Supervision method of indoor construction engineering quality acceptance based on cloud computing

Jian Zhang

doi:10.1515/jisys-2022-0056

Article Open Access

Supervision method of indoor construction engineering quality acceptance based on cloud computing

Jian Zhang

Published/Copyright: June 27, 2022

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From the journal Journal of Intelligent Systems Volume 31 Issue 1

Abstract

As an important part of Chinese economy, the construction industry has a great contribution to the economy, and plays an important role in the Chinese economic development. Therefore, it has certain research significance for the quality acceptance supervision method of construction engineering. This article takes Shenzhen S project as an example, combined with cloud computing, discusses the quality acceptance and supervision methods of indoor construction projects. In the introduction to the technical part, this article first briefly introduces the definition of cloud computing and then introduces the particle swarm algorithm and traditional genetic algorithm in the cloud computing task scheduling method. The algorithm is introduced into the quality acceptance of indoor construction projects to obtain the quality, most efficient method for acceptance supervision. The experimental part of this article takes S project as the research object and the residents’ satisfaction with the project as the experimental purpose. Finally, through statistical analysis, it is concluded that the residents’ satisfaction with S project reaches more than 70%.

Keywords: cloud computing; indoor building; quality acceptance; supervision method

1 Introduction

This is the development trend of the next-generation IT architecture. Compared with the traditional computer technology, joint use of resources, and on-demand payment, cloud computing is a major innovation in today’s information field. At present, with the rapid development of the Internet, big data and cloud computing are widely used increasingly in many industries. Facts have proved that cloud computing can create large social and economic benefits. While Chinese economy continues to develop, all walks of life are also continuing to develop. With the rapid development of the construction industry, its production and management methods have changed, from the original extensive type to increasingly refined construction methods and acceptance methods. For the construction industry, the refinement of construction and quality acceptance determine the development prospects of construction enterprises to a certain extent. The interior construction project is closely related to people’s daily life, work, and study, and its importance is obvious. Therefore, the contemporary society has higher and higher requirements for the quality of interior construction projects, and the control of quality details is becoming increasingly strict. A more accurate and reliable indoor construction project quality acceptance supervision system is urgently needed for the development of the indoor construction industry. With its own powerful functions, cloud computing plays an important role in the refined development of construction engineering quality acceptance. Therefore, it has certain practical significance to study the quality acceptance supervision method of indoor construction engineering based on cloud computing.

The continuous development of scientific research makes the development of cloud computing faster and faster. Among them, Liu and Srinivasamurthy discovered that cloud computing would cause information security problems, so he mainly studied how to solve these problems. He described the characteristics of cloud computing, as well as its advantages and disadvantages, and finally gave an analysis of the security issues of cloud computing [1]. Tsai and Lo proposed an efficient distributed mobile cloud computing service certification scheme through their research. They proposed to solve the security problem based on the bilinear pairing cryptosystem and dynamic random number generation, which provide a guarantee for the security and convenience of cloud computing services [2]. Tang and Feng studied a framework combining cloud computing and high-performance computing, which can be applied in parallel map projection of large spatial data. They found that if cloud computing could be combined with accelerated computing capabilities, the development of high-performance computing in cloud computing could be promoted [3]. Du et al. found that if you want to solve the problem of cloud computing offloading, it is necessary to use a low-complexity suboptimal algorithm, while the offloading decision can be achieved by randomization, resource allocation is achieved by Lagrangian dual decomposition [4]. Cao et al. proposed a new model of ICT infrastructure optimization based on cloud computing. The overall model is more flexible and the cost is relatively low. He compared the model with mature algorithms and proposed an improved algorithm [5]. Wong et al. suggested that interior construction engineering quality (IEQ) acceptance predictions are critical for sustainable building development, and they proposed an open acceptance model that can evaluate IEQ. This model is not only flexible but can also solve the difficulties encountered in the modeling process [6]. Facchinetti et al. believe that indoor monitoring using mobile cloud computing technology will be more automated, flexible, and efficient, and it will also enrich the user experience. To this end, he developed a prototype of an application program called IPSOS Assistant that supports mobile cloud computing, which is used to monitor and manage indoor environment conditions [7]. However, the above research also has some shortcomings, namely, the research content is not comprehensive enough, the experiment is more complicated, the amount of calculation is large, and the connection between cloud computing and indoor construction engineering quality acceptance supervision methods is not close enough.

