirect benefit analysis into the application benefit analysis of intelligent construction sites, categorizing it into four dimensions: product, organization, management, and strategy. Using the Analytic Hierarchy Process (AHP) analysis method, evaluation indicators and their weights were determined through a questionnaire survey involving 12 experts, and indirect benefit measurement and standards were established. This study aims to construct a comprehensive evaluation system covering both direct and indirect benefits, providing support for the promotion and application of intelligent construction sites.

The remaining part of this study is organized as follows: Section 2 emphasizes a literature review of previous studies. Section 3 introduces the research methods. Section 4 and Section 5, respectively, present the results of the case study and in-depth discussions, while Section 6 elaborates on the research conclusions, limitations and provides guidance for future research directions.

2. Literature Review

2.1. Application of Intelligent Construction Sites

According to the “14th Five-Year Plan” for the Development of the Construction Industry issued by the Ministry of Housing and Urban-Rural Development in 2022, China’s construction industry should leverage the coordinated development of intelligent construction and new-type construction industrialization as a driving force to accelerate the transition toward green and low-carbon practices [17]. Against this backdrop, the implementation of intelligent construction sites has become a critical pathway for advancing the digital transformation of the construction industry. A intelligent construction site is an integrated system that achieves automated and intelligent management of construction sites, built upon information technologies such as the Internet of Things (IoT), big data, Building Information Modeling (BIM), and Artificial Intelligence (AI) [18,19]. Through technological integration, this application closely connects management decision-making with on-site construction activities, thereby enhancing construction management efficiency, optimizing resource allocation, strengthening safety controls, and reducing environmental impacts [20,21].

At the technical foundation level, existing research has explored the core enabling technologies of intelligent construction sites from various dimensions. In terms of data processing and analysis, Wu et al. (2022) noted the increasing adoption of Natural Language Processing (NLP) in the construction sector, highlighting its role in improving project management efficiency through information extraction and document organization [22]. Xia et al. (2022) explored functional integration of railway transportation in intelligent cities by analyzing the integration approaches of Geographic Information Systems (GIS) and BIM technologies, offering a new technological perspective for intelligent construction sites [23]. Regarding safety management, Jiang et al. (2023) investigated risk data synchronization and mapping technologies between virtual and physical construction sites, providing important guidance for safety management in intelligent construction sites [24].

At the application practice level, intelligent construction site technologies have achieved significant progress in areas such as safety monitoring, risk management, and real-time tracking. In health and safety monitoring, Piotr Sowiński et al. (2023) utilized intelligent watches to monitor workers’ health conditions through heart rate monitoring in a privacy-preserving manner, effectively preventing the oversight of health hazards [25]. In risk early warning, Gan et al. (2024) developed a collision warning model that accurately predicts collision risk levels for workers on construction sites, enhancing on-site safety and risk management capabilities [26]. In comprehensive management, Han et al. [27] created material classification BIM models and point cloud models to generate a Construction Material Library (CML), enabling efficient classification and organization of on-site materials; Radha Nanga et al. [28] utilized GPS to collect and record equipment operation data on construction sites, analyzing it on a software platform to achieve automated assessment of equipment operating conditions. Lee et al. (2023) integrated AI and IoT technologies to develop an intelligent safety monitoring platform for construction, capable of real-time monitoring of construction site activities, proactively preventing entry into hazardous zones, and rapidly responding to emergencies [29]. Site layout planning has also been a topic of scholarly interest [30].

In summary, intelligent construction sites that integrate advanced information technologies can not only enhance the efficiency and quality of construction projects but also effectively safeguard the safety and health of on-site personnel, representing a vital direction for the transformation and upgrading of the construction industry.

2.2. Application Benefit Analysis of Intelligent Construction Site

Research on the benefit evaluation of intelligent construction sites primarily draws upon studies concerning the application benefits of BIM technology. This body of work mainly addresses the exploration of factors influencing BIM application and its benefits, as well as methods for benefit identification and calculation.

Studies exploring influencing factors predominantly employ qualitative research methods, including literature review, model and process analysis, questionnaire surveys, general reasoning, and statistical investigation [31,32,33,34,35,36,37,38]. Babatunde et al. [39] identified the driving factors of BIM technology application and its benefits for the quantity surveying profession through literature review, subsequently determining the top 12 driving factors via questionnaire surveys. Xu Huier et al. [40] proposed a comprehensive research model to examine factors affecting BIM adoption. By drawing upon the Technology Acceptance Model and Innovation Diffusion Theory and validating their findings with survey data from the Chinese construction industry, their results indicated that attitudinal, technological, and organizational dimensions indirectly influence actual BIM usage through perceived usefulness and perceived ease of use. These two perceptions emerged as the primary determinants of BIM adoption, with the attitudinal dimension exerting a significant positive effect on actual BIM usage.

