# Mountain Rainfall Estimation and BIM Technology Site Safety Management Based on Internet of Things

Authors: Peng Liu
Journal: Mobile Information Systems
 (2021)

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## Abstract

Mountain rainfall estimation is a major source of information for determining the safety of a geographical (mountainous) area. It can be done easily by using a modeling and simulation application, BIM, which is a building information modeling tool. It helps in transforming the real-time scenarios into the construction and business models. Now, this whole process can be easily realized by the help of an evolving technology known as IoT (Internet of Things). Internet of Things is supposedly going to take over the world by the end of this decade. It will reshape the whole communication architecture. IoT is actually going to be a basis for D2D (Device to Device) communication. Here, the MTC (Machine Type Communications) are going to take place which have almost zero human involvement. Now, in order to overcome the problem that the traditional construction site safety management method is difficult to accurately estimate the rainfall, resulting in poor safety management effect, a mountain rainfall estimation and BIM technology site safety management methods based on Internet of things are proposed. Firstly, based on the Internet of Things data, the limit learning machine method is used to accurately estimate the mountain rainfall. Secondly, based on the rainfall estimation results and combined with BIM technology, the construction site safety and management model is constructed. In the end, experimental verification is carried out. The experimental results show that this method can precisely estimate the rainfall in mountainous areas, and the computational results of safety factor are basically consistent with the actual results, indicating that the safety management effect of this system is good. In this paper, I reveal the complications and drawbacks associated with the ongoing mechanisms used for mountain rainfall estimations and how to overcome them by using the new technology, i.e., Internet of Things.


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## Body

## 1. Introduction

Rainfall is an important parameter of mountain construction, which largely reflects the trend of disasters. Rainfall has a significant impact on the agricultural production, soil, water loss, and engineering applications. Accurate prediction of rainfall in an area can help agriculture and water conservancy departments improve their ability to prevent drought and flood disasters and minimize the harm. For this, we can make a use of a modeling tool BIM. After modeling the whole scenario of a geographical (mountainous) zone, one can relate the rainfall estimation and BIM technology to the Internet of Things. This paper further throws light on how the whole process is going to take place including the estimation of rainfall and making model in a BIM technology.In recent years, with the frequent occurrence of flood disasters in China, precise and timely use of meteorological data to predict rainfall has become more and more dominant [1]. The advent of the big data era has also brought new challenges to the weather forecasting industry. Meteorological data mainly comes from ground observation, meteorological satellite remote sensing, weather radar, and numerical prediction products. These four types of data mentioned earlier account for more than 90% of the total data and are directly applied to meteorological operations, weather forecasting, climate forecasting, and meteorological services [2]. For this purpose, we might take help from stream data which is a set of digitally encoded and continuous signals. Generally, data flow can be regarded as an infinite over time and is widely used in many fields, such as network public opinion analysis, stock market trend, satellite positioning, real-time financial monitoring, Internet of Things monitoring, and real-time meteorological monitoring. Along with this, there is still much room for development in the field of rainfall prediction based on large-scale meteorological flow data [3].In order to solve the problem of construction land, the stability of mountain construction has become the key to the safety and stability of mountain construction projects. Among the many aforementioned factors affecting the mountain construction stability, rainfall infiltration is the main factor influencing the slope instability and failure, especially the threat of continuous heavy rainfall to slope stability cannot be ignored. On the account of this, study the impact of rainfall on mountain construction slope stability; it has an important reference value for upgrading mountain construction safety. The main contributions of this paper are as follows:(1)
In this paper, we determining the safety of individual at a construction site using the IoT. The IoT devices are installed at constriction site and capture various parameters about the environment and surrounding conditions.(2)
The rainfall is directly affecting the safety at construction site; therefore, the estimation of rainfall for a particular region or a specific site is mandatory. We have devised a scheme for rainfall estimation.The rest of the paper is organized as follows. Section2 contains the mountain rainfall estimation based on Internet of Things and BIM technology site safety management. Section 3 contains the experimental verification, and conclusion is mentioned in Section 4.