The innovation of this article lies (1) on the basis of previous research, combined with the advantages of cloud computing technology, it is closely linked with the quality acceptance of indoor construction projects, and the refined supervision method of acceptance quality is studied. (2) An indoor construction project (hereinafter referred to as S project) is selected as a case study, and the feasibility of the quality acceptance supervision method of indoor construction engineering based on cloud computing is studied in the case study.

2 Method of building engineering quality acceptance based on cloud computing

2.1 Concepts and characteristics of cloud computing

Cloud computing is a new generation of information technology, it is a powerful information processing technology. The service model of cloud computing must be based on the network, which is also called computer network, and can process large amounts of data in a lot of time [8]. Cloud computing is an innovation in the information age. Moreover, a major advance in the information age. Cloud computing will soon become popular all over the world. Cloud computing can not only improve work efficiency, but also has high scalability, and most importantly, it can make the user experience soar. and have great flexibility and ease of use. Cloud computing has the following technical characteristics:

Extensiveness of network access. At any time and any place, if there is a network, there is no need for complicated software and hardware installation. Any simple equipment connected to the network, such as mobile phones, monitors, etc., can be directly connected to the cloud platform to flexibly use what the market needs, existing resources or new services and resources.
Sustainability. Users can take advantage of the conditions for rapid development of application software to more easily expand the existing resource services and new resource services. For example, if there are equipment failures in the cloud system, the computer cloud dynamic expansion function can now be used for efficient expansion of other servers. In this way, you can ensure that the use of the computer is not hindered and the work is completed smoothly. Cloud computing can effectively expand various computer applications, while dynamically expanding virtual resources, improving the functional level of computing clouds [9].
Elasticity. The cloud provider or the cloud user determines the requirements in advance, and the cloud user can automatically expand IT resources on demand by relying on the initial conditions of the cloud service operation. The elastic feature is a relatively straightforward and important feature of cloud computing. The main reason is that it can better reduce investment and costs and provide cloud users with more flexible and convenient network resource services.
Resource sharing. Computer resources are centrally stored and aggregated in the cloud, and then rationally allocated according to user needs. It can provide resource services for multiple consumers through the multitenant model. Physically, resources exist in a distributed sharing manner, but they are ultimately presented to users in the form of a single whole, and ultimately achieve resource sharing and reuse on the cloud, forming a resource pool.
Pay on demand. Cloud users can individually access network resources in the cloud according to the authorization of the supplier or provider. For network resources that have been configured in advance, the access of cloud users is fully automated, and cloud users can pay according to their needs, use cloud services to get IT resources you want [10]. The characteristics of cloud computing are shown in Figure 1:

Figure 1

Cloud computing characteristics.

There are certain links and subtle differences between these five types of technical characteristics, such as some subtle differences in task processing.

2.2 Key technologies of cloud computing

The key technologies of cloud computing can be divided into various types according to different application objects. This article selects three basic technologies of cloud computing to introduce, including data center technology, virtualization technology, and resource scheduling technology.

Data center technology: The data center includes physical and virtual servers, such as databases, communications, network facilities, and applications. The physical RESOURCES refer to the infrastructure placed in computer equipment or network systems and equipment, as well as hardware systems and operating systems [11]. Data centers have high availability for cloud users. To maintain this high availability, data centers adopt increasingly redundant designs to cope with system failures. The data centers can be automated, remotely operated, and managed. It has system equipment specially used to store large data information, including physical and virtual, to meet the large capacity storage requirements [12].
Virtualization technology: Virtualization technology is a technology that transforms physical IT resources into virtual IT resources. Virtualization performs the best integration of physical servers and resource replication. In addition to the virtualization of some physical devices and operating systems, virtualization can also be managed, so that large user operating systems and software can be used seamlessly in virtual environments, no additional customization, configuration or modification is required. The virtualized cloud computing platform completely abstracts physical and virtual resources into virtualized components, to reconfigure, expand, and release resources, monitor, and manage all IT resources [13].
Resource planning technology: An important technology of computer cloud, which is different from machine virtualization technology. It integrates natural resources to form a resource group and carries out resource planning in the resource library through the level of resource management (intermediate software management). Cloud computing resource management should be responsible for tasks such as work management, user management, and security management to achieve multifunction [14].