Benefit identification largely relies on questionnaire surveys, general reasoning, and exploratory factor analysis. Enshass et al. [41], seeking to identify and assess the potential benefits of BIM implementation in the Architecture, Engineering, and Construction (AEC) industry in the Gaza Strip, administered questionnaires to 270 construction professionals. Through exploratory factor analysis, they identified four major categories of BIM benefits: life-cycle cost control and environmental considerations, effective construction processes, design and quality improvement, and decision-making support. Masood et al. [42] synthesized seven categories of BIM application benefits through literature review, then conducted questionnaire surveys among professionals in the Pakistani construction industry. Using a Likert scale for benefit rating and calculating relative weights, they ultimately established a ranking of benefit importance.

Research on benefit calculation encompasses both quantitative and qualitative approaches. Quantitative methods primarily include transaction cost theory, evaluation models, and case-based computational analysis [43,44,45,46]. Won et al. [13] established a success level assessment model to evaluate the applicability of BIM projects with defined objectives. By determining collectible, quantifiable, and comparable key performance indicators and analyzing the indicator gaps between two similar projects—one employing BIM and one without—they quantified BIM application benefits. Qualitative methods include workshops and comparative case studies [47,48,49,50]. Stowe et al. [51], investigating whether BIM benefits warrant deeper implementation, conducted over 51 workshops worldwide with current BIM practitioners, identifying BIM benefits in the AEC industry and determining the 19 most influential benefits through workshop-based case studies. Lu Weisheng et al. [52] compared a representative BIM project with a non-BIM project, employing an innovative methodology to develop and analyze actual time–effort distribution curves for two public housing construction projects in Hong Kong. These curves vividly demonstrated that BIM implementation increases effort during the design phase (i.e., throughout the architectural and engineering process), yet this additional effort yields returns during the construction phase.

Existing research has primarily reflected the benefits of intelligent construction sites through the lens of BIM technology application benefits, without conducting benefit evaluation of intelligent construction sites per se through both direct and indirect benefit analysis approaches. This study aims to facilitate the development and promotion of intelligent construction sites through benefit analysis from multiple perspectives.

3. Methodology

This study employed two analytical approaches to demonstrate the application benefits of intelligent construction sites. The first is a direct benefit expression approach primarily based on calculating Return on Investment (ROI). The second is based on the Analytic Hierarchy Process (AHP) method, which determines evaluation indicators and their weights through expert questionnaire surveys, and establishes indirect benefit measurement criteria and standards, thereby deriving the threshold values for indirect benefits. These two benefit evaluation approaches together constitute the benefit evaluation framework of this study. The specific research roadmap is shown in Figure 1 below.

3.1. Direct Benefit Method

Direct benefits are measured using the Return on Investment (ROI) calculated through discounted cash flow (DCF) analysis. Given that intelligent construction site benefits extend over a 50-year operational period, the time value of money must be explicitly considered. Following China’s Methods and Parameters for Economic Evaluation of Construction Projects [53], a social discount rate of 8% is applied to discount all future cash flows to their present values. The present value of costs and benefits are calculated as:

$$P{V}_C=\sum _{t=1}^n{\displaystyle \frac{C_t}{{(1+r)}^t}}$$

$$P{V}_B=\sum _{t=1}^n{\displaystyle \frac{B_t}{{(1+r)}^t}}$$

where PV_C is the present value of costs, PV_B is the present value of benefits, C_t and B_t are the costs and benefits in year t, r is the discount rate (8%), and n is the evaluation period.

The Return on Investment is then calculated as:

$$ROI= {\displaystyle \frac{P{V}_B-{PV}_C}{{PV}_C}}\ast 100\%$$

This formulation represents the net return per unit of investment in present value terms, enabling meaningful comparison across projects with different scales and time horizons [54,55]. A positive ROI indicates that the present value of benefits exceeds the present value of costs, demonstrating economic viability.

Investment in an intelligent construction site primarily includes costs for software and hardware facilities, personnel training, technical services, and management. The direct economic benefits generated by intelligent construction site applications are multifaceted, mainly encompassing three aspects: shortening project duration, reducing costs, and improving quality. The specifics are shown in

Table 1. Direct economic benefits of intelligent construction site applications.

Category	Estimation Method
Schedule Reduction	Collaborative design shortens the design cycle days × Bd
	Visualization reduces the number of bidding and tendering days × Bd
	Automatic statistics of engineering quantities shorten the number of days for cost estimation × Bd
	Reduced engineering changes and shortened construction period × Bd
	Other shortened construction period days × Bd
Cost Saving	Material quantity reduced through parametric design × Material unit price
	Pipeline quantity reduced through MEP coordination optimization × Pipeline unit price
	Operational cost saving
	Other cost saving
Quality Improvement	Reduced engineering change costs by clash detection
	Reduced engineering change costs by MEP detailed design
	Reduced engineering change costs by other quality improvement

Note: Bd represents the average daily benefits after operation.

below:

3.2. Indirect Benefit Method

Based on project practice and literature [56,57,58,59], a total of 19 secondary indirect benefit indicators were sorted out from four dimensions: organizational benefits, management benefits, product benefits, and strategic benefits. The indirect benefit index system of the established intelligent construction site is shown in Figure 2 below.

For the indicator system of indirect benefits, this study adopted the Analytic Hierarchy Process (AHP) for weight assignment, inviting 12 experts to participate in the scoring. The Analytic Hierarchy Process (AHP) is a systematic analysis method proposed by Professor T.L. Saaty of the University of Pittsburgh in the 1970s [60]. It integrates qualitative and quantitative analysis, modeling and quantifying the decision-makers’ thought process when dealing with complex systems. Using this method, decision-makers can decompose complex problems into multiple levels and factors, and through simple pairwise comparisons and calculations, obtain the weights of different alternatives, thereby providing a basis for selecting the optimal solution. AHP is characterized by clear logic, simple methodology, wide applicability, and strong systematic nature, making it a powerful tool for analyzing complex systems involving multiple objectives, factors, and criteria.

A hierarchical model is generally divided into the goal level, criteria level, and alternative level. The goal level contains only one element, representing the predetermined objective or desired outcome of the decision problem. The criteria level comprises the intermediate elements involved in achieving the goal. The alternative level refers to the measures that facilitate goal achievement. To enable a more intuitive analysis of the goal level, quantitative evaluation of each indicator element at the alternative level is required. Therefore, it is necessary to further identify the importance ranking of each evaluation indicator and determine its weight. The main process is as follows:

(1)

Establishing the Hierarchical Structure Model

The process of establishing the benefit evaluation system does not involve specific alternatives. This model contains two levels of indirect benefit indicators: the goal level (indirect benefits) and the criteria level.

(2)

Constructing Judgment Matrices for Each Level

The nine-point scale method was introduced to quantitatively compare the relative importance between pairs of elements. The importance of first-level indicators relative to the goal level, as well as the importance of second-level indicators relative to their corresponding first-level indicators, were compared, resulting in a total of five judgment matrices.

$$A_B=\left[\begin{array}{ccc}{b}_{11}& \dots & b_{1n}\\ ⋮& \ddots & ⋮\\ b_{n1}& \dots & b_{nn}\end{array}\right]$$

$b_{ij}$ represents the relative importance between element $b_i$ and element $b_j$ in the criteria level B with respect to the upper-level criterion or goal A.

Table 2. First-level indicator judgment matrix.

	Organizational Benefits	Management Benefits	Product Benefits	Strategic Benefits
Organizational Benefits	1.00	0.50	0.50	2.00
Management Benefits	2.00	1.00	1.00	3.00
Product Benefits	2.00	1.00	1.00	3.00
Strategic Benefits	0.50	0.33	0.33	1.00

below shows the judgment matrix for first-level indicators constructed based on expert survey results. The detailed scoring sheets are available from the corresponding author upon reasonable request.

(3)

Hierarchical Single Ranking and Consistency Testing

There are four methods for calculating weights: the eigenvector method, the summation method, the root method (geometric mean method), and the least squares method. By using the eigenvector method, the maximum eigenvalue ${\lambda }_{max}$and eigenvector $\omega$ can be obtained simultaneously. Normalizing the eigenvector yields the importance weight vector W.

$$A\omega ={\lambda }_{max}\omega$$

$$W={\displaystyle \frac{\omega }{\sum {\omega }_i}},(i=\mathrm{1,2},3\dots \dots n)$$

Consistency testing is used to verify the reasonableness of the judgment matrix. When CR < 0.10, the consistency of the judgment matrix is considered acceptable.

$$CI={\displaystyle \frac{{\lambda }_{max}-n}{n-1}}$$

$$CR={\displaystyle \frac{CI}{RI}}$$

From the first-level indicator judgment matrix, the weight vector is obtained: $W={(0.19, 0.35, 0.35, 0.11)}^T$, ${\lambda }_{max}=4.0104$, $CR=0.0039<0.10$, which satisfies the consistency test.

The same procedures were applied to construct judgment matrices for second-level indicators within each criterion category. Four second-level judgment matrices were established: Organizational Benefits (2 × 2), Management Benefits (8 × 8), Product Benefits (5 × 5), and Strategic Benefits (4 × 4). All matrices passed consistency tests with CR values below 0.10. The complete second-level judgment matrices and their consistency check results are available from the corresponding author upon reasonable request.

The expert panel consisted of 12 professionals with expertise in construction management, BIM technology, and intelligent construction applications. To ensure response independence, experts completed the online questionnaire individually without group discussion, and were not informed of other participants’ responses during the data collection period.

(4)

Synthesizing Weights to Obtain Evaluation Indicator Weights

The second-level indicator judgment matrices require the same operations as in steps (2) and (3), which are not presented here. Finally, the weights of the first-level and second-level indicators are synthesized to obtain absolute weights. The indirect benefit evaluation system is shown in

Table 3. Indirect benefits evaluation system.

Evaluation Object	Criteria Level	Criteria Level Weight	Indicator Level	Indicator Level Weight (Relative)	Indicator Level Weight (Absolute)
Indirect Benefits of Intelligent Construction Site Application	Organizational Benefits	0.19	Labor Savings	0.75	0.14
			Organizational Flexibility	0.25	0.05
	Management Benefits	0.35	Reduced Changes	0.13	0.05
			Improved Communication Efficiency	0.13	0.05
			Reduced Rework	0.22	0.08
			Efficient Project Decision-making	0.13	0.05
			Reduced Omissions/Errors	0.22	0.08
			Streamlined Business Processes	0.07	0.03
			Fewer Lawsuits/Claims	0.05	0.02
			Risk Control	0.05	0.02
	Product Benefits	0.35	Shortened Schedule	0.16	0.06
			Quality Improvement	0.3	0.1
			Enhanced Safety	0.3	0.1
			Visualization in Construction	0.15	0.05
			Sustainable Construction	0.09	0.03
	Strategic Benefits	0.11	Talent Development	0.23	0.02
			Enterprise Competitiveness	0.23	0.02
			Strategic Alliance	0.12	0.01
			Customer Satisfaction	0.42	0.05

below. The detailed evaluation questionnaire is available from the corresponding author upon reasonable request.

Based on the application results of the intelligent construction site of the project, review experts will be invited to evaluate the application benefits of the intelligent construction site of the project during the project acceptance, with a full score of 10 points for each indicator. The benefit grades and corresponding measurements of intelligent construction sites are shown in

Table 4. Level of benefits performance.

Level	Excellent	Good	Fair	Fail	Poor
Measurement	(9, 10]	(7, 9]	(5, 7]	(3, 5]	(0, 3]

below.

4. Case Study and Results

4.1. Case Background

This research is based on the Nanjing International Expo Center Phase III project, which is a continuation project of the phase I and II of the International Expo Center in Jianye District, Nanjing City. The project covers an area of approximately 84,200 square meters, with a total construction area of about 385,900 square meters, including about 243,400 square meters above ground and about 142,500 square meters underground. The maximum height of the building reaches 128 m. The main structure of the building includes a 27-story hotel and office building, two 28-story office buildings and a single-story exhibition hall. It also features a two-story underground parking lot and functional rooms, with over 2400 parking spaces. In the middle, there is a 400 m long commercial pedestrian street.

It is connected to the expansion part of the first phase of the Nanbo Center through the above-ground and underground passages spanning Jinsha West Road. The total investment of this project is approximately 3.87 billion yuan. Construction began in June 2016, and it was completed and put into use in June 2020. The application cost of this project includes technical service fees, project management fees, software and hardware purchase fees, as well as employee training fees. For specific information, please refer to

Table 5. Project cost statistics table.

Cost Category	Technical Service Fee	Project Management Fee	Software and Hardware Purchase Fee	Employee Training Fee
Amount (RMB/10,000 yuan)	229.5	195	1.4	11.08

. This project features a large-scale, high construction difficulty and strict construction requirements. The renderings and completion drawings are shown in Figure 3. In view of the above characteristics, the project has introduced information technology from multiple aspects during the actual construction process, providing a guarantee for the smooth realization of various management goals.

4.2. Intelligent Construction Site Implement

During actual construction, this project incorporated information technologies across multiple aspects to ensure the successful achievement of various management objectives. A BIM construction model was established, with BIM technology applied to visual technical briefings, 4D simulation of construction processes, and 5D integrated project management, thereby forming a comprehensive BIM database. Concurrently, on-site data collected during the construction phase—including project progress, quality, cost, and safety information—were integrated to establish an intelligent construction site management cloud platform, as illustrated in Figure 4. This platform encompasses multiple business management modules, including project overview, digital construction sites, intelligent analysis, safety management, green construction, quality management, technical management, production management, business management, and party building activities. The platform monitors real-time transmission and dynamic visualization of on-site data, enabling refined and intelligent management of various data types throughout the construction process.

Safety management employs infrared sensing technology for edge protection monitoring at construction openings, IoT sensors for overload warning on unloading platforms, and intelligent monitoring systems for critical hazards such as deep foundation pits and high formwork. Video surveillance systems, mobile safety inspection applications, and smoke detection devices are also integrated. Personnel management implements a real-name registration system for workers who access the site through card-based identification devices, with real-time workforce statistics displayed on LED screens. VR-based safety training equipment is deployed on-site, and workers engaged in hazardous operations wear smart helmets equipped with GPS positioning. Equipment management involves installing black boxes, sensors, and visualization devices on tower cranes and construction elevators to monitor operational status in real time, with data transmitted to a cloud platform. A 3D collision avoidance algorithm is employed to prevent accidents in multi-crane operations. Environmental management utilizes monitoring instruments to track PM2.5, PM10, and other air quality parameters, automatically activating spray-based dust suppression systems when thresholds are exceeded. Water and electricity consumption is also monitored and analyzed through intelligent systems.

4.3. Benefit Results

Applying discounted cash flow analysis with a social discount rate of 8%, the project achieved an ROI of 102.7%. As shown in

Table 6. Return on Investment (ROI) Calculation with Discounted Cash Flow Analysis.

Item	Year/Detail	Nominal (10,000 RMB)	Discount Factor	PV (10,000 RMB)
I. Cost Present Value Calculation
Annual Cost Components:
Technical Service Fee	2017–2020	57.38/yr	—	—
Project Management Fee	2017–2020	48.75/yr	—	—
Software and Hardware	2017–2020	0.35/yr	—	—
Employee Training	2017–2020	2.77/yr	—	—
Annual Total		109.24	—	—
Year-by-Year Discounting:
	2017 (t = 1)	109.24	0.9259	101.15
	2018 (t = 2)	109.24	0.8573	93.66
	2019 (t = 3)	109.24	0.7938	86.72
	2020 (t = 4)	109.24	0.735	80.3
Cost Subtotal (C)		436.98		361.83
II. Benefit Present Value Calculation
B1. Construction Period Savings:
Clash Detection Savings	100 × 2	200	—	—
Schedule Reduction	387,000 × 36/1460 × 4%	381.7	—	—
Subtotal (at t = 4)		581.7	0.735	427.57
B2. Operation Period Savings:
Maintenance Labor	400 days × 150 yuan	6.00/yr	—	—
Maintenance Material	40 × 7000 yuan	28.00/yr	—	—
Annual Savings		34	—	—
Annuity PV (50 yr, 8%)	(1–1.08−50)/0.08	—	12.2335	415.94
Discount to t = 0		415.94	0.735	305.73
Benefit Subtotal (B)				733.29
III. ROI Calculation
Total Benefits PV (B)				733.29
Total Costs PV (C)				361.83
Net Present Value (B-C)				371.46
ROI = (B-C)/C × 100%				102.70%
IV. Sensitivity Analysis
Discount Rate		Cost PV	Benefit PV	ROI
6%		378.55	885.25	133.90%
8% (Baseline)		361.83	733.29	102.70%
10%		346.29	627.56	81.20%
Parameter			Value
Social Discount Rate			8%
Base Year			2017 (t = 0)
Construction Period			4 years
Operation Period			50 years

, the present value of total costs is RMB 3.6183 million, while the present value of total benefits is RMB 7.3329 million, yielding a net present value of RMB 3.7146 million. The positive ROI demonstrates that intelligent construction site applications deliver substantial economic returns. Sensitivity analysis indicates that even under a higher discount rate of 10%, the ROI remains positive at 81.2%, confirming the robustness of the economic benefits. Based on the application outcomes of the intelligent construction site, review experts and project owners were invited to evaluate the benefits of intelligent construction site application during project implementation, with each indicator scored on a scale of 10.

In the AHP-based calculation of indirect benefits for intelligent construction sites, evaluation factors for indirect benefits during intelligent construction site investment were identified, and corresponding measurements for each category of evaluation factors were established. A questionnaire was designed for this case study and distributed to the experts. Valid returned questionnaires were processed by removing the highest and lowest scores before calculating the average score for each measurement indicator. The final measurement score for this case study was obtained, with the project achieving an overall indirect benefit score of 9.42, as shown in

Table 7. Expert Scoring Data for Indirect Benefits.

	Criteria Layer	Criteria Weight	Indicator Layer	Indicator Weight (Relative)	Indicator Weight (Absolute)	Average Expert Score	Weighted Average Expert Score
Indirect Benefits of Intelligent Construction Site Application	Organizational Benefits	0.19	Labor Savings	0.75	0.14	9	1.26
			Organizational Flexibility	0.25	0.05	8	0.4
	Management Benefits	0.35	Reduced Changes	0.13	0.05	8	0.4
			Improved Communication Efficiency	0.13	0.05	10	0.5
			Reduced Rework	0.22	0.08	10	0.8
			Efficient Project Decision-making	0.13	0.05	8	0.4
			Reduced Omissions/Errors	0.22	0.08	10	0.8
			Streamlined Business Processes	0.07	0.03	8	0.24
			Fewer Lawsuits/Claims	0.05	0.02	10	0.2
			Risk Control	0.05	0.02	8	0.16
	Product Benefits	0.35	Shortened Schedule	0.16	0.06	10	0.6
			Quality Improvement	0.30	0.10	10	1
			Enhanced Safety	0.30	0.10	10	1
			Visualization in Construction	0.15	0.05	10	0.5
			Sustainable Construction	0.09	0.03	8	0.24
	Strategic Benefits	0.11	Talent Development	0.23	0.02	8	0.16
			Enterprise Competitiveness	0.23	0.02	9	0.18
			Strategic Alliance	0.12	0.01	8	0.08
			Customer Satisfaction	0.42	0.05	10	0.5
Total							9.42

. According to the evaluation criteria, this rating qualifies as “excellent,” demonstrating significant benefits compared to similar construction projects that did not employ intelligent construction site technologies.

5. Discussion

5.1. Analysis of Direct Benefits

The case study revealed an ROI of 102.7% for intelligent construction site application using discounted cash flow analysis, providing compelling evidence for the economic viability and superiority of this technology. This result indicates that for every RMB 1 invested in intelligent construction site technology, the project generates approximately RMB 2.03 in present value returns. Examining the temporal distribution of benefits, the operational phase yields approximately three times the benefits of the construction phase, indicating a pronounced long-tail effect in value creation. Although technology deployment is concentrated during construction, the resulting benefits extend throughout the entire operational lifecycle. This “early investment, long-term return” pattern carries significant implications for construction enterprises’ investment decisions: focusing solely on short-term input–output ratios during the construction phase may substantially underestimate the true value of intelligent construction sites. Decision-makers should adopt a whole-lifecycle perspective when evaluating such investments.

The cost structure reveals noteworthy application patterns. Technical service fees and management fees constitute most costs, while software and hardware investments represent a remarkably small proportion. This finding contradicts the conventional assumption that intelligent construction sites require substantial hardware investment. For small- and medium-sized construction enterprises, this observation carries important implications: the barrier to intelligent construction site adoption lies not in hardware infrastructure, but in the alignment of technical service capabilities and management competencies. Enterprises can reduce initial investment pressure through service procurement, offering a more flexible pathway for the widespread adoption of intelligent construction site technologies.

5.2. Analysis of Indirect Benefits

The indirect benefit evaluation results illuminated the core value proposition of intelligent construction site technology. Management benefits and product benefits received the highest weights (0.35 each), collectively accounting for 70% of the total weight. This reflects the construction industry’s primary expectations for intelligent construction sites: enhanced project management and improved product quality.

The disparity between “full-score” and “non-full-score” items in expert ratings merits careful consideration. Indicators such as quality improvement, safety enhancement, rework reduction, error minimization, schedule compression, visual construction, communication efficiency, and litigation/claim reduction received perfect scores. These represent “visible, measurable, and controllable” benefits—collision detection reduces rework, real-time monitoring enhances safety, and information platforms improve communication efficiency, all delivering immediate and tangible results. Conversely, indicators such as organizational flexibility, strategic alliances, talent development, and sustainable construction received relatively lower scores. These represent “soft” benefits that require longer periods to materialize. This discrepancy suggests that current intelligent construction site applications remain predominantly at the technical tool level, with their deeper impacts on organizational capabilities and strategic competitiveness yet to be fully realized.

The lowest weight assigned to strategic benefits (0.11) also warrants reflection. This may not indicate that strategic benefits are unimportant, but rather reflects the industry’s current focus on short-term, tangible project-level benefits, with limited appreciation for long-term strategic value. Benefits such as enhanced enterprise competitiveness, talent cultivation, and strategic alliance formation exhibit lagged and cumulative characteristics that are difficult to fully manifest within a single project cycle, yet they constitute essential foundations for sustainable enterprise development. As intelligent construction site applications mature and industry understanding deepens, the importance of strategic benefits may require reassessment.

It should be noted that 10 of 19 indicators received scores of 9 or above, reflecting a ceiling effect in the evaluation results. This pattern can be attributed to several contextual factors. First, the Nanjing International Expo Center Phase III project served as a flagship demonstration project for intelligent construction site technology, receiving above-average implementation resources and management attention. Second, the evaluation was conducted at project acceptance when the benefits were most visible and salient to evaluators. Third, the project owner organization demonstrated strong commitment to achieving successful outcomes, which may have contributed to higher-than-typical performance across multiple dimensions.

These contextual factors suggest that the absolute score of 9.42 may not be directly generalizable to routine intelligent construction site implementations, particularly for smaller enterprises or less complex projects. The primary transferable contribution of this study lies in the evaluation framework and indicator weight system, rather than the absolute scores obtained from this specific case.

5.3. Implications for Intelligent Construction Site Adoption

The question of benefit certainty receives preliminary resolution. As noted in the introduction, uncertainty arising from the diverse composition of intelligent construction site benefits has substantially dampened stakeholders’ enthusiasm for adoption. Through dual validation of direct and indirect benefits, this study provides empirical evidence addressing this uncertainty. The 102.7% ROI combined with the 9.42-point indirect benefit score collectively demonstrate that intelligent construction site applications generate significant positive outcomes from both financial return and multidimensional benefit perspectives. This conclusion helps alleviate stakeholders’ concerns regarding technology investment and strengthens their confidence in adopting intelligent construction site technologies.

Then, the quantification of indirect benefits renders “hidden value” visible. Traditional project evaluation tends to emphasize financial indicators, while indirect benefits such as management efficiency improvement, product quality enhancement, and organizational capability optimization are often overlooked due to quantification difficulties. By incorporating these indirect benefits into the evaluation framework and assigning appropriate weights through the AHP method, this study enables decision-makers to develop a more comprehensive understanding of intelligent construction site value. Although these indirect benefits cannot be directly recorded in financial statements, they represent important sources of enterprise core competitiveness. Their explicit articulation helps encourage more enterprises to attend and invest in intelligent construction site technologies.

The evaluation framework provides decision-support tools for intelligent construction site promotion. The ROI indicator addresses the question of “whether investment is worthwhile,” providing a clear financial benchmark for investment decisions. The AHP evaluation addresses the question of “where the value lies,” helping enterprises identify the core value propositions of intelligent construction sites. This combination of “hard metrics and soft evaluation” offers differentiated reference points for various types of decision-makers, facilitating the transition of intelligent construction sites from demonstration projects to large-scale application.

However, several factors warrant consideration when attributing the observed benefits specifically to intelligent construction site applications. Project-level characteristics, including contractor capability and leadership commitment, may have influenced both the magnitude of ROI and expert perceptions. In this study, the use of a single general contractor with consistent management practices throughout the project lifecycle partially controlled for organizational variability. The adoption of ROI as a ratio-based metric also normalizes for project scale, enhancing cross-project comparability. Leadership commitment, rather than being viewed as a confounder, is better understood as an enabling condition inherently linked to successful technology implementation—the benefits observed thus represent achievable outcomes when organizational support aligns with digital transformation efforts. Additionally, the intelligent construction site platform integrates multiple technologies (BIM, IoT, cloud computing) by design; the evaluation captures their synergistic effects rather than isolating individual contributions. Nevertheless, the absence of a direct control group limits strict causal inference. The expert ratings provide implicit benchmarking based on professional experience with both traditional and intelligent construction practices, but future research employing quasi-experimental designs with matched control projects would strengthen causal attribution.

5.4. Research Contributions

A key contribution lies in establishing a comprehensive evaluation framework that integrates direct and indirect benefits. Existing research predominantly evaluates intelligent construction site benefits from a single dimension, focusing either on financial analysis or qualitative description, lacking a systematic comprehensive evaluation framework. This study extends the benefit dimensions to encompass economic, organizational, management, product, and strategic aspects, forming a dual-track “ROI + AHP” evaluation model that more comprehensively reflects the multidimensional value of intelligent construction sites. This evaluation framework addresses the methodological gap in existing research by providing an operational analytical framework for intelligent construction site benefit evaluation.

