CN-121980349-A - Quick general investigation and grading method for structural damage of urban existing building group

CN121980349ACN 121980349 ACN121980349 ACN 121980349ACN-121980349-A

Abstract

The invention discloses a rapid general investigation and classification method for structural damage of an existing city building group. The method comprises the steps of obtaining space-time unification point clouds of a vehicle-mounted laser radar and constructing building objects, carrying out real-time calculation and evaluation on elevation coverage sufficiency and data reliability of each building object according to a preset grading evidence template, generating grading evidence scores and positioning evidence gaps, generating a compensation strategy based on a minimum action principle aiming at the evidence gaps, solving a compensation action set which can bring maximum evidence gain and has minimum execution cost, and executing the compensation action to obtain incremental data and carrying out reflux fusion until grading requirements are met. The invention effectively solves the problems of low efficiency and evidence deletion caused by blind acquisition in the prior art by establishing a closed-loop control mechanism of acquisition, evaluation, decision-making and compensation, and realizes the credible acquisition of the structural hierarchical evidence with minimum cost in a single task window.

Inventors

WEI XIAOBIN
TANG DONGYING
LIU YANG
GUO JIANXIANG
WANG RUOCHEN
WANG CHENG

Assignees

江苏省建筑工程质量检测中心有限公司

Dates

Publication Date: 20260505
Application Date: 20260123

Claims (10)

1. A rapid general investigation and grading method for structural damage of an existing city building group is characterized by comprising the following steps: Acquiring original point cloud and pose track data acquired by a vehicle-mounted laser radar, and performing space-time alignment processing on the original point cloud and pose track data to obtain space-time uniform point cloud; Building clustering and elevation extraction are carried out based on space-time unification point clouds, and a building object set and a building elevation fragment set corresponding to each building object are constructed; according to a pre-configured hierarchical evidence template, computing and evaluating the coverage sufficiency and the data reliability of the elevation coverage of each building object in the building object set to obtain a hierarchical evidence score, and identifying areas which do not meet the requirements of the hierarchical evidence template to obtain an evidence gap list; generating a compensation strategy based on a minimum action principle aiming at gaps existing in the evidence gap list, wherein the compensation strategy comprises a compensation action set which is generated aiming at specific gaps and maximizes evidence gain and minimizes execution cost; And when the grading evidence grading meets the preset grading requirement, outputting a structure damage grading result aiming at the building object set.
2. The method of claim 1, wherein computing and evaluating the sufficiency of coverage of the facade and the data reliability of each building object in the set of building objects according to the pre-configured hierarchical evidence template to obtain a hierarchical evidence score, and identifying areas that do not meet the requirements of the hierarchical evidence template to obtain an evidence gap list, comprises: reading a hierarchical evidence template, and mapping each segment in the building elevation segment set into a plurality of evidence evaluation units with space boundaries; based on the space-time consistency point cloud, carrying out visibility statistics and shielding analysis on each evidence evaluation unit to generate an observability index set comprising visible area occupation ratio and coverage continuity; Comparing the observability index set with the minimum evidence requirement in the hierarchical evidence template, calculating the evidence satisfaction of each evidence evaluation unit, and obtaining hierarchical evidence scores according to building aggregation; and the evidence evaluation unit is used for positioning the evidence with the satisfaction degree lower than a preset threshold and generating an evidence gap list according to the failure reason.
3. The method of claim 2, wherein performing visibility statistics and occlusion analysis on each evidence evaluation unit based on the spatiotemporal consistency point cloud to generate a set of observability indicators comprising visible area occupancy and coverage continuity comprises: Cutting out an effective point set of the observation unit from the space-time unification point cloud according to the space boundary of the evidence evaluation unit, and projecting the effective point set to a facade local coordinate system for rasterization treatment to obtain a facade coverage grid; Determining a vehicle travelling track based on pose track data when the space-time consistency point cloud is acquired, and constructing an observation ray set for connecting a sensor viewpoint and a facade coverage grid unit around the vehicle travelling track and the facade normal direction of the evidence evaluation unit when the space-time consistency point cloud is acquired; Performing visibility judgment along the observation ray set, identifying a foreground shelter on a ray path, and extracting the strength of a shelter trace to distinguish a real data missing area from a shelter area on a vertical face; and calculating to obtain an observability index set based on occupation statistics and shielding trace intensity of the elevation coverage grid.
4. A method according to claim 3, wherein performing a visibility determination along the set of observed rays, identifying foreground obscurations on the ray path, extracting an occlusion trace intensity, comprises: Detecting whether the observed ray set passes through a space region with high point density before reaching the evidence evaluation unit, and identifying a close-range high-density shielding band caused by a foreground object; Counting the stability of the occupation state of the elevation covering grid along with the change of the vehicle advancing visual angle, and identifying the unstable area occupied by the grid; calculating the depth distribution of the effective point set of the observation unit relative to the normal direction of the vertical face of the evidence evaluation unit, and identifying a point depth abrupt change zone; And (5) integrating the distribution characteristics of the close-range high-density shielding belt, the unstable area occupied by the grid and the point depth abrupt change belt, and calculating the strength of shielding traces.
5. The method of claim 2, wherein locating the evidence evaluation unit with the degree of satisfaction below the predetermined threshold generates the evidence gap list based on the failure cause thereof, comprising: according to the specific index item that the evidence evaluation unit does not pass through the grading evidence template, the gap is classified into a coverage deficiency type, a density deficiency type, a reliability deficiency type or a shielding dominant type; generating a corresponding requirement description field of the replenishable card aiming at each gap after attribution, wherein the requirement description field comprises a coverage type needing to be replenished, a point density target value needing to be lifted or a shielding direction needing to be relieved; and (3) carrying out association packaging on the space positioning information, the gap type and the requirement description field of the evidence evaluation unit to obtain an evidence gap list.
6. The method of claim 1, wherein generating a replenishment strategy based on a minimum action principle for gaps present in the evidence gap list comprises: Aiming at a building object with an evidence gap, generating a group of candidate compensation action sets meeting the running constraint of the vehicle; predicting the repair capability of each action in the candidate compensation action set aiming at the evidence gap based on the building elevation fragment set to generate action evidence gain assessment; Calculating the execution cost and disturbance risk of each action in the candidate compensation action set, and generating action cost evaluation; And selecting an optimal combination from the candidate compensation action set to generate a compensation action set by taking the requirement in the evidence gap list as a constraint and taking the maximization action evidence gain evaluation and the minimization action cost evaluation as targets.
7. The method of claim 6, wherein predicting the repair capability of each action in the set of candidate complementary scanning actions for the evidence gap based on the set of building facade segments, generating an action evidence gain assessment, comprises: Expanding each candidate compensation action into action instance parameters comprising an expected travelling track and a sampling configuration; Simulating a virtual view angle corresponding to the action instance parameter in a coordinate space where the building elevation segment set is located, and calculating the coverage potential of the virtual view angle on the elevation area where the evidence gap is located; And predicting the visible area occupation ratio improvement and the effective point density improvement which can be brought by the candidate complementary scanning action based on the coverage potential, and obtaining action evidence gain evaluation.
8. The method of claim 7, further comprising, after generating the action evidence gain assessment: the visible area occupation ratio in the action evidence gain evaluation is improved, the effective point density is improved, and the visible area occupation ratio and the effective point density are mapped to the elevation subregions corresponding to all the gaps in the evidence gap list; Comparing the requirement description fields in the evidence gap list, and calculating gap satisfaction improvement estimation of each candidate compensation action on each evidence gap; based on the gap satisfaction degree improvement estimation, a gap coverage prediction table recording the satisfaction relation between the candidate compensation action and the evidence gap is generated.
9. The method of claim 8, wherein selecting an optimal combination from the candidate set of complement scan actions with the constraint of meeting requirements in the evidence gap list with the goal of maximizing action evidence gain assessment and minimizing action cost assessment comprises: constructing a combined optimization model, and setting the lowest evidence requirement in an evidence gap list as a hard constraint condition which must be met; setting the sum minimization of action cost evaluation as a first optimization target, and setting the total satisfaction degree improvement maximization in the gap coverage prediction table as a second optimization target; And solving a combination optimization model, and screening out the minimum action combination with the minimum quantity and the lowest cost as a compensation action set on the premise of ensuring that the high-priority notch is covered.
10. The method of claim 9, wherein in generating the set of swipe actions, further comprising: performing space-time conflict detection on actions related to adjacent building objects in the minimum action combination, and performing conflict resolution by adjusting the execution sequence or merging the adjacent actions; And configuring a compensation stop condition for the compensation action set, wherein the compensation stop condition comprises stopping when the repaired proportion of the gap in the evidence gap list reaches a preset threshold value or stopping when the accumulated execution cost reaches a preset upper limit.

