CN111582632B - Multi-factor safety stage prediction method for whole process of underground large space construction - Google Patents
Multi-factor safety stage prediction method for whole process of underground large space construction Download PDFInfo
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Abstract
The invention discloses a multi-factor safety prediction method and a system for the whole process of underground large-space construction, wherein the method comprises the following steps: constructing and training a safety prediction neural network model before construction so as to form a nonlinear mapping relation from an input layer to an output layer; constructing and training a safety prediction neural network model in construction so as to form a nonlinear mapping relation from an input layer to an output layer; and connecting the pre-construction safety prediction neural network model and the in-construction safety prediction neural network model in series to form a construction prediction series model, and performing staged safety prediction in the whole construction process by using the construction prediction series model. The construction safety prediction model is established based on the neural network with the characteristics of self-adaptability, nonlinearity and strong fault tolerance, the safety prediction can be carried out on the underground large-space construction before the construction without depending on the internal working mechanism of a rock-soil system, the real-time prediction is carried out in the construction, and the series model is formed for carrying out the whole construction process prediction.
Description
Technical Field
The invention relates to the technical field of civil engineering construction, in particular to a multi-factor safety prediction method and system for the whole process of underground large-space construction.
Background
Underground engineering practice shows that geological environment, hydrological environment, surrounding building environment and construction method in the construction process all influence deformation, stress, strain, settlement, displacement and the like in construction, and in most cases, engineering technicians can only master the changes of deformation, stress, strain, settlement, displacement and the like by collecting monitoring data in construction, so as to improve the construction method, and cannot predict the changes roughly before construction, which often causes various disasters and construction safety accidents due to untimely improvement.
In addition, a theoretical calculation method is mainly adopted to predict multiple safety factors such as deformation, stress, strain, settlement, displacement and the like in construction at present. However, due to lack of cognition on the internal working mechanism of the geotechnical system, great difficulty is inevitably existed in establishing a corresponding theoretical calculation expression, and the prediction calculation of the safety interference factor related to the geotechnical engineering system shows very complicated high-order nonlinear characteristics, while the nonlinear calculation has certain difficulty.
The neural network is particularly suitable for processing various nonlinear problems due to the characteristics of adaptability, nonlinearity, strong fault tolerance and the like. It can extract the causal relationships implied in the samples through the learning of a large number of samples. Therefore, the neural network provides a research idea completely different from mathematical modeling for the field of underground engineering, avoids a complex constitutive model, and becomes an effective way for solving the problem of underground engineering. Meanwhile, the neural network has a lot of applications in other fields, for example, an evaluation method of the rainstorm disaster risk of the foundation side slope of the section power transmission line tower is used for establishing a mapping relation between the control factors and the rainstorm landslide accident rate by using disaster statistics and artificial rainfall side slope erosion test results and combining with an improved hierarchical analysis calculation program to obtain the evaluation result of the rainstorm disaster of the side slope of each section of the line. The method comprises two stages before construction and in construction, the whole construction process is considered through establishing a series model of the two stages, safety prediction is carried out before construction and real-time prediction is carried out in construction, safety judgment is carried out through relevant specifications or a grading system, and the safety of the whole process construction is guaranteed.
Disclosure of Invention
At least one of the objectives of the present invention is to overcome the above problems in the prior art, and to provide a method and a system for multi-factor safety prediction in large underground space construction, which are capable of establishing a construction safety prediction model based on a neural network with high adaptivity, nonlinearity, and fault tolerance, performing safety prediction on large underground space construction before construction without depending on the internal working mechanism of a geotechnical system, and performing real-time prediction during construction.
In order to achieve the above object, the present invention adopts the following aspects.
A multi-factor safety prediction method for the whole process of underground large space construction comprises the following steps:
step 1, constructing a safety prediction neural network model before construction, and training the safety prediction neural network model before construction by using a first training sample so as to enable the performance of the safety prediction neural network model before construction to tend to be stable and form a nonlinear mapping relation from an input layer to an output layer;
wherein, the input parameters of the input layer of the safety prediction neural network before construction are as follows: engineering geological conditions, hydrological conditions, surrounding building environment, construction method, management level and construction level; the output parameters of the prediction neural network before construction are the predicted values of corresponding stress, strain, displacement and settlement;
step 2, constructing a safety prediction neural network model in construction, and training the safety prediction neural network model in construction by using a second training sample so as to enable the performance of the safety prediction neural network in construction to tend to be stable before construction and form a nonlinear mapping relation from an input layer to an output layer of the safety prediction neural network in construction;
wherein, the input parameters of the input layer of the safety prediction neural network model in the construction are as follows: input values of stress, strain, displacement and settlement of a certain time node in construction; the output parameters of the safety prediction neural network model in construction are predicted values of stress, strain, displacement and settlement of the next time node in construction;
and 3, connecting the pre-construction safety prediction neural network model and the in-construction safety prediction neural network model in series by taking the output parameters of the pre-construction safety prediction neural network model as the input parameters of the in-construction safety prediction neural network model to form a construction prediction series model, and performing staged safety prediction in the whole construction process by using the construction prediction series model.