Building upon this framework, the study systematically develops an indirect benefit indicator system for intelligent construction sites. From four dimensions—organizational, management, product, and strategic—this study identifies 19 secondary indirect benefit indicators, forming a hierarchically structured and content-complete evaluation indicator system. This indicator system concretizes the abstract concept of “indirect benefits” into evaluable and comparable specific indicators, providing a referenceable analytical tool for subsequent research and practical applications.

Beyond theoretical construction, this study validates the effectiveness of the evaluation framework through an actual project. Empirical research conducted on the Nanjing International Expo Center Phase III project demonstrates the operability of the evaluation framework. The case study results indicate that intelligent construction site applications generate significant positive outcomes in both direct and indirect benefits. This empirical validation provides practical evidence for the broader application of the evaluation framework, enhancing the persuasiveness and practical guidance value of the research conclusions.

From a practical standpoint, this study provides quantitative support for intelligent construction site adoption decisions. The uncertainty surrounding intelligent construction site benefits has long been a significant factor constraining their promotion. Through quantitative analysis, this study transforms the ambiguous question of “whether intelligent construction sites are beneficial” into comparable and verifiable specific data, providing scientific evidence for stakeholders’ investment decisions. This contribution facilitates the construction industry’s transition from the hesitation stage of “whether to adopt intelligent construction sites” to the advancement stage of “how to better utilize intelligent construction sites.”

6. Conclusions

As a critical vehicle for digital transformation in the construction industry, intelligent construction sites have faced adoption challenges primarily due to uncertainty surrounding their application benefits. This study focuses on benefit analysis of intelligent construction site applications, establishing a comprehensive evaluation framework that integrates direct and indirect benefits, with empirical validation conducted through the Nanjing International Expo Center Phase III project. The findings demonstrate that intelligent construction site applications yield significant positive outcomes. Regarding direct benefits, applying a social discount rate of 8%, the project achieved a return on investment (ROI) of 102.7%, with a net present value of RMB 3.7146 million. The analysis reveals that construction period benefits (PV = RMB 4.2757 million) exceed operation period benefits (PV = RMB 3.0573 million) in present value terms, highlighting the importance of early-stage BIM coordination and clash detection in generating economic returns. For indirect benefits, expert evaluation based on the AHP method yielded a score of 9.42, reaching the “excellent” rating. Notably, management benefits and product benefits received the highest weights (0.35 each), while indicators such as quality improvement, safety enhancement, and rework reduction obtained perfect scores, fully reflecting the core value of intelligent construction sites in elevating project management standards and product quality.

The primary contributions of this study include establishing a comprehensive benefit evaluation framework for intelligent construction sites encompassing five dimensions—economic, organizational, management, product, and strategic—thereby transcending the limitations of existing research that predominantly focuses on single financial indicators; systematically identifying 19 indirect benefit indicators that transform abstract indirect benefits into evaluable and comparable analytical tools; and validating the operability and effectiveness of the evaluation framework through an actual project, providing empirical support for quantitative assessment of intelligent construction site benefits.

This study has certain limitations. A key limitation of this study is the absence of a directly matched control project, which constrains causal inference. The observed benefits may be partially attributable to factors beyond intelligent construction site implementation, including project team capabilities, favorable site conditions, or concurrent management innovations. Future research should employ quasi-experimental designs comparing multiple intelligent construction sites and non-intelligent construction site projects with propensity score matching to isolate the causal effect of intelligent construction site technologies. The representativeness of a single case is inherently limited, and conclusions drawn from large-scale public building projects require cautious verification when extended to other project types. The expert sample size is relatively constrained, and the AHP method itself carries inherent subjectivity. Furthermore, operational phase benefits are based on projected data, and actual benefits may deviate due to various factors. Future research directions include conducting multi-case, cross-category comparative studies to verify the generalizability of the evaluation framework; establishing dynamic benefit tracking mechanisms to observe benefit evolution patterns throughout the whole lifecycle; and exploring key factors influencing the realization of intelligent construction site benefits to provide more targeted application guidance. This study offers scientific evidence for stakeholders’ investment decisions regarding intelligent construction sites, with the aspiration of promoting broader adoption of intelligent construction site technologies and facilitating digital transformation and high-quality development in the construction industry.