2.3 Cloud computing task scheduling algorithm

2.3.1 Particle swarm optimization algorithm

Particle Swarm Optimization (PSO) is very advanced, and the basic idea is fishing behavior. The PSO algorithm can get the best solution to the problem in the communication between people. Any particle in the PSO algorithm represents a feasible solution in the search domain, and the totality can be understood as an overall possible solution. The initial population is created randomly. In the process of simulating the foraging behavior of a group of birds, the position of each particle in the population will continue to change. Each particle will record the best position it experienced during the flight, while learning from the particles with the best position in the population [15]. Through each particle’s continuous learning and cooperation with each other, the entire population will continue to approach the optimal solution. The calculation model of the PSO algorithm is as follows:

When considering a minimal optimization problem:

(1) min ρ = f ( x ) , x ∈ k ⊂ R a .

Suppose there are N kinds of particles in a certain particle population, and they all fly in the range of M at a specific speed. Note that the flight state of any particle b at time T is as follows:

Speed:

(2) V t = ( V b 1 t , V b 2 t … V b d t , V b n t ) , V b d t ∈ [ V min , V max ] ,

where V _min and V _max represent the slowest and fastest speed of the particle, respectively.

(3) x b t = ( x b 1 t , x b 2 t ..... . x b d t ... . x b n t ) , x b d t ∈ [ x min , x max ] ,

where x _min and x _max, respectively, represent the lower limit and upper limit of the particle’s flight range. The best position the particle has experienced:

(4) K b t = ( K b 1 t , K b 2 t ⋯ K b d t ..... . K b n t ) .

The best position the particle swarm has experienced:

(5) K g t = ( K g 1 t , K g 2 t … K g b t … K g b t … K g n t ) .

Then, the position velocity of particle b at time t + 1 depends on the following transformation formula:

(6) V b d t + 1 = v b d t + c 1 l 1 ( k b d t − x b d t ) + c 2 l 2 ( k g b t − x b d t ) ,

(7) x b d t + 1 = x b d t + v b d t + 1 .

In formulas (6) and (7), 1 ≤ b ≤ N , 1 ≤ d ≤ n , c 1 , c 2 are two acceleration constants called cognitive learning factor and social learning factor and l 1 , l 2 are two random values that are uniformly distributed between 0 and 1 and do not affect each other. The best position that particle b has passed at time t k b t depends on the following formula:

(8) k b t = k b t − 1 , f ( x ) ≥ f ( k b t − 1 ) .

And the best position of the entire population at time t k g t can be determined by the following formula:

(9) k g t = arg min { f ( k o t ) , f ( k 1 t ) .. . , f ( k n t ) } .

The above is the PSO algorithm model, which is usually called the basic PSO algorithm. Each part of the particle individual and the group is checked and balanced with different weights to obtain the optimal position.

2.3.2 Traditional genetic algorithm

Genetic algorithm is a random search algorithm that obtains the optimal solution of the problem by imitating the laws of biological evolution in nature. In this regard, it is somewhat similar to the PSO algorithm [16]. Global optimization of things is achieved through stochastic search and optimization methods. The genetic algorithm imitates the evolutionary process of biology and directly performs a series of related operations on structural objects. During this period, it strictly follows the cruel rule of survival of the fittest in nature, selects a better group in the evolution of generations, and finally selects the best individual. In this process, the group evolves from generation to generation based on the fitness function, and gradually moves closer to the optimal individual. After evolution, the optimal individual generated in the evolution process is decoded according to the prescribed coding rules, and the optimal solution to the specific problem can be obtained. The above is the basic idea of traditional genetic algorithm [17]. The following are the basic steps of a traditional genetic algorithm:

