Bridge construction progress monitoring and early warning method and system

Abstract

The present disclosure provides a bridge construction progress monitoring and early warning method and system, the method comprising: analyzing the construction plan, taking the symmetry axis of the bridge structure as the reference, establishing the progress reference sequence for the symmetric construction segment; collecting the actual progress data of the construction site, comparing it with the progress reference sequence, identifying and calculating the progress time difference between the symmetric segments; based on the progress time difference, estimating the additional mechanical effect difference generated at the key parts of the bridge structure due to the asymmetric construction progress; integrating the current state, change trend and spatial distribution sensitivity of the additional mechanical effect difference, performing multi-dimensional risk coupling assessment, and generating graded warning information according to the assessment results. The method constructs a progress monitoring and early warning system, tracks the symmetry state of the cantilever construction in real time, converts the progress deviation into a mechanical risk index, and realizes the early warning of the tower root stress.

Description

Technical Field

[0001] This disclosure relates to the field of fault prediction and health management in bridge construction, and in particular to a method and system for monitoring and early warning of bridge construction progress. Background Technology

[0002] In the field of fault prediction and health management in bridge construction, particularly for the construction of semi-floating bridge systems such as cable-stayed or suspension bridges, ensuring the symmetrical balance of the cantilever construction of the main girder is crucial. Semi-floating bridge systems connect the main girder to the main tower via spherical steel bearings and longitudinal viscous dampers. This design allows the main girder to undergo slight sliding or rotation under temperature changes or external forces to absorb vibrations and alleviate stress concentration, but it also introduces unique structural sensitivities. During the cantilever construction phase, workers need to add segments symmetrically from the main tower outwards to maintain overall balance.

[0003] Traditional monitoring methods primarily focus on the construction progress of individual segments, such as tracking the start and completion times of each segment through a schedule, but neglect the pairing relationship between symmetrical segments. This isolated monitoring approach cannot effectively capture the mechanical risks caused by construction asymmetry. As a result, in actual construction, when one segment is delayed due to equipment failure or other reasons while the other continues to progress, the base of the main tower will be subjected to huge unbalanced bending moments. Because the flexible boundary design of the spherical support can only buffer displacement but cannot actively balance the force, this bending moment difference is directly transmitted to the main tower, which may cause the tower to tilt, local concrete cracking, or even structural instability.

[0004] It is evident that current mainstream methods still focus on monitoring the time dimension of schedule, lacking a mechanism to map schedule differences to mechanical effects, and thus failing to estimate tower root bending moment differences or provide early warnings of accumulated risks in real time. More seriously, short-term schedule fluctuations may be overlooked, while long-term unilateral delays can gradually amplify risks through the slack-slip effect of supports or residual deformation of concrete, and existing technologies struggle to detect this trend of deterioration. These shortcomings not only increase construction safety risks but also drive up maintenance costs and schedule uncertainty.

[0005] Therefore, there is an urgent need for a bridge construction progress monitoring and early warning method and system that can track symmetrical progress in real time, quantify mechanical effects, and provide dynamic early warnings to ensure structural stability and engineering efficiency in the construction of semi-floating bridge systems. Summary of the Invention

[0006] In view of this, in order to solve the problems caused by the existing technology, this application provides a method and system for monitoring and early warning of bridge construction progress.

[0007] In a first aspect, this disclosure provides a method for monitoring and early warning of bridge construction progress, the method comprising:

[0008] Analyze the construction plan and establish a schedule baseline sequence for symmetrical construction segments, using the bridge structure's axis of symmetry as a reference.

[0009] Collect actual progress data from the construction site, compare it with the progress benchmark sequence, and identify and calculate the progress time difference between symmetrical segments;

[0010] Based on the aforementioned time difference, estimate the difference in additional mechanical effects caused by the asymmetry in construction progress at key parts of the bridge structure.

[0011] By integrating the current state, trend, and spatial distribution sensitivity of the additional mechanical effect difference, a multi-dimensional risk coupling assessment is conducted, and graded early warning information is generated based on the assessment results.

[0012] Optionally, the establishment of the schedule baseline sequence includes:

[0013] Identify the directional features and position numbers in the construction segment identifiers, and construct the pairing relationship between the left and right segments with the center line of the main tower as the axis of symmetry;

[0014] A symmetrical schedule baseline sequence is generated based on the segment pairs in the pairing relationship, as well as the planned start and finish times of each segment;

[0015] By coordinating construction resources through a digital twin platform, resource conflicts in the schedule baseline sequence can be eliminated.

[0016] Optionally, identifying and calculating the progress time difference between symmetrical segments includes:

[0017] Collect actual progress data from the construction site, and extract the timestamp field from the actual progress data for format cleaning and standardization.

[0018] The processed actual progress data is aligned pairwise with the symmetrical progress reference sequence.

[0019] Calculate the time difference between each pair of symmetrical segments.

[0020] Optionally, based on the aforementioned schedule time difference, the difference in additional mechanical effects at key parts of the bridge structure caused by the asymmetry in construction progress is estimated, including:

[0021] Map the schedule time difference to a schedule deviation participation factor;

[0022] Based on the participation coefficient, segmental equivalent gravity, and lever arm parameters, the bending moment difference at key parts of the bridge is calculated.