Description

Quick general investigation and grading method for structural damage of urban existing building group Technical Field The invention belongs to the technical field of building structure detection, and particularly relates to a rapid general investigation and classification method for structural damage of an existing building group in a city. Background With the acceleration of urban updating process, the general investigation of structural safety and damage states of existing building groups has become a serious issue for urban public safety management. Compared with traditional manual close-range photography or frame construction detection, the vehicle-mounted laser radar LiDAR is utilized for conducting large-range non-contact street view scanning, and the vehicle-mounted laser radar LiDAR is a mainstream technical means for general investigation and digital construction of urban-level buildings at present due to the high efficiency and full digitization characteristics. The existing vehicle-mounted laser radar screening technology generally adopts an open-loop operation mode of acquisition and processing. The census vehicle generally performs traversal scanning on the neighborhood according to a pre-planned fixed path (such as a path covering all lanes or a high overlapping rate), so as to obtain massive original point cloud data. After the acquisition task is finished, the technician performs denoising, splicing, semantic segmentation and three-dimensional reconstruction on the data through an off-line algorithm in the industry, and further extracts the building elevation features for subsequent analysis. This mode relies on high redundancy coverage of the acquisition phase in an attempt to reduce the dead zone rate. However, the blindness problem caused by the acquisition and evaluation dislocation generally exists in the prior art, and the census efficiency and the credibility of the grading evidence are difficult to be considered in a single task. Specifically, due to lack of real-time evidence sufficiency feedback, the acquisition end cannot sense whether the current scan data has met the lowest evidence threshold of structural classification (e.g., whether the key components are blocked by trees, and whether the point density meets the standard). The blind scanning mechanism causes two deep contradictions, namely, on one hand, evidence gaps caused by dynamic shielding or limited visual angles can be found only in an offline processing stage and are forced to be acquired by high-cost secondary reworking, and on the other hand, in order to avoid the risk of missed scanning, the existing scheme often adopts an indiscriminate multi-pass scanning strategy, and lacks a minimum action optimization mechanism aiming at a specific gap, so that a large amount of invalid data can be acquired and stored, and the maximum evidence gain can not be acquired with minimum marginal cost. Disclosure of Invention The invention aims to provide a rapid screening and grading method for structural damage of an existing building group in a city, so as to solve the problems in the prior art. According to the technical scheme, the rapid general investigation and classification method for the structural damage of the existing city building group comprises the following steps: Acquiring original point cloud and pose track data acquired by a vehicle-mounted laser radar, and performing space-time alignment processing on the original point cloud and pose track data to obtain space-time uniform point cloud; Building clustering and elevation extraction are carried out based on space-time unification point clouds, and a building object set and a building elevation fragment set corresponding to each building object are constructed; according to a pre-configured hierarchical evidence template, computing and evaluating the coverage sufficiency and the data reliability of the elevation coverage of each building object in the building object set to obtain a hierarchical evidence score, and identifying areas which do not meet the requirements of the hierarchical evidence template to obtain an evidence gap list; generating a compensation strategy based on a minimum action principle aiming at gaps existing in the evidence gap list, wherein the compensation strategy comprises a compensation action set which is generated aiming at specific gaps and maximizes evidence gain and minimizes execution cost; And when the grading evidence grading meets the preset grading requirement, outputting a structure damage grading result aiming at the building object set. The method has the beneficial effects that the problems of low efficiency and evidence deletion caused by blind acquisition in the prior art are effectively solved by establishing a closed-loop control mechanism of acquisition, evaluation, decision-making and compensation, and the credible acquisition of the structural grading evidence is realized with minimum cost in a single task window. Drawi