Assuming that g jobs are assigned to the nth virtual machine, the time F(n) required for the virtual machine to complete the tasks assigned to it is:

(10) f ( n ) = ∑ i = 1 g L i n .

where L i n represents the time taken by the nth virtual machine to complete the ith task assigned to it. From formula (10), it can be concluded that the total execution completion time TF of all tasks is:

(11) TF = Max n = 0 m − 1 F ( n ) .

Among them, m represents the number of virtual machines. The average load KL and load balance difference PL of the total virtual resources are expressed as:

(12) KL = ∑ n = 0 m − 1 F ( n ) / m ,

(13) PL = ∑ n = 0 m − 1 ( f ( n ) − KL ) / m .

According to the above, the smaller the KL, the more similar the load of each virtual machine, the more reasonable the task scheduling strategy, and the higher the resource utilization; at the same time, the total task completion time TF calculated by formula (11) will also be smaller. Based on the above, the available fitness function is:

(14) Fitness = 1 1 + KL + TF ,

where KL represents the virtual machine load, and TF represents the total task completion time. Crossover and mutation: Set the probability of adaptive mutation, and execute the mutation engine in routing mode according to the relative applicability of the specified virtual machine. In the virtual resource set of available virtual machines, the parameters between them cannot be completely consistent. Relative fitness of the rth virtual machine, VRF^r, the calculation formula is as follows:

(15) VRF r = V mips r TV mips ,

(16) TV mips = ∑ n = 0 m − 1 V mips n .

V mips r represents the execution speed of the rth virtual machine in the virtual resource set [18].

The basic steps of the entire genetic algorithm are shown in Figure 2:

Figure 2

Genetic algorithm step diagram.

As can be seen from Figure 2, the whole process of the genetic algorithm is not complex and can be completed in a limited time. Cloud computing task scheduling algorithm has powerful data processing and task scheduling functions. Next this article will analyze the indoor building quality acceptance supervision experiment based on the cloud computing task scheduling algorithm.

3 Case experiment and analysis

3.1 Basic overview of the experimental object S project

The S project of Shenzhen HK Company is located in the south section of Qingping Expressway in Longgang District, Shenzhen, and is an indoor residential area project. From the beginning of the development of the project to the present, the surrounding development of the project has matured, and the urban supporting facilities have developed rapidly.

The project has good potential. The goal of the S project is to build a high-end residential community with sound infrastructure, complete supporting facilities, exquisite and beautiful houses, and good architectural quality. The specific construction land area indicators of this project are shown in Table 1:

Table 1

Area indicators of construction land

Land area (m²)	34,856	Building area above ground 88652.6 m²	Residential area (m²)	84,536
Total surface area (m²)	126780.9		Business along the street (m²)	3,600
Gross floor area (m²)	3,068		Door post (m²)	80
Basement area (m²)	5,270		Property management room (m²)	486
Volume rate	2.8	Underground building area 26954.31 m²	Floor 1 (m²)	20,089
Coverage	13.02%	Underground building area 26954.31 m²	Storage room	1,460
Greening rate	32%	Motorized parking space 800	On the ground	168
Parking space ratio	1/8	Motorized parking space 800	Under the ground	652

3.2 Experimental method

The experimental method consists of field observation method, questionnaire method, and data analysis method.

3.3 Experimental steps

3.3.1 Field observation

We observed the S project twice. The dates are mid-March and late June 2021. The purpose of on-site observation is to understand the overall evaluation of the project quality by the owner and property personnel after the project is delivered, such as whether there are building quality problems during the quality acceptance process, which failure events are more common, etc.