Preferably, the method further comprises: and inputting the stress, strain, displacement and settlement monitored in real time in the construction into the in-construction safety prediction neural network model so as to predict the stress, strain, displacement and settlement of the next time node in real time in the construction through the in-construction safety prediction neural network model.
Preferably, the current output value of the node weight of each layer of the construction prediction series model is calculated by the following formula:
wherein,
input sum of the ith element of the kth layer;
Is the output of the ith element of the kth layer;
The connection weight from the ith element of the kth-1 layer to the jth element of the kth layer is set; f is an excitation function;
Is the actual output of the jth element of the mth layer associated with the weight vector W and the input vector X, where the mth layer is the output layer.
Preferably, the weight of each layer of node of the construction prediction series model is adjusted through the following formula:
when k = m, the number of the magnetic poles is as follows,
when k is<When m is greater than the total number of the carbon atoms,
wherein,
is the input sum of the ith element of the kth layer;
Is the output of the ith element of the kth layer;
The connection weight value from the ith element of the kth-1 layer to the jth element of the kth layer is obtained; f is an excitation function, which may be S c (x)；
Is the actual output of the jth element of the mth layer relative to the weight vector W and the input vector X; y is i Is the desired output of the jth element of the mth layer relative to the weight vector W and the input vector XWherein, the mth layer is the output layer.
Preferably, when the pre-construction safety prediction neural network model and the in-construction safety prediction neural network model are constructed, the maximum number of iterations is set to 5000 and the learning rate η is set to 0.5.
Preferably, when the error rate of the pre-construction safety prediction neural network model and the error rate of the in-construction safety prediction neural network model are smaller than a preset value, the performance of the pre-construction safety prediction neural network tends to be stable.
In a further embodiment of the invention, the invention also provides a multi-factor safety prediction system for the whole process of underground large space construction, which comprises at least one processor and a memory which is in communication connection with the at least one processor; the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the above-described method.
In summary, due to the adoption of the technical scheme, the invention at least has the following beneficial effects:
establishing a safety prediction neural network model before construction and a safety prediction neural network model during construction based on a neural network with strong adaptivity, nonlinearity and fault tolerance, taking engineering geological conditions, hydrological conditions, surrounding building environment, a construction method, a management level and a construction level as input variables of the safety prediction neural network model before construction, taking values of stress, strain, displacement and settlement corresponding to the input variables as output vectors of the safety prediction neural network model before construction, performing safety prediction on underground large space construction before construction, connecting the safety prediction neural network models during construction in series to form a construction prediction series model, and performing staged safety prediction on the whole construction process by using the construction prediction series model; the prediction method comprehensively considers two stages before construction and in construction, considers the whole construction process by establishing a series model of the two stages, performs safety prediction before construction and performs real-time prediction in construction, and performs safety judgment through construction specifications and/or a grading system so as to ensure the safety of the whole construction process.
And inputting the stress, strain, displacement and settlement monitored in real time in the construction into the in-construction safety prediction neural network model so as to predict the stress, strain, displacement and settlement of the next time node in real time in the construction through the in-construction safety prediction neural network model.
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FIG. 1 is a flow diagram of a pre/intermediate construction safety prediction neural network model training process, according to an exemplary embodiment of the present invention.
FIG. 2 is a pre-construction safety predictive neural network model topology diagram in accordance with an exemplary embodiment of the present invention.
FIG. 3 is a diagram of an in-construction safe predictive neural network model topology, according to an exemplary embodiment of the invention.
FIG. 4 is a block diagram of construction prediction tandem model construction staging prediction according to an exemplary embodiment of the present invention.