## 2. Mountain Rainfall Estimation and BIM Technology Site Safety Management Based on Internet of Things

### 2.1. Mountain Rainfall Estimation Based on Internet of Things

With the sustained improvement and rapid growth of meteorological service informatization level, it is very essential to apply real-time meteorological data processing to meteorological information business. Furthermore, data mining technology is the most widely used and efficient method of meteorological data processing. With the support of Internet of Things technology, it is a motif of great significance to analyze the meteorological characteristics and laws of meteorological data hiding through data mining, provide meteorological researchers with knowledge decision-making based on meteorological data, and predict future meteorological changes [4].Single hidden layer feedforward neural network (SLFN) is widely used in many fields because of its ability to approximate nonlinear mapping, but it needs to set a large number of parameters artificially in the process of application, so the network will fall into local optimization due to improper selection of parameters. Huang Guangbin et al. proposed a learning algorithm of single hidden layer feedforward neural network, called limit learning machine (ELM), which does not need to adjust all the network specifications. The input weight and hidden layer deviation are given randomly at the beginning of training and fixed in the training process, while the output connection weight can be obtained by solving the least square solution of linear equations. Therefore, in order to enhance the accuracy of mountain rainfall estimation, based on the data provided by the Internet of things, the limit learning machine method is used to estimate mountain rainfall to ensure construction safety [5]. The specific estimation process is as follows.The mountain rainfall training sample set isN, where xi,ti meets xi=xi1,xi2,…,xinT∈Rn and ti=ti1,xti2,…,timT∈Rm. For a single hidden layer feedforward neural network with L hidden layer nodes, the excitation function can be expressed as(1)∑i=1Lβigiwi⋅xj+bi=oj,j=1,2,…,N,where wi=wi1,wi2,…,winT represents the input weight connecting the ith hidden layer and the input neuron, βi=βi1,βi2,…,βimT represents the output weight connecting the ith hidden layer node and the output neuron, and bi represents the deviation of the ith hidden layer node. If the single hidden layer feedforward neural network of the excitation function can approach N training samples xi,ti with zero error, then(2)∑j=1Loj−tj=0,(3)∑i=1Lβigwi⋅xj+bi=tj,j=1,2,…,N.Therefore, it can be expressed in the matrix form:(4)Hβ=T,(5)Hw1,w2,…,wL,b1,b2,…,bL,x1,x2,…,xN=gw1x1+b1⋯gwLx1+bL⋮⋮gw1xN+bL⋯gwLxN+bL,β=β1T⋮βLT,T=t1T⋮tNTN×m,where H represents the output matrix of the hidden layer, T represents the expected output, and β represents the output weight. In order to train the single hidden layer neural network, we want to get w^i, b^i, and β^i. The calculation formula is(6)Hw^i,b^iβ^i−T=minHwi,biβi−T.Then, the output layer weightβ can be obtained by solving the following linear equations by the least square method:(7)β=H+T,where H+ represents the Moore–Penrose generalized inverse of the hidden layer output matrix H.Let the initial training set beN0, and the data sample is trained by ELM algorithm in the batch learning mode. β0=K0−1H0TT0 can be obtained from formula (7), where K0=H0TH0. Assuming that there is another dataset N1, the problem solving becomes a minimization problem:(8)Emin=H0H1β−T0T1.When a new training data enters the system, assuming thatN1 samples enter the model, the following β1 can be obtained:(9)β1=K1−1H0H1T0T1=β0+K1−1H1TT1−H1β0.Formula (9) is extended to the general problem, that is, when k samples enter the model, the recursive formula of output weight β of online sequence limit learning machine algorithm can be obtained:(10)βk+1=βk+Kk+1−1Hk+1TTk+1−Hk+1βk.Formula (10) is a recursive formula derived in order to find βk+1 in an online sequence optimization problem.In order to make the online sequential limit learning machine model with multiple parallel nodes have higher prediction accuracy, this paper adopts the random gradient descent algorithm and weighted average method to dynamically adjust the error weight of the prediction output results of multiple different nodes in the cluster. Nodes with high accuracy of rainfall estimation are given higher weights. The final rainfall estimatey¯ can be obtained by weighted average of error weights through the output results of each node:(11)y¯j=∑i=1kσjiyji∑i=1kσji,where σji is the error j weight of the ith learning machine node, yji is the output value of the ith learning machine node, and j represents the jth batch in the estimation stage. Through the above estimation, based on the rainfall data in the Internet of Things, the limit learning machine method is used to estimate the rainfall in mountainous areas.


### 2.2. BIM Technology Site Safety Management

It is of an immense importance to strengthen the application of BIM technology for construction safety management of mountain engineering. BIM technology is based on the forward-looking overall planning and planning of the construction safety management of the whole mountain project. At present, the mountain engineering construction industry attaches great importance to BIM technology and regards it as a key force to promote the reform of the whole mountain engineering construction industry [6–8].BIM (Building Information Modeling) simulates the real information of buildings through digital information simulation. The information includes three-dimensional geometry of buildings, materials, weight, progress, price, and other information of building components. In the engineering design stage, BIM technology mainly realizes virtual design or intelligent construction through the 3D model, and with the help of building related graphics, documents and other information these design elements are highly logical. When the model changes, the related graphics or documents will be updated automatically. Currently, collision detection, energy consumption analysis, and cost prediction are fully used in the design stage. The implementation of project integrated delivery management is also the leading component of BIM application. BIM is used for the whole life cycle management of the project, so as to realize the integration of virtual design, CCB, maintenance, and management [9]. Through dynamic management, effective integration, and visual operation, the information of project progress, manpower, materials, equipment, cost, and site in the construction stage can be dynamically integrated. At the same time, all parties of the project can realize project negotiation, negotiation, construction quality control, safety management, cost control, and progress management through the access system, so as to realize collaborative work and information sharing.In the safety management of mountain construction quality, first obtain the image information of mountain construction safety status, then acquire the corresponding two-dimensional array of mountain construction safety, after this establish the mountain construction safety model, calculate the two-dimensional data length of mountain construction safety, and gain the dynamic changes of mountain construction safety quality, so as to realize the safety management of mountain construction site [10–13]. The specific steps are as follows.Primarily, the safety status image of mountain construction site represented byTCCD is determined by using the gray values of R, G, and B of pixels, which is expressed as follows:(12)TCCD=fR,G,B,where f represents the brightness coefficient of the safety status monitoring image.Moreover, obtain the corresponding two-dimensional array of construction site safety, establish the dynamic monitoring model of construction safety, and calculate the two-dimensional array of construction safety by using the following formula:(13)T=A′×TCCD,where A′ represents the dynamic parameter of safety on the construction site. Then, the expression of the construction safety dynamic monitoring model is(14)si∗=yi−y¯Sy×xij∗.In the third place, the two-dimensional data field of construction site safety is calculated to provide a basis for obtaining the dynamic change expression of construction safety, so the two-dimensional data length calculation formula is(15)m1=P1⊗X0Tt1si∗.In the end, determine the dynamic changes of construction safety and complete the dynamic monitoring of construction safety. The expression is(16)y=m1−b˜n×km1.To sum up, it can be elaborated that the establishment of construction safety dynamic monitoring model can effectively complete the construction safety dynamic monitoring, but according to the problem of inaccurate monitoring, corresponding optimization treatment is needed [14].In view of the sparse distribution of dynamic supervision data of construction site safety and the large error of construction site safety supervision when using traditional monitoring methods to monitor and manage construction safety, a construction site safety management method based on BIM is proposed.In the process of safety supervision on the construction site, BIM is now integrated with the safety status monitoring information on the construction site. On the basis of obtaining all the safety status monitoring information on the construction site, the multiquadric radial basis function method is used to interpolate the monitoring data on the construction site, and the calculation results are used to build the safety supervision model on the construction site [15]. The specific calculation steps are as follows:(1)
Carry out interpolation calculation on the safety data of the construction site to provide a basis for obtaining the interpolation three-variable function of the spatial data of the safety state monitoring of the construction site. On the basis of BIM and construction site safety monitoring information, use the Multiquadric radial basis function method to interpolate the construction site safety data length:(17)Fx,y=∑k=1nakhkx,y+∑k=1mbkqkx,y,wherex,y represents a point in the basis function of the construction site safety monitoring data field, qkx,y represents the polynomial basis of the construction site safety monitoring data field, which is less than m, hk represents the sparsity of the construction site safety monitoring data field, and ak and bk represent the coefficients of the construction site safety monitoring data field.(2)
Calculate the interpolation three-variable function of spatial data of construction site safety status monitoring, so as to provide the foundation for establishing the information model of construction site safety monitoring. The independent variables of construction safety are extended to obtain the interpolation three variable functions of spatial data of construction safety status monitoring, which is expressed by the following formula:(18)Fx,y,z=∑j=1najx−xj2+y−yj2+z−zj2+c21/2Fx,y,wherec represents any constant.(3)
Establish the information model of safety condition monitoring on the construction site, and update and refine it. By importing the values ofn points xi,yi,zi represented by fi into the above formula, the construction site safety status monitoring information model can be made, which is expressed by the following formula:(19)fi=∑j=1najxi−xj2+yi−yj2+zi−zj2+c2Fx,y,z.It is assumed thatQj represents any quadratic basis function and aj represents the coefficient of the monitoring model. After updating and enhancing the safety status monitoring information model of the construction site, the above model can be rewritten as(20)fi=∑j=1najQij.(4)
Obtain the physical quantity value of the safety monitoring information node on the construction site. Now, the model expression in the above formula is transformed into the matrix form and expressed by the following formula:(21)F=QAfi.In the formula,F represents the expression matrix of the data information monitoring model of the safety state of the construction site, Q represents the difference basis function of the model, and A represents the safety state parameters of the construction site. Then, the physical quantity value of the construction site quality monitoring information node represented by P is calculated by the following formula:(22)P=QPQ−1F.In the formula,QP represents the discrete points of the safety monitoring information data field on the construction site and Q−1 represents the discrete basis function.To sum up, it can be illustrated that, in the process of safety supervision on the construction site, the multiquadric radial basis function is tied down to interpolate the safety monitoring data on the construction site, establish the data information monitoring model for safety status monitoring on the construction site, and calculate the physical value of the safety monitoring information node on the construction site, so as to lay a groundwork for the realization of safety management on the construction site.The physical value of the safety monitoring information node on the construction site is combined with the distance mapping method to obtain the similarity between the safety monitoring information competition layer neuron and its input mode weight vector. The dimension of the input vector is reduced and mapped to the two-dimensional plane, so as to effectively complete the safety management of the construction site. The specific computation steps are as follows:(1)
Determine the neuron competition rules of safety monitoring data information on the construction site and obtain its input mode weight. SupposeP′ represents the input vector of safety status monitoring on the construction site, wj represents the initial value given to the weight vector, M represents the number of neurons in the output layer, and Euclidean distance is used as the discrimination function of competition; based on the physical quantity value of the construction site safety monitoring information node obtained by the above formula, the following formula is used to represent the competition rules of the construction site safety monitoring data information node:(23)P′−wg=minjp′−wj×pp′×wj×M.Using the above formula, the neuron represented byg can be determined as the competition winner. The weight of neuron j input mode in gG neighborhood is expressed by the subsequent formula:(24)wjt+1=wjt+ηthigtp′−wjtP′−wg,wherewjt+1 represents the modified weight of neuron j in g neighborhood, wjt represents the weight of neuron j in g neighborhood, ηt represents the iterative learning rate in step t, and higt represents the topology factor.(2)
Determine the distance between the input vector and the initial value of the weight vector to complete the similarity comparison.M represents the number of neurons, which are fixed on the two-dimensional output plane grid, xj represents the coordinates, and distance between P and W is calculated by the following formula: represents the number of neurons, which are fixed on the two-dimensional output plane grid, X represents the coordinates, and the distance between p′ and wj is calculated by the following formula:(25)dj=M×p′−wjxj.Calculate the similarity between the trained output layer neuronsj and p′ by using the following formula:(26)Rjp′=fdjNgj∑i=1MfdjNgj,whereg represents the BMU of the construction site safety monitoring vector p′ and Ngj represents the exponential function of the construction site safety monitoring. Assuming that the neuron j is within the neighborhood g of j, the value of Ngj is 1, otherwise it is 0. fdj is a function of distance dj.(3)
After obtaining the similarity between all output layer neurons of safety monitoring on the construction site and input vectorp′, the dimension of input vector p′ is reduced and mapped to two-dimensional plane by using the following formula to complete the safety supervision on the construction site:(27)xp=∑j=1MRjpxj.To sum up, it can be stated that, by fusing the distance mapping method to obtain the phase velocity of the construction site safety monitoring information, the competitive layer neuron, and its input mode weight vector, mapping the dimension reduction of the input vector to the two-dimensional flat key, and the construction site safety supervision can be effectively completed.


## 2.1. Mountain Rainfall Estimation Based on Internet of Things

With the sustained improvement and rapid growth of meteorological service informatization level, it is very essential to apply real-time meteorological data processing to meteorological information business. Furthermore, data mining technology is the most widely used and efficient method of meteorological data processing. With the support of Internet of Things technology, it is a motif of great significance to analyze the meteorological characteristics and laws of meteorological data hiding through data mining, provide meteorological researchers with knowledge decision-making based on meteorological data, and predict future meteorological changes [4].Single hidden layer feedforward neural network (SLFN) is widely used in many fields because of its ability to approximate nonlinear mapping, but it needs to set a large number of parameters artificially in the process of application, so the network will fall into local optimization due to improper selection of parameters. Huang Guangbin et al. proposed a learning algorithm of single hidden layer feedforward neural network, called limit learning machine (ELM), which does not need to adjust all the network specifications. The input weight and hidden layer deviation are given randomly at the beginning of training and fixed in the training process, while the output connection weight can be obtained by solving the least square solution of linear equations. Therefore, in order to enhance the accuracy of mountain rainfall estimation, based on the data provided by the Internet of things, the limit learning machine method is used to estimate mountain rainfall to ensure construction safety [5]. The specific estimation process is as follows.The mountain rainfall training sample set isN, where xi,ti meets xi=xi1,xi2,…,xinT∈Rn and ti=ti1,xti2,…,timT∈Rm. For a single hidden layer feedforward neural network with L hidden layer nodes, the excitation function can be expressed as(1)∑i=1Lβigiwi⋅xj+bi=oj,j=1,2,…,N,where wi=wi1,wi2,…,winT represents the input weight connecting the ith hidden layer and the input neuron, βi=βi1,βi2,…,βimT represents the output weight connecting the ith hidden layer node and the output neuron, and bi represents the deviation of the ith hidden layer node. If the single hidden layer feedforward neural network of the excitation function can approach N training samples xi,ti with zero error, then(2)∑j=1Loj−tj=0,(3)∑i=1Lβigwi⋅xj+bi=tj,j=1,2,…,N.Therefore, it can be expressed in the matrix form:(4)Hβ=T,(5)Hw1,w2,…,wL,b1,b2,…,bL,x1,x2,…,xN=gw1x1+b1⋯gwLx1+bL⋮⋮gw1xN+bL⋯gwLxN+bL,β=β1T⋮βLT,T=t1T⋮tNTN×m,where H represents the output matrix of the hidden layer, T represents the expected output, and β represents the output weight. In order to train the single hidden layer neural network, we want to get w^i, b^i, and β^i. The calculation formula is(6)Hw^i,b^iβ^i−T=minHwi,biβi−T.Then, the output layer weightβ can be obtained by solving the following linear equations by the least square method:(7)β=H+T,where H+ represents the Moore–Penrose generalized inverse of the hidden layer output matrix H.Let the initial training set beN0, and the data sample is trained by ELM algorithm in the batch learning mode. β0=K0−1H0TT0 can be obtained from formula (7), where K0=H0TH0. Assuming that there is another dataset N1, the problem solving becomes a minimization problem:(8)Emin=H0H1β−T0T1.When a new training data enters the system, assuming thatN1 samples enter the model, the following β1 can be obtained:(9)β1=K1−1H0H1T0T1=β0+K1−1H1TT1−H1β0.Formula (9) is extended to the general problem, that is, when k samples enter the model, the recursive formula of output weight β of online sequence limit learning machine algorithm can be obtained:(10)βk+1=βk+Kk+1−1Hk+1TTk+1−Hk+1βk.Formula (10) is a recursive formula derived in order to find βk+1 in an online sequence optimization problem.In order to make the online sequential limit learning machine model with multiple parallel nodes have higher prediction accuracy, this paper adopts the random gradient descent algorithm and weighted average method to dynamically adjust the error weight of the prediction output results of multiple different nodes in the cluster. Nodes with high accuracy of rainfall estimation are given higher weights. The final rainfall estimatey¯ can be obtained by weighted average of error weights through the output results of each node:(11)y¯j=∑i=1kσjiyji∑i=1kσji,where σji is the error j weight of the ith learning machine node, yji is the output value of the ith learning machine node, and j represents the jth batch in the estimation stage. Through the above estimation, based on the rainfall data in the Internet of Things, the limit learning machine method is used to estimate the rainfall in mountainous areas.