3.3.2 Resident questionnaire survey

Through the form of questionnaires, we specifically understood the residents’ satisfaction with the S project building and summarize the existing problems. This survey randomly distributed questionnaires to the owners and property management personnel of the S project. A total of 62 questionnaires were distributed and 58 questionnaires were recovered, of which 2 questionnaires were scrapped. Such a questionnaire survey method can effectively understand the residents’ satisfaction with the construction project. After analyzing the validity of the questionnaire, the final number of valid questionnaires is 56. The results of the questionnaire survey are shown in Table 2.

Table 2

Satisfaction survey of construction

Attributes	Options	Quantity	Percent (%)
Gender	Male	35	63
Gender	Female	21	37
Age	Under 30	8	14
	30–50 years old	32	57
	Over 50 years old	16	29
Satisfaction	Satisfied	42	75
Satisfaction	Dissatisfied	14	26

It can be seen from Figure 3 that the residents’ satisfaction with the S project is basically above 70%, which is an excellent level. It also shows that there are indeed unsatisfactory aspects of the S project. Regarding the dissatisfaction, we conducted a detailed understanding of the dissatisfied people in the form of interviews, and found that the three most generally dissatisfied areas are: structure, wall quality, and floor quality. After the interview, we made statistics on the specific number of people who were dissatisfied in these three areas by gender and age. The results are shown in Figure 4:

Figure 3

Graph of survey results.

Figure 4

Graph of statistical results.

3.4 Result analysis

Through the above picture, it can be clearly seen that people are basically dissatisfied with three aspects, namely, the structure of the S project, the quality of the walls, and the quality of the floor. It shows that the degree of the problems in these three aspects is basically the same, which is a common problem in the S project. Next we combine the PSO algorithm of cloud computing technology and traditional genetic algorithm and other task scheduling algorithms to calculate and process the relevant data of these three problems to find the problems in the quality acceptance management of these problems and the most important problems [19]. Finally, we found that human error and regulatory negligence were the main reasons for the above three problems. For this reason, we summarize the following improvement measures:

Establish a complete quality acceptance and feedback system. Quality information is the basic information reflecting the quality of construction and work. It is mainly derived from the results of project use and regression surveys or residents’ opinions; the second is from the engineering team, initial records, and other information related to the quality of the project. The third is to reflect the quality level collected by the quality control team. Doing a good job of quality acceptance and information feedback is a prerequisite for effective implementation of comprehensive quality management and an important tool for strict construction quality control. The basic requirements are accuracy, timeliness, comprehensiveness, and meticulousness. To meet these requirements, a sound quality acceptance and information feedback system is inevitable. It can be combined with cloud computing and other technologies to improve the ability to identify and process quality information, and gradually establish a sound quality acceptance and information feedback system [20].
Establish and improve the accountability mechanism for quality management. Based on the experimental results, this article concluded that establishing and improving the quality management responsibility system, making the responsibilities of all departments and project participants clear, and establishing a strict quality management responsibility accountability mechanism is essential. It includes the accountability system for leaders and technical leaders at all levels, the accountability system for administrative departments and directors, and the accountability system for employees, mainly through the following methods: carry out comprehensive quality management work and establish a quality assurance system; timely feedback information to establish a sound quality assurance management system; do a good job in various core tasks, organize regular quality inspections, and conduct regular dynamic quality analysis. Technical measures for common quality problems are: earnestly implement the reward and punishment system, praise the advanced, and if the punishment is not in place, actively carry out and organize various quality competitions, mobilize the enthusiasm of all parties, and continuously improve the sense of responsibility.
Introduce a new construction quality management model. The role of some existing quality supervision models in construction quality management is not obvious. There are various reasons, including human error, that lead to the emergence of the problem. Sometimes the supervision unit is basically in the perspective of bystanders during construction and cannot play the role of supervision well. In some developed cities, some construction projects have begun to try the third-party management company model. This type of company usually obtains the corresponding compensation for discovering quality problems during construction. In addition, in terms of architectural design, these third-party management companies are gradually the introduction forces, construction units, and design units to improve the quality of construction projects and construction design. Although the development of these companies cannot completely replace the role of the supervision unit, it can at least play a part of the function of the supervision unit’s dereliction of duty and play a supplementary and perfect role for it. Finally, for construction units, BIM technology, cloud computing, and other related technologies can be introduced, which can not only reduce leadership mistakes in decision-making but also more comprehensively control the progress of the structure, structure, and facilities of each building. This makes the project progress smoother and the construction design more efficient, thereby effectively improving the building quality acceptance and supervision capabilities to improve the building quality.