FIG. 5 is a schematic structural diagram of a multi-factor safety prediction system for the whole process of underground large space construction according to an exemplary embodiment of the invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and embodiments, so that the objects, technical solutions and advantages of the present invention will be more clearly understood. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Example 1
The embodiment provides a multi-factor safety prediction method for the whole process of underground large-space construction, which comprises the following steps:
step 1, constructing a safety prediction neural network model before construction, and training the safety prediction neural network model before construction by using a first training sample so as to enable the performance of the safety prediction neural network model before construction to tend to be stable and form a nonlinear mapping relation from an input layer to an output layer; and normalizing the data of the stress, strain, displacement and settlement corresponding to the data under different engineering geological conditions, hydrological conditions, surrounding building environments, construction methods, management levels and construction levels to obtain a first training sample.
And (4) determining input parameters. The method comprises the steps of firstly considering all projects which influence the safety and the environmental safety of the engineering during construction, such as geological conditions, hydrological conditions, surrounding building environments, construction methods and the like, and then dividing the projects according to the reference standard, the standard and the expert demonstration and determining the numerical value of the input parameter through normalization.
Determining an output parameter, based on the measured data, normalizing the output parameter; and establishing a database, namely establishing the database by using the engineering conditions, namely the input parameters, and establishing the database by using the measured data, namely the output parameters.
And (4) operation of the neural network. The learning algorithm is a multilayer neural network algorithm guided by teacher signals, is a supervised learning process, and is used for learning according to given (input and output) sample pairs and reflecting the learning effect by adjusting the network connection weight.
Neural networks have two states. In the learning stage, the inputs of the learning sample pairs are added to the input end of the network, and the outputs are generated in the input and excitation function (Sigmoid function) mode in each layer of neurons along the forward direction (i.e. the input layer-the output layer). Then, the difference between the actual output value and the expected output value of the output layer neuron is reversely propagated to each layer of neuron, and each connection weight value is correspondingly adjusted according to the size and the sign of the error. This process continues until the neural network weight connection can produce a given output result with some accuracy given the input sample conditions, i.e., the learning phase is deemed to be complete. In the working stage, when the sample to be tested is input into the input end of the learnt neural network, the neural network generates the required solution at the output end in an interpolation or extension mode according to the principle of similar output.
Specifically, as shown in fig. 1, the implementation steps are reading in a learning sample, normalizing data (normalization in this patent), initializing a weight of a neural network, calculating an output value of a hidden node, calculating an output value of an output node, calculating an error of an output layer, calculating an error of a hidden node, adjusting a weight, ending if the error is within an allowable error range, and performing training by normalizing again if the error is not within the allowable error range.
The process of constructing the pre-construction safety prediction neural network model shown in fig. 2 includes: and (4) determining initial parameters. Weighted values w of the network ij And giving a small non-zero random real number initial value, and setting a learning rate eta and an inertia coefficient alpha. The random initial weights are different, and the final weights are also different. The number of implicit layers and the number of implicit elements of the network depend on different specific issues. The number of hidden layers can be known from the proven mapping theorem, and any mapping function can be approximated to complete a given mapping task as long as a three-layer neural network of the hidden layers exists.
The number of the hidden layer units is more than the calculation formula of the number of the hidden layer units, and the formula is as follows:
n 1 ＝log 2 n (1)
in the formula: n is 1 -the number of the hidden layer units,
n-number of input layer units.
And the number of samples, namely, the logarithm of the samples has a close relation with the number of the hidden layers and the number of the units of the hidden layers during learning. The more the hidden layers are, the more accurate the learned connection weights are, but the worse the network generalization capability is. According to research, the relation between the adjustable total weight W of the connection and the necessary logarithm N of the training sample of the network can be approximately expressed to enable the multi-layer network to have generalization capability
τ -coefficient, taken to be about 10.
At the time of learning, a condition for termination of learning is given. Two methods are generally used for network learning termination: first, a given error minimum value, which is terminated when the actual output error is less than the given error; the second is to specify the number of iterations (e.g., 5000). The former is adopted in the present system. In this embodiment, the maximum number of iterations is set to 5000, and the learning rate η is set to 0.5.
And (4) training a neural network model. The first of the P (input, output) sample pairs is input.
The actual output value is calculated. According to the formula
And calculating the actual output value of each layer element of the network. Adjusting the weight of each connection according to the formula
Adjusting each connection weight, wherein:
when k = m, the number of the magnetic poles is as follows,
when k is<When the sum is m, the sum is,
and after the first sample pair is finished, inputting a subsequent sample pair, and repeating the steps till the end. Recycle P sample pairs until w ij It tends to be stable until it is unchanged. When r is less than or equal to epsilon during network training, the learning process is finished.
The meaning of the symbols in the above calculation formula is as follows:
f: the excitation function, which may be S c (x)；
y i : the desired output of the jth element of the mth layer (i.e., the output layer) in relation to the weight vector W and the input vector X.
Step 2, constructing a safety prediction neural network model in construction as shown in fig. 3, and training the safety prediction neural network model in construction by using a second training sample so as to enable the performance of the safety prediction neural network in construction to tend to be stable before construction and form a nonlinear mapping relation from an input layer to an output layer of the safety prediction neural network in construction; and the stress, strain, displacement and settlement data of a certain time node in construction and the stress, strain, displacement and settlement data of the next time node are normalized to form a second training sample.
Similar to step 1, the determination of the parameters is entered. Prediction in construction: the input parameters of the input layer of the safety prediction neural network model in the construction are as follows: input values of stress, strain, displacement and settlement of a certain time node in construction; the output parameters of the safety prediction neural network model in construction are predicted values of stress, strain, displacement and settlement of the next time node in construction; and determining initial parameters. Weighted values w of the network ij And giving a small non-zero random real number initial value, and setting a learning rate eta and an inertia coefficient alpha. The random initial weights are different, and the final weights are also different. The number of implicit layers and the number of implicit elements of the network depend on different specific issues. The specific determination method is according to the above description and formulas (1), (2). Inputting the first sample pair of P (stress, strain, displacement and settlement data of a certain time node in construction, and stress, strain, displacement and settlement data of the next time node) sample pairs, calculating an actual output value, and calculating the actual output value according to the formula (3). According to the above-mentioned formulas (4) - (6), after the first sample pair of each connection weight value is adjusted,inputting subsequent sample pairs, and repeating the steps till the end. Recycle P sample pairs until w ij It tends to be stable until it is unchanged. When r is less than or equal to epsilon during network training, the learning process is finished. After the model training is finished, the working stage can be carried out, the safety classification can be carried out, and the corresponding emergency and coping method can be determined according to the classification.
And 3, connecting the pre-construction safety prediction neural network model and the in-construction safety prediction neural network model in series by taking the output parameters of the pre-construction safety prediction neural network model as the input parameters of the in-construction safety prediction neural network model to form a construction prediction series model, and performing staged safety prediction in the whole construction process by using the construction prediction series model.
As shown in fig. 2, a prediction flow of the predictive series model is constructed. In actual application, two safety prediction neural network models before and during construction are used in series. The series model firstly inputs engineering geology, hydrogeology, surrounding environment conditions, a construction method, a management level and a construction level before construction, predicts stress, strain, displacement and settlement before a certain construction process in construction through a prediction model before construction, predicts the stress, strain, displacement and settlement of monitoring data at the next construction time point in construction through a prediction model in construction, and simultaneously predicts the stress, strain, displacement and settlement of the monitoring data at the next construction time point in construction through the prediction model in construction according to the stress, strain, displacement and settlement of the monitoring data in construction, so as to predict the stress, strain, displacement and settlement of the monitoring data at the next construction time point in construction in real time.
Furthermore, when the method is actually used, data of stress, strain, displacement and settlement of a current time node monitored in real time in the construction process are input into the in-construction safety prediction neural network model, so that the stress, strain, displacement and settlement of the next time node can be predicted in real time in construction through the in-construction safety prediction neural network model.
Example 2
Fig. 5 illustrates a multi-factor safety prediction system for the whole process of underground large space construction according to an exemplary embodiment of the present invention, namely, an electronic device 310 (e.g., a computer server with program execution function) including at least one processor 311, a power supply 314, and a memory 312 and an input/output interface 313 communicatively connected to the at least one processor 311; the memory 312 stores instructions executable by the at least one processor 311, the instructions being executable by the at least one processor 311 to enable the at least one processor 311 to perform a method as disclosed in any one of the embodiments; the input/output interface 313 may include a display, a keyboard, a mouse, and a USB interface for inputting/outputting data; the power supply 314 is used to provide power to the electronic device 310.
Those skilled in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: various media that can store program codes, such as a removable Memory device, a Read Only Memory (ROM), a magnetic disk, or an optical disk.