## 2.2. BIM Technology Site Safety Management

It is of an immense importance to strengthen the application of BIM technology for construction safety management of mountain engineering. BIM technology is based on the forward-looking overall planning and planning of the construction safety management of the whole mountain project. At present, the mountain engineering construction industry attaches great importance to BIM technology and regards it as a key force to promote the reform of the whole mountain engineering construction industry [6–8].BIM (Building Information Modeling) simulates the real information of buildings through digital information simulation. The information includes three-dimensional geometry of buildings, materials, weight, progress, price, and other information of building components. In the engineering design stage, BIM technology mainly realizes virtual design or intelligent construction through the 3D model, and with the help of building related graphics, documents and other information these design elements are highly logical. When the model changes, the related graphics or documents will be updated automatically. Currently, collision detection, energy consumption analysis, and cost prediction are fully used in the design stage. The implementation of project integrated delivery management is also the leading component of BIM application. BIM is used for the whole life cycle management of the project, so as to realize the integration of virtual design, CCB, maintenance, and management [9]. Through dynamic management, effective integration, and visual operation, the information of project progress, manpower, materials, equipment, cost, and site in the construction stage can be dynamically integrated. At the same time, all parties of the project can realize project negotiation, negotiation, construction quality control, safety management, cost control, and progress management through the access system, so as to realize collaborative work and information sharing.In the safety management of mountain construction quality, first obtain the image information of mountain construction safety status, then acquire the corresponding two-dimensional array of mountain construction safety, after this establish the mountain construction safety model, calculate the two-dimensional data length of mountain construction safety, and gain the dynamic changes of mountain construction safety quality, so as to realize the safety management of mountain construction site [10–13]. The specific steps are as follows.Primarily, the safety status image of mountain construction site represented byTCCD is determined by using the gray values of R, G, and B of pixels, which is expressed as follows:(12)TCCD=fR,G,B,where f represents the brightness coefficient of the safety status monitoring image.Moreover, obtain the corresponding two-dimensional array of construction site safety, establish the dynamic monitoring model of construction safety, and calculate the two-dimensional array of construction safety by using the following formula:(13)T=A′×TCCD,where A′ represents the dynamic parameter of safety on the construction site. Then, the expression of the construction safety dynamic monitoring model is(14)si∗=yi−y¯Sy×xij∗.In the third place, the two-dimensional data field of construction site safety is calculated to provide a basis for obtaining the dynamic change expression of construction safety, so the two-dimensional data length calculation formula is(15)m1=P1⊗X0Tt1si∗.In the end, determine the dynamic changes of construction safety and complete the dynamic monitoring of construction safety. The expression is(16)y=m1−b˜n×km1.To sum up, it can be elaborated that the establishment of construction safety dynamic monitoring model can effectively complete the construction safety dynamic monitoring, but according to the problem of inaccurate monitoring, corresponding optimization treatment is needed [14].In view of the sparse distribution of dynamic supervision data of construction site safety and the large error of construction site safety supervision when using traditional monitoring methods to monitor and manage construction safety, a construction site safety management method based on BIM is proposed.In the process of safety supervision on the construction site, BIM is now integrated with the safety status monitoring information on the construction site. On the basis of obtaining all the safety status monitoring information on the construction site, the multiquadric radial basis function method is used to interpolate the monitoring data on the construction site, and the calculation results are used to build the safety supervision model on the construction site [15]. The specific calculation steps are as follows:(1)
Carry out interpolation calculation on the safety data of the construction site to provide a basis for obtaining the interpolation three-variable function of the spatial data of the safety state monitoring of the construction site. On the basis of BIM and construction site safety monitoring information, use the Multiquadric radial basis function method to interpolate the construction site safety data length:(17)Fx,y=∑k=1nakhkx,y+∑k=1mbkqkx,y,wherex,y represents a point in the basis function of the construction site safety monitoring data field, qkx,y represents the polynomial basis of the construction site safety monitoring data field, which is less than m, hk represents the sparsity of the construction site safety monitoring data field, and ak and bk represent the coefficients of the construction site safety monitoring data field.(2)
Calculate the interpolation three-variable function of spatial data of construction site safety status monitoring, so as to provide the foundation for establishing the information model of construction site safety monitoring. The independent variables of construction safety are extended to obtain the interpolation three variable functions of spatial data of construction safety status monitoring, which is expressed by the following formula:(18)Fx,y,z=∑j=1najx−xj2+y−yj2+z−zj2+c21/2Fx,y,wherec represents any constant.(3)
Establish the information model of safety condition monitoring on the construction site, and update and refine it. By importing the values ofn points xi,yi,zi represented by fi into the above formula, the construction site safety status monitoring information model can be made, which is expressed by the following formula:(19)fi=∑j=1najxi−xj2+yi−yj2+zi−zj2+c2Fx,y,z.It is assumed thatQj represents any quadratic basis function and aj represents the coefficient of the monitoring model. After updating and enhancing the safety status monitoring information model of the construction site, the above model can be rewritten as(20)fi=∑j=1najQij.(4)
Obtain the physical quantity value of the safety monitoring information node on the construction site. Now, the model expression in the above formula is transformed into the matrix form and expressed by the following formula:(21)F=QAfi.In the formula,F represents the expression matrix of the data information monitoring model of the safety state of the construction site, Q represents the difference basis function of the model, and A represents the safety state parameters of the construction site. Then, the physical quantity value of the construction site quality monitoring information node represented by P is calculated by the following formula:(22)P=QPQ−1F.In the formula,QP represents the discrete points of the safety monitoring information data field on the construction site and Q−1 represents the discrete basis function.To sum up, it can be illustrated that, in the process of safety supervision on the construction site, the multiquadric radial basis function is tied down to interpolate the safety monitoring data on the construction site, establish the data information monitoring model for safety status monitoring on the construction site, and calculate the physical value of the safety monitoring information node on the construction site, so as to lay a groundwork for the realization of safety management on the construction site.The physical value of the safety monitoring information node on the construction site is combined with the distance mapping method to obtain the similarity between the safety monitoring information competition layer neuron and its input mode weight vector. The dimension of the input vector is reduced and mapped to the two-dimensional plane, so as to effectively complete the safety management of the construction site. The specific computation steps are as follows:(1)
Determine the neuron competition rules of safety monitoring data information on the construction site and obtain its input mode weight. SupposeP′ represents the input vector of safety status monitoring on the construction site, wj represents the initial value given to the weight vector, M represents the number of neurons in the output layer, and Euclidean distance is used as the discrimination function of competition; based on the physical quantity value of the construction site safety monitoring information node obtained by the above formula, the following formula is used to represent the competition rules of the construction site safety monitoring data information node:(23)P′−wg=minjp′−wj×pp′×wj×M.Using the above formula, the neuron represented byg can be determined as the competition winner. The weight of neuron j input mode in gG neighborhood is expressed by the subsequent formula:(24)wjt+1=wjt+ηthigtp′−wjtP′−wg,wherewjt+1 represents the modified weight of neuron j in g neighborhood, wjt represents the weight of neuron j in g neighborhood, ηt represents the iterative learning rate in step t, and higt represents the topology factor.(2)
Determine the distance between the input vector and the initial value of the weight vector to complete the similarity comparison.M represents the number of neurons, which are fixed on the two-dimensional output plane grid, xj represents the coordinates, and distance between P and W is calculated by the following formula: represents the number of neurons, which are fixed on the two-dimensional output plane grid, X represents the coordinates, and the distance between p′ and wj is calculated by the following formula:(25)dj=M×p′−wjxj.Calculate the similarity between the trained output layer neuronsj and p′ by using the following formula:(26)Rjp′=fdjNgj∑i=1MfdjNgj,whereg represents the BMU of the construction site safety monitoring vector p′ and Ngj represents the exponential function of the construction site safety monitoring. Assuming that the neuron j is within the neighborhood g of j, the value of Ngj is 1, otherwise it is 0. fdj is a function of distance dj.(3)
After obtaining the similarity between all output layer neurons of safety monitoring on the construction site and input vectorp′, the dimension of input vector p′ is reduced and mapped to two-dimensional plane by using the following formula to complete the safety supervision on the construction site:(27)xp=∑j=1MRjpxj.To sum up, it can be stated that, by fusing the distance mapping method to obtain the phase velocity of the construction site safety monitoring information, the competitive layer neuron, and its input mode weight vector, mapping the dimension reduction of the input vector to the two-dimensional flat key, and the construction site safety supervision can be effectively completed.