[0023] Optionally, the calculation of the bending moment difference at key parts of the bridge further includes:

[0024] The bending moment difference is corrected according to the importance weighting coefficient of the segment's spatial location;

[0025] The bending moment difference is corrected using structural boundary condition coefficients based on the locked state of the spherical support and the activated state of the viscous damper.

[0026] Optionally, the multi-dimensional risk coupling assessment, which integrates the current state, changing trend, and spatial distribution sensitivity of the difference in the additional mechanical effects, includes:

[0027] Risk assessment is conducted using a three-pronged mechanism that integrates critical state determination, trend continuity detection, and spatial sensitivity weighting.

[0028] The critical state determination is achieved by comparing the difference in the additional mechanical effects with the design allowable value and the fracture threshold to classify the instantaneous risk level.

[0029] The trend continuity detection quantifies the accumulation of time-dimensional risk by analyzing the cumulative severity and directional persistence of the difference in the additional mechanical effects within a sliding time window.

[0030] The spatial sensitivity weighting is based on the distance of segments from key parts and the importance of the structure, assigning weights to spatial dimension risks;

[0031] Based on the instantaneous risk level, the cumulative risk over time, and the weights in the spatial dimension, a risk coupling assessment result is generated using a coupling algorithm.

[0032] Optionally, the method further includes:

[0033] Based on the results of the risk coupling assessment, warning signals of different levels are output, including at least yellow, orange and red warnings;

[0034] Depending on the type of warning signal, a coordinated response is triggered, including 3D visualization lighting effects, on-site physical warnings, and construction instructions.

[0035] Secondly, this disclosure provides a bridge construction progress monitoring and early warning system, the system comprising:

[0036] Symmetrical schedule baseline building unit: configured to analyze the construction plan, using the axis of symmetry of the bridge structure as a reference, to establish a schedule baseline sequence for symmetrical construction segments;

[0037] Real-time progress comparison unit: configured to collect actual progress data at the construction site, compare it with the progress benchmark sequence, identify and calculate the progress time difference between symmetrical segments;

[0038] Mechanical effect conversion unit: configured to estimate the difference in additional mechanical effects caused by the asymmetry in construction progress at key parts of the bridge structure based on the aforementioned schedule time difference;

[0039] Multi-dimensional early warning decision unit: configured to integrate the current state, changing trend and spatial distribution sensitivity of the additional mechanical effect difference, perform multi-dimensional risk coupling assessment, and generate graded early warning information based on the assessment results.

[0040] Thirdly, this disclosure provides an electronic device including a memory and at least one processor, the memory storing a computer program, and the processor executing the computer program to implement the method of the first aspect described above.

[0041] Fourthly, this disclosure provides a computer storage medium storing a computer program that, when executed, implements the method described in the first aspect.

[0042] The beneficial effects of this disclosure are that, compared with the prior art, this disclosure has the following advantages:

[0043] 1) Addressing the shortcomings of existing technologies that isolate the monitoring of single-segment progress and neglect left-right symmetry correlation, this invention employs segmental symmetry pairing and dynamic baseline generation technology. By intelligently analyzing the construction plan and constructing a spatial equilibrium sequence, the system transforms discrete segment information into a collaborative monitoring unit centered on the main tower, changing the traditional rough mode of schedules that only record time nodes. This mechanism enables the construction team to perceive the collaborative status of the left and right cantilever arms in real time, fundamentally eliminating the risk of tower base bending moment imbalance caused by unilateral lag.

[0044] 2) Addressing the limitation of existing technologies in quantifying the impact of schedule deviations on structural mechanics, this invention establishes a precise conversion engine from time difference to bending moment difference. Based on the unique force transmission characteristics of the spherical support and the boundary conditions of the viscous damper in the semi-floating system, the system achieves a scientific conversion of schedule asymmetry into additional bending moment at the tower base through parametric modeling of equivalent gravity, lever arm, and construction cycle time. This upgrades the early warning mechanism from a simple time-delay alarm to a structural stress safety early warning.

[0045] 3) To address the systemic flaws of misjudging short-term fluctuations and failing to trigger alarms for long-term accumulation, this invention constructs a multi-dimensional risk coupling analysis framework. By integrating three mechanisms—critical state determination, trend continuity detection, and spatial sensitivity weighting—the system can identify both sudden exceedances such as unilateral stagnation caused by equipment failure and gradual risks such as cumulative effects with lag. Combined with BIM 3D lighting warnings and construction command linkage, a closed-loop management system is formed from risk perception to engineering intervention.

[0046] In summary, this invention provides structured input for mechanical mapping through symmetrical monitoring, injects a physical core into risk analysis through the mechanical model, and achieves precise grading of early warnings through a spatiotemporal coupling mechanism. By linking progress monitoring, mechanical transformation, and risk decision-making, semi-floating bridge systems can proactively balance the contradiction between structural stress and construction efficiency during the cantilever construction phase. This avoids serious accidents such as main tower tilting and concrete cracking, and reduces construction delays through dynamic construction adjustments, fundamentally improving the safety assurance capabilities of major bridge projects throughout their entire lifecycle. Attached Figure Description

[0047] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0048] Figure 1 The figure shows a standard cross-sectional view of the main beam of the waterway bridge provided in the disclosed embodiment of the present invention;

[0049] Figure 2A An elevation view of a standard segment of a steel main beam provided in an embodiment of the present invention is shown;

[0050] Figure 2B This is a cross-sectional view of a standard segment of the steel main beam provided in an embodiment of the present invