Construction quality evaluation and construction process optimization method based on multi-sensor data

Abstract

The invention discloses a construction quality evaluation and construction process optimization method based on multi-sensor data. The method comprises the following steps: determining the types and quantity of construction processes of constructional engineering, control key points of construction quality, index factors of quality evaluation and limit values of the index factors; setting a quality evaluation grade and membership functions corresponding to different index factors; determining the membership degree of each piece of sensor data to the comment set of the quality evaluation grade; determining fuzzy evaluation matrixes of different construction processes; determining weights of the index factors; defining scores of different comment sets by adopting a centesimal system to form a score set; calculating to obtain comprehensive evaluation vectors of different construction processes; calculating to obtain comprehensive evaluation indexes of different construction processes; and determining a preferable construction process. According to the method, the problem that an existing construction quality evaluation method cannot be quantized due to multiple construction quality control key points during optimization of multiple construction processes of building engineering is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of building construction, and in particular to a construction quality evaluation and construction process optimization method based on multi-sensor data. Background Art

[0002] In the current construction process determination of construction projects, it is mostly based on experience and is highly subjective. Although when the construction project is complex or there is a lack of relevant experience, the feasibility of the process can be determined more objectively by carrying out sample section construction and measuring different construction quality control points. However, when faced with multiple construction process comparisons, since there are many construction quality control points in construction projects and their importance varies, the comprehensive comparative evaluation method for construction quality is still imperfect. Summary of the invention

[0003] In order to overcome the defects of the existing technology, a construction quality evaluation and construction process optimization method based on multi-sensor data is provided to solve the problem that the existing construction quality evaluation method cannot be quantified due to the large number of construction quality control points when optimizing multiple construction processes in construction projects.

[0004] To achieve the above purpose, a method for construction quality evaluation and construction process optimization based on multi-sensor data is provided, comprising the following steps:

[0005] Determine the types and quantity of construction techniques for building projects and the key points for controlling construction quality;

[0006] Based on the control points, determine the index factors and their limits for quality evaluation;

[0007] Set the quality evaluation level and the membership functions corresponding to different index factors;

[0008] Carry out construction according to different construction techniques and obtain data of different index factors under different construction techniques through sensors;

[0009] Determining the membership of each sensor data to the comment set of the quality evaluation level according to the membership function;

[0010] Determining the fuzzy evaluation matrix of different construction processes based on the membership degree of the comment set of the quality evaluation level;

[0011] Determining the weights of the indicator factors;

[0012] Defining scores of different review sets using a percentage system to form score sets;

[0013] Obtaining a comprehensive evaluation vector of different construction techniques based on the weights of the index factors and the fuzzy evaluation matrix;

[0014] Based on the score set and the comprehensive evaluation vector, calculating and obtaining comprehensive evaluation indexes of different construction techniques;

[0015] The optimal construction process is determined by comparing the comprehensive evaluation indexes of different construction processes.

[0016] Furthermore, the step of determining the fuzzy evaluation matrix of different construction processes based on the membership degree of the comment set of the quality evaluation level includes:

[0017] Calculate the average membership of the i-th index factor in the k-th construction process to the comment set;

[0018] The average membership degrees of m types of index factors of the kth construction process are sorted out to obtain the fuzzy evaluation matrix of the kth construction process.

[0019] Furthermore, the step of determining the weight of the indicator factor includes:

[0020] Normalize the same type of sensor data for different construction processes;

[0021] Calculate the ratio of all i-th type indicator factor data obtained by each sensor data in all types of construction processes;

[0022] Based on the ratio, the information entropy of various indicator factors is calculated;

[0023] Based on the information entropy, the weights of the indicator factors are calculated.

[0024] The beneficial effect of the present invention is that the construction quality evaluation and construction process optimization method based on multi-sensor data of the present invention comprehensively considers different construction quality control points, and realizes comprehensive quantitative evaluation of construction technology based on quality evaluation indicators obtained based on multi-sensor data, and the results are objective and direct.

[0025] The construction quality evaluation and construction process optimization method based on multi-sensor data of the present invention does not limit the length of similar sensor data obtained by the construction process, and has strong adaptability to the site.

[0026] The weight calculation of the construction quality evaluation and construction process optimization method based on multi-sensor data of the present invention is objective, comprehensively considers the contribution of different construction process data to information, and simultaneously considers the discreteness of data within the same construction process. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0028] Figure 1It is a flowchart of a method for construction quality evaluation and construction process optimization based on multi-sensor data according to an embodiment of the present invention.

[0029] Figure 2 The following is a process for determining weights of different evaluation indicators in an embodiment of the present invention. DETAILED DESCRIPTION

[0030] The present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the relevant invention, rather than to limit the invention. It is also necessary to explain that, for ease of description, only the parts related to the invention are shown in the accompanying drawings.

[0031] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0032] Reference Figure 1 and Figure 2 As shown, the present invention provides a construction quality evaluation and construction process optimization method based on multi-sensor data, comprising the following steps:

[0033] S1. Determine the types and quantity of construction techniques for building projects and the key points for controlling construction quality.

[0034] S2. Based on the key control points, determine the index factors and their limits for quality evaluation.

[0035] Specifically, step S2 includes:

[0036] S21. According to the key points of construction quality control, determine the corresponding quantitative indicators to form an indicator factor set including m types of indicators;

[0037] S22. Determine the limit value corresponding to the quantitative indicator.

[0038] S3. Set the quality evaluation level and the membership functions corresponding to different indicator factors.

[0039] Specifically, step S3 includes:

[0040] S31: According to the evaluation index limit, divide the quality grade comment set V containing s evaluation grades.

[0041] S32: Determine the ranges corresponding to different indicators at different quality levels.

[0042] S33: Determine the membership functions of different indicators at different quality levels.

[0043] S4. Carry out construction according to different construction techniques and obtain data of different indicator factors under different construction techniques through sensors.

[0044] Sensor layout planning is carried out according to relevant evaluation indicators. For some parameters that need to obtain data through pre-embedded sensors, the pre-embedded sensors are completed before construction. After the pre-embedded sensors are completed, construction is carried out according to different construction processes.

[0045] Through sensors, data of m types of indicators under n different construction processes are obtained. For the kth construction process, the amount of sensor data of the i-th indicator (i≤m) obtained is h ik , the amount of sensor data of the jth index (j≤m, j≠i) is h jk ,h ik With h jk The size relationship of is not limited and can be randomly determined according to the on-site sensor conditions.

[0046] S5. Determine the membership degree of each sensor data to the comment set of the quality evaluation level according to the membership function.

[0047] According to the membership functions of different indicators at different quality levels, determine the membership of the rth sensor data of the i-th indicator (i≤m) obtained by the k-th construction process for different evaluation levels (t=1,2……s), and finally form the membership matrix of the kth construction technology and the ith index (i≤m)

[0048]

[0049] S6. Based on the membership degree of the comment set of the quality evaluation level, determine the fuzzy evaluation matrix of different construction processes.

[0050] Specifically, step S6, the step of determining the fuzzy evaluation matrix of different construction processes based on the membership degree of the comment set of the quality evaluation level includes:

[0051] S61. Calculate the average membership of the i-th index factor in the k-th construction process to the comment set

[0052]

[0053] In this step, sensor data of different lengths are classified into a membership vector of equal length.

[0054] S62. Arrange the average membership of the m-type index factors of the k-th construction process to obtain the fuzzy evaluation matrix R of the k-th construction process k .

[0055]

[0056] S7. Determine the weights of indicator factors.

[0057] Specifically, step S7, the step of determining the weight of the indicator factor includes:

[0058] S71. Normalize the sensor data of the same type with different construction processes.

[0059] Assume that h obtained by the kth construction process ik The data of the i-th indicator is Normalize as follows:

[0060]

[0061] S72, calculate the ratio P of all i-th index factor data obtained by each sensor data in all n construction processes C .

[0062]

[0063] S73. Based on the ratio, calculate and obtain the information entropy of various indicator factors.

[0064] Find the information entropy of various indicators, the i-th indicator e i :

[0065]

[0066] Among them, m types of indicators, n types of comparison processes, j types of indicators, h jk is the amount of sensor data of the jth indicator obtained by the kth comparison process.

[0067] S74. Based on information entropy, the weights of the indicator factors are calculated.

[0068] Get the weight vector W of various indicators, where the weight W j ;

[0069]

[0070] S8. Define the scores of different comment sets using a percentage system to form a score set.

[0071] Specifically, the scores corresponding to different comment sets V are defined using a percentage system to form a corresponding score set v = {v1, v2, v3...v s}.

[0072] S9. Based on the weights of the index factors and the fuzzy judgment matrix, the comprehensive evaluation vector of different construction techniques is obtained.

[0073] Specifically, calculate the comprehensive evaluation vector B of the kth construction technology k =Wk ·R k ={b1,b2...b s} k .

[0074] S10. Based on the score set and the comprehensive evaluation vector, the comprehensive evaluation index of different construction techniques is calculated.

[0075] Calculate the comprehensive evaluation index I corresponding to the kth construction technology k .

[0076]

[0077] S11. Determine the optimal construction process by comparing the comprehensive evaluation indexes of different construction processes.

[0078] The construction quality evaluation and construction process optimization method based on multi-sensor data of the present invention comprehensively considers different construction quality control points, and realizes comprehensive quantitative evaluation of construction technology based on quality evaluation indicators obtained based on multi-sensor data, and the results are objective and direct.

[0079] The construction quality evaluation and construction process optimization method based on multi-sensor data of the present invention does not limit the length of similar sensor data obtained by the construction process, and has strong adaptability to the site.

[0080] The weight calculation of the construction quality evaluation and construction process optimization method based on multi-sensor data of the present invention is objective, comprehensively considers the contribution of different construction process data to information, and simultaneously considers the discreteness of data within the same construction process.

[0081] In order to further explain the construction quality evaluation and construction process optimization method based on multi-sensor data of the present invention in detail, refer to Figure 1 The invention is shown and described in conjunction with the following embodiments.

[0082] Embodiment 1

[0083] This embodiment provides a construction quality evaluation and construction process optimization method based on multi-sensor data, including the following steps:

[0084] a. Determine the types and quantity of construction techniques for construction projects and the key points for controlling construction quality.

[0085] Among them, n different construction techniques are proposed;

[0086] It is now necessary to construct a large concrete wall, and two processes are planned, namely process A and process B, that is, n=2.

[0087] According to the construction requirements, sort out the key points of construction quality control;

[0088] According to the construction requirements, the wall should not have temperature cracks, the deviation between the wall and the theoretical surface coordinates should be ≤±5mm, and the roughness should be less than 0.2mm. Therefore, the key points for controlling the construction quality are the temperature stress of the concrete, the wall position, and the wall roughness.

[0089] b. Based on the control points, determine the quality evaluation index factors and their limits

[0090] Among them, determine the index factors and corresponding limits of quality evaluation;

[0091] Step b-1: According to the key points of construction quality control, determine the corresponding quantitative indicators to form an indicator factor set containing m types of indicators;

[0092] In this embodiment, the temperature stress of concrete can be evaluated by the temperature difference between the inside and outside of concrete, the wall position can be evaluated by the absolute deviation of the key point coordinates, and the roughness can be directly evaluated by the measured roughness. Thus, an indicator factor set containing three types of quantitative indicators is formed: the temperature difference between the inside and outside of concrete x, the absolute deviation of the key point coordinates y, and the roughness z;

[0093] Step b-2: Determine the limit value corresponding to the quantitative indicator.

[0094] According to the key points of construction quality control, the limits of different indicators are determined. In this embodiment, the limit of the maximum inner and outer temperature difference ΔT that allows the concrete wall to not have temperature cracks can be obtained by referring to relevant references (such as theoretical calculations, existing literature, numerical simulations, etc.), and is 22°C after theoretical calculation; the absolute deviation limit of the key point coordinates is 5mm, and the roughness limit is 0.2mm.

[0095] c. Set the quality evaluation level and the membership functions corresponding to different indicator factors.

[0096] Among them, set the quality evaluation level and the corresponding membership degree of different parameters:

[0097] Step c-1: According to the evaluation index limit, divide the quality level comment set V containing s evaluation levels

[0098] In this embodiment, the quality levels corresponding to different quantitative indicators are divided into quality level comments including three evaluation levels, namely "good, better, and poor", so V = {good, better, and poor}.

[0099] Step c-2: Determine the range of different indicators at different quality levels

[0100] In this embodiment, when the temperature difference x between the inside and outside of the concrete is between 0 and 10°C, it corresponds to "good", when x is between 10°C and 22°C, it corresponds to "better", and when x is greater than 22°C, it corresponds to "bad"; when the absolute deviation of the key point coordinates y is between 0 and 3mm, it corresponds to "good", when y is between 3 and 5mm, it corresponds to "bad", and when y is greater than 5mm, it corresponds to "bad"; when the roughness z is between 0 and 0.1mm, it corresponds to "good", when z is between 0.1 and 0.2mm, it corresponds to "better", and when z is greater than 0.2mm, it corresponds to "bad".

[0101] Step c-3: Determine the membership functions of different indicators at different quality levels.

[0102] In this embodiment, the membership functions of different parameters at different levels all adopt triangular membership functions. That is, the membership of the concrete inner and outer temperature difference x, the absolute deviation of the key point coordinates y, and the roughness z for different quality levels is determined according to the following table 1.

[0103] Table 1. Membership function table of different parameters at different levels

[0104]

[0105] d. Carry out construction according to different construction techniques and obtain data of different index factors under different construction techniques through sensors

[0106] Among them, sensor pre-embedding and different construction processes are carried out:

[0107] Sensor layout planning is carried out according to relevant evaluation indicators. For some parameters that need to obtain data through pre-embedded sensors, the pre-embedded sensors are completed before construction. After the pre-embedded sensors are completed, construction is carried out according to different construction processes.

[0108] In this embodiment, three temperature sensors are embedded in process A, and the inside and outside temperature differences of three groups of concrete can be obtained; four temperature sensors are embedded in process B, and the inside and outside temperature differences of four groups of concrete can be obtained.

[0109] Sensor data acquisition:

[0110] Through sensors, data of m types of indicators under n different construction processes are obtained. For the kth construction process, the amount of sensor data of the i-th indicator (i≤m) obtained is h ik , the amount of sensor data of the jth index (j≤m, j≠i) is h jk ,h ik With h jk The size relationship of is not limited and can be randomly determined according to the on-site sensor conditions.

[0111] In this embodiment, data of three types of indicators under two different construction processes are obtained. For construction process A, the inside and outside temperature differences of three groups of concrete are obtained through different sensors, which are 18°C, 22°C, and 24°C respectively; the absolute deviations of the coordinates of five key points are obtained, which are 0.5mm, 2mm, 1mm, 3mm, and 4mm respectively; the roughness of four positions is obtained, which is 0.15mm, 0.1mm, 0.12mm, and 0.04mm respectively. For construction process B, the inside and outside temperature differences of four groups of concrete are obtained through different sensors, which are 16°C, 21°C, 25°C, and 24°C respectively; the absolute deviations of the coordinates of four key points are obtained, which are 0.3mm, 1.5mm, 4mm, and 2mm respectively; the roughness of five positions is obtained, which is 0.15mm, 0.15mm, 0.16mm, 0.06mm, and 0.03mm respectively.

[0112] e. Determine the membership degree of each sensor data to the comment set of the quality evaluation level according to the membership function.

[0113] Specifically, the membership degree of each sensor data to the comment set V is determined according to the membership function:

[0114] According to the membership functions of different indicators at different quality levels, determine the membership of the rth sensor data of the i-th indicator (i≤m) obtained by the k-th construction process for different evaluation levels (t=1,2……s), and finally form the membership matrix of the kth construction technology and the ith index (i≤m)

[0115]

[0116] In this embodiment, the membership of each sensor data of method A to the comment set V is:

[0117] (1) The temperature difference between the inside and outside of the three groups of concrete is: 18°C: [0, 2 / 3, 1 / 3]; 22°C: [0, 0, 1]; 24°C: [0, 0, 1]. The membership matrix of the first category index (the temperature difference between the inside and outside of concrete) is:

[0118]

[0119] (2) The absolute deviations of the coordinates of the five key points are: 0.5 mm: [1, 0, 0]; 2 mm: [1, 0, 0]; 1 mm: [1, 0, 0]; 3 mm: [1, 0, 0]; 4 mm: [0, 1, 0]. The membership matrix of the second category of indicators (absolute deviations of key point coordinates) is:

[0120]

[0121] (3) Roughness at four positions: 0.15 mm: [0, 1, 0]; 0.1 mm: [1, 0, 0]; 0.12 mm: [0.6, 0.4, 0]; 0.04 mm: [1, 0, 0]; then the membership matrix of the third index (roughness) is:

[0122]

[0123] Similarly, using the same method B, the membership of each sensor data to the comment set V is:

[0124]

[0125] f. Based on the membership degree of the comment set of the quality evaluation level, determine the fuzzy evaluation matrix of different construction processes.

[0126] Among them, the fuzzy evaluation matrix of different construction methods is determined as follows:

[0127] Step f-1: Calculate the average membership of the i-th indicator (i≤m) in the k-th construction process to the comment set V

[0128]

[0129] Step f-2: Arrange the average membership degree of m-type indicators of the k-th construction process to obtain the fuzzy evaluation matrix R of the k-th construction process k :

[0130]

[0131] In this embodiment, the average membership of the first indicator in process A—the temperature difference between the inside and outside—to the comment set V is: [(0+0+0) / 3, (2 / 3+0+0) / 3, (1 / 3+1+1) / 3]=[0, 2 / 9, 7 / 9]; the average membership of the second indicator—the absolute deviation of the key point coordinates—to the comment set V is: [4 / 5, 1 / 5, 0]; the average membership of the third indicator—the roughness—to the comment set V is: [13 / 20, 7 / 20, 0];

[0132] The average membership degree of the three types of indicators of process A is sorted out to obtain the fuzzy evaluation matrix R of process A. A :

[0133]

[0134] The same method is used to obtain the fuzzy evaluation matrix R of process B B for:

[0135]

[0136] g. Determine the weights of the indicator factors.

[0137] Step g-1: Normalize the sensor data of the same type in different processes. Assume that h obtained by the kth construction process ik The data of the i-th indicator is Normalize as follows:

[0138]

[0139] Step g-2: the ratio of all i-th indicator data obtained by each sensor data in all n construction processes;

[0140]

[0141] The contribution of different construction process data to information is comprehensively considered, and the discreteness of data within the same construction process is also considered. For example, in the example, the roughness in methods A and B is first normalized (internal differences), and then the ratio is considered together (considering the contribution of all sensor data to information).

[0142] Step g-3: Calculate the information entropy of each type of indicator, the i-th type indicator e i :

[0143]

[0144] Among them, m types of indicators, n types of comparison processes, j types of indicators, h jk is the amount of sensor data of the jth indicator obtained by the kth comparison process.

[0145] Step g-4: Get the weight vector W of each indicator, where the weight W j ;

[0146]

[0147] According to step g-1:

[0148] In this embodiment, the sensor data of the same type in processes A and B are normalized to obtain:

[0149]

[0150] According to step g-2:

[0151] The ratio of each sensor data to all sensor data of the same index:

[0152]

[0153] The data in the table take all the first category index data obtained by construction process A as an example:

[0154] 1 / (1+0.333+0.001+1+0.444+0.001+0.111)=0.346;

[0155] 0.333 / (1+0.333+0.001+1+0.444+0.001+0.111)=0.115;

[0156] 0.001 / (1+0.333+0.001+1+0.444+0.001+0.111)=0.

[0157] According to step g-3:

[0158] Information entropy of various indicators:

[0159] Temperature difference between inside and outside of concrete Absolute deviation of keypoint coordinates Roughness 1.276 1.714 1.728

[0160] The calculation process of the temperature difference between the inside and outside of concrete is as follows:

[0161]

[0162] According to step g-4:

[0163] The weight vector of each index is W = [0.1610.4150.424]; the calculation process of the weight of the temperature difference between the inside and outside of concrete is as follows:

[0164]

[0165] h. Define the scores of different review sets using a percentage system to form a score set.

[0166] Specifically, the scores corresponding to different comment sets V are defined using a percentage system to form a corresponding score set v = {v1, v2, v3...v s}.

[0167] In this embodiment, the scores corresponding to different comment sets V are defined using a percentage system, forming a corresponding score set v={100, 85, 60}.

[0168] i. Based on the weights of the index factors and the fuzzy evaluation matrix, a comprehensive evaluation vector of different construction techniques is obtained.

[0169] Calculate the comprehensive evaluation vector B of the kth construction technology k =W k ·R k ={b1,b2...b s} k .

[0170] A comprehensive evaluation vector E of method A :

[0171] The comprehensive evaluation vector E of method B B : j. Based on the score set and the comprehensive evaluation vector, the comprehensive evaluation index of different construction techniques is calculated.

[0172] Calculate the comprehensive evaluation index I corresponding to the kth construction technology k :

[0173]

[0174] The corresponding comprehensive evaluation index I of method A A

[0175]

[0176] The corresponding comprehensive evaluation index I of method B B

[0177]

[0178] k. Determine the preferred construction process by comparing the comprehensive evaluation indexes of different construction processes.

[0179] The process with the largest comprehensive evaluation index is selected as the preferred process.

[0180] Because I A >I B , process A is selected as the preferred process.

[0181] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the inventive concept. For example, the above features are replaced with (but not limited to) technical features with similar functions disclosed in the present application.

Claims

1. A construction quality evaluation and construction process optimization method based on multi-sensor data, characterized in that: The following steps are involved: Determine the types and quantity of construction techniques for building projects and the key points for controlling construction quality; Based on the control points, determine the index factors and their limits for quality evaluation; Set the quality evaluation level and the membership functions corresponding to different index factors; Carry out construction according to different construction techniques and obtain data of different index factors under different construction techniques through sensors; Determining the membership of each sensor data to the comment set of the quality evaluation level according to the membership function; Determining the fuzzy evaluation matrix of different construction processes based on the membership degree of the comment set of the quality evaluation level; Determining the weights of the indicator factors; Defining scores of different review sets using a percentage system to form score sets; Obtaining a comprehensive evaluation vector of different construction techniques based on the weights of the index factors and the fuzzy evaluation matrix; Based on the score set and the comprehensive evaluation vector, calculating and obtaining comprehensive evaluation indexes of different construction techniques; The optimal construction process is determined by comparing the comprehensive evaluation indexes of different construction processes.

2. The method for construction quality evaluation and construction process optimization based on multi-sensor data according to claim 1 is characterized in that: The step of determining the fuzzy evaluation matrix of different construction processes based on the membership degree of the comment set of the quality evaluation grade comprises: Calculate the average membership of the i-th index factor in the k-th construction process to the comment set; The average membership degrees of m types of index factors of the kth construction process are sorted out to obtain the fuzzy evaluation matrix of the kth construction process.

3. The method for construction quality evaluation and construction process optimization based on multi-sensor data according to claim 1 is characterized in that: The step of determining the weight of the indicator factor comprises: Normalize the same type of sensor data for different construction processes; Calculate the ratio of all i-th type indicator factor data obtained by each sensor data in all types of construction processes; Based on the ratio, the information entropy of each indicator factor is calculated; based on the information entropy, the weight of the indicator factor is calculated.

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