When the integrated unit of the present invention is implemented in the form of a software functional unit and sold or used as a separate product, it may also be stored in a computer-readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present invention or portions thereof contributing to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, a ROM, a magnetic or optical disk, or other various media that can store program code.
The foregoing is merely a detailed description of specific embodiments of the invention and is not intended to limit the invention. Various alterations, modifications and improvements will occur to those skilled in the art without departing from the spirit and scope of the invention.
Claims (7)
1. A multi-factor safety prediction method for the whole process of underground large space construction is characterized by comprising the following steps:
step 1, constructing a safety prediction neural network model before construction, and training the safety prediction neural network model before construction by using a first training sample so as to enable the performance of the safety prediction neural network model before construction to tend to be stable and form a nonlinear mapping relation from an input layer to an output layer;
wherein, the input parameters of the input layer of the safety prediction neural network before construction are as follows: engineering geological conditions, hydrological conditions, surrounding building environment, construction method, management level and construction level; the output parameters of the prediction neural network before construction are the predicted values of corresponding stress, strain, displacement and settlement; normalizing the data of stress, strain, displacement and settlement under different engineering geological conditions, hydrological conditions, surrounding building environments, construction methods, management levels and construction levels and corresponding conditions of the engineering geological conditions, the hydrological conditions and the surrounding building environments into a first training sample; the values of the input parameters are determined through normalization by reference to standards, standards and expert argumentation; normalizing the data as output parameters based on the measured stress, strain, displacement and settlement data;
step 2, constructing a safety prediction neural network model in construction, and training the safety prediction neural network model in construction by using a second training sample so as to enable the performance of the safety prediction neural network in construction before construction to tend to be stable and form a nonlinear mapping relation from an input layer to an output layer of the safety prediction neural network in construction;
wherein, the input parameters of the input layer of the safety prediction neural network model in construction are as follows: input values of stress, strain, displacement and settlement of a certain time node in construction; the output parameters of the safety prediction neural network model in construction are stress, strain, displacement and settlement prediction values of the next time node in construction;
and 3, connecting the pre-construction safety prediction neural network model and the in-construction safety prediction neural network model in series by taking the output parameters of the pre-construction safety prediction neural network model as the input parameters of the in-construction safety prediction neural network model to form a construction prediction series model, and performing staged safety prediction in the whole construction process by using the construction prediction series model.
2. The method of claim 1, further comprising: and inputting the stress, strain, displacement and settlement monitored in real time in the construction into the in-construction safety prediction neural network model so as to predict the stress, strain, displacement and settlement of the next time node in real time in the construction through the in-construction safety prediction neural network model.
3. The method of claim 1, wherein the current output value of the node weight of each layer of the construction prediction series model is calculated by the following formula:
wherein,
is the input sum of the ith element of the kth layer;
Is the output of the ith element of the kth layer;
The connection weight value from the ith element of the kth-1 layer to the jth element of the kth layer is obtained; f is an excitation function;
Is the actual output of the jth element of the mth layer relative to the weight vector W and the input vector X, wherein the mthThe layer is the output layer.
4. The method of claim 3, wherein the weight of each layer node of the construction prediction series model is adjusted by the following formula:
when k = m, the number of the magnetic poles is zero,
when k is<When m is greater than the total number of the carbon atoms,
wherein,
is the input sum of the ith element of the kth layer;
Is the output of the ith element of the kth layer;
The connection weight from the ith element of the kth-1 layer to the jth element of the kth layer is set; f is an excitation function, which may be S c (x)；
Is the actual output of the jth element of the mth layer relative to the weight vector W and the input vector X; y is i Is the desired output of the jth element of the mth layer associated with the weight vector W and the input vector X, where the mth layer is the output layer.
5. The method according to claim 1, wherein the pre-construction safety prediction neural network model and the in-construction safety prediction neural network model are constructed with a maximum number of iterations set to 5000 and a learning rate η set to 0.5.
6. The method of claim 1, wherein the pre-construction safety prediction neural network performance is determined to be stable when the pre-construction safety prediction neural network model error rate and the in-construction safety prediction neural network model error rate are less than preset values.
7. The multi-factor safety prediction system for the whole process of underground large space construction is characterized by comprising at least one processor and a memory which is in communication connection with the at least one processor; the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 6.
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| Title |
|---|
| 变形监测多模型组合预测方法研究;武鹏;《铁道勘察》;20180215(第01期);5-7 * |
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