4 Discussion

There is no rule and no circle. For any construction project, quality acceptance management is the most basic and prerequisite work. If the quality acceptance management is not in place, then, once the quality problems occur in the building, it will seriously affect the benefits of the project, and in addition, it will endanger the safety of people’s lives. A poor-quality project is called a bean curd dregs project. Any bean curd dregs project appears to cause certain harm, so every participant in this project can hardly shirk the blame. For interior construction projects, quality management must be the first management concept, and no aspect can match the importance of quality. People, as another important factor, exist in the construction quality acceptance management. People can play a key role in quality management. Any construction project is done by the people and is done for the people. Therefore, the quality acceptance management of construction projects must adhere to the people-oriented principle, mobilize the enthusiasm, initiative, and sense of responsibility of each project participant in the operation process, and reduce the impact of human factors on the quality of construction. Instill the requirement of quality first in mind, and mobilize the enthusiasm of each participant to avoid human error, thereby affecting quality. After ensuring the people-oriented quality acceptance management principle, on this basis, we will continue to explore more reasonable quality acceptance supervision methods to improve quality control capabilities and ensure that every link is avoided as much as possible. As explored in this article, the cloud computing technology can be used to strengthen the monitoring and control of the quality problems of indoor construction projects and improve the quality of inspection and supervision capabilities. Only by implementing the people-oriented quality acceptance principle and using appropriate quality acceptance and supervision methods, we can achieve strict construction project quality acceptance supervision to the greatest extent.

5 Conclusion

With the rapid economic development in today’s era, interior construction projects will inevitably also develop rapidly. However, there will also be a lot of tofu dregs projects. The reason is that the quality acceptance management is not in place. Although many interior construction projects were completed, they could not be put into use in the end, or large problems occurred in the engineering. How to carry out process control and acceptance management of the quality of indoor construction projects have become a major issue in the construction industry. Although there are related quality acceptance management methods in China, with the rapid development of technology and people’s living standards, these methods have certain limitations and cannot better meet people’s quality requirements for indoor construction projects. To this end, this article takes the Shenzhen S project as an example to analyze the problems that occurred in its quality acceptance, and then, in response to the problems, combines with cloud computing technology to discuss how to effectively control the quality and acceptance management of indoor construction projects. It includes many aspects, and the work is complicated and difficult. Due to the limited level of research, there is no way to discuss them one by one. However, in any case, the research on the quality acceptance supervision methods of indoor construction projects will never stop here. Due to the limited knowledge of indoor construction quality acceptance, the content of this article may have certain limitations. The application of cloud computing technologies and related algorithms is also inadequate. This article mainly intends to play a role as a reference to the quality acceptance management of the indoor construction industry. Chinese indoor construction industry is vast and has a large market potential. People’s desire for higher quality indoor construction will definitely promote the rapid expansion of the indoor construction industry. As people have more opportunities to get in touch with indoor construction projects, the experience of acceptance management personnel will gradually accumulate. With continuous efforts in scientific research, more and better construction project quality inspection and supervision methods will appear in the future. Building quality acceptance management will directly affect people’s sense of use of buildings. Only by better building quality acceptance management can the quality of buildings become better, and the construction industry in China will become more vigorous and upward.

Conflict of interest: The author declares no conflict of interest.
Data availability statement: Data sharing not applicable to this article as no datasets were generated or analysed during the current study

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Received: 2022-02-03

Revised: 2022-03-17

Accepted: 2022-04-09

Published Online: 2022-06-27

This work is licensed under the Creative Commons Attribution 4.0 International License.

Articles in the same Issue

https://doi.org/10.1515/jisys-2022-0056

Keywords for this article

cloud computing; indoor building; quality acceptance; supervision method

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