## 3. Experimental Verification

In order to verify the practical application performance of the proposed mountain rainfall estimation based on the Internet of Things and the field safety management method of BIM technology, comparative verification experiments are carried out.


### 3.1. Experimental Environment and Scheme

To highlight the experimental effect, a mountain construction site in the province is selected as the research area, the rainfall in this area is estimated, and the safety of the construction site is managed. The construction site drawing is shown in Figure1.Figure 1
Construction site.According to the above construction site area, verify the rainfall estimation accuracy of this method and the calculation accuracy of safety factor on the construction site, and compare the calculation results of this method with the actual results to fully verify the safety management performance of this method.


### 3.2. Rainfall Estimation Accuracy

Rainfall results are very important for the safety management of mountain construction site. In the process of safety management, we should always pay attention to the changes of rainfall. Therefore, in the process of verifying the performance of the method, the impact of rainfall should be considered. The comparison results between the rainfall estimation results of this method and the actual rainfall results are shown in Figure2.Figure 2
Rainfall estimation accuracy results.From the comparison results of rainfall estimation accuracy shown in Figure2, it can be seen that, within the period of one month construction, the rainfall estimation results of this method are basically the same as the actual rainfall results, and the maximum rainfall estimation error is no more than 2 mm. Therefore, it shows that this paper can accurately estimate the rainfall on the construction site through the Internet of things data, so as to provide a basis for improving the safety management of the construction site.


### 3.3. Safety Factor of Construction Site

The safety factor of the construction site is the basis for judging the safety situation of the construction site. The safety factor of the construction site is calculated according to the rainfall estimation results, and the safety factor calculation results of this method are compared with the actual safety factor results to verify the performance of this method. The safety factor results of the construction site are shown in Figure3.Figure 3
Safety factor of construction site.From the comparison results of safety factors on the construction site shown in Figure3, it can be seen that this method can accurately calculate the safety factor on the construction site according to the rainfall estimation results, and the calculation results of the safety factor in this method are consistent with the actual safety factor results. Hence, it shows that this method can effectively manage the safety of the construction site.


## 3.1. Experimental Environment and Scheme

To highlight the experimental effect, a mountain construction site in the province is selected as the research area, the rainfall in this area is estimated, and the safety of the construction site is managed. The construction site drawing is shown in Figure1.Figure 1
Construction site.According to the above construction site area, verify the rainfall estimation accuracy of this method and the calculation accuracy of safety factor on the construction site, and compare the calculation results of this method with the actual results to fully verify the safety management performance of this method.


## 3.2. Rainfall Estimation Accuracy

Rainfall results are very important for the safety management of mountain construction site. In the process of safety management, we should always pay attention to the changes of rainfall. Therefore, in the process of verifying the performance of the method, the impact of rainfall should be considered. The comparison results between the rainfall estimation results of this method and the actual rainfall results are shown in Figure2.Figure 2
Rainfall estimation accuracy results.From the comparison results of rainfall estimation accuracy shown in Figure2, it can be seen that, within the period of one month construction, the rainfall estimation results of this method are basically the same as the actual rainfall results, and the maximum rainfall estimation error is no more than 2 mm. Therefore, it shows that this paper can accurately estimate the rainfall on the construction site through the Internet of things data, so as to provide a basis for improving the safety management of the construction site.


## 3.3. Safety Factor of Construction Site

The safety factor of the construction site is the basis for judging the safety situation of the construction site. The safety factor of the construction site is calculated according to the rainfall estimation results, and the safety factor calculation results of this method are compared with the actual safety factor results to verify the performance of this method. The safety factor results of the construction site are shown in Figure3.Figure 3
Safety factor of construction site.From the comparison results of safety factors on the construction site shown in Figure3, it can be seen that this method can accurately calculate the safety factor on the construction site according to the rainfall estimation results, and the calculation results of the safety factor in this method are consistent with the actual safety factor results. Hence, it shows that this method can effectively manage the safety of the construction site.


## 4. Conclusion

In order to improve the safety of mountain construction, it is mandatory to strengthen the safety management of construction site. Thus, a mountain rainfall estimation and BIM technology field safety management method based on Internet of Things is proposed, and the performance of the method is verified from both theory and experiment. This method has high accuracy of rainfall estimation and safety management system in rainfall estimation and safety management. The calculation results of the two indexes are basically consistent with the actual results. Accordingly, it shows that the proposed method based on Internet of Things and BIM technology can better meet the needs of rainfall estimation and safety management.


### 4.1. Future Scope

This work can be extended further at a very major level. This method based on Internet of Things can actually meet the demands of different construction sites. It can also be applied to the plain areas other than the hilly areas.


## 4.1. Future Scope

This work can be extended further at a very major level. This method based on Internet of Things can actually meet the demands of different construction sites. It can also be applied to the plain areas other than the hilly areas.



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*Source: 1017200-2021-10-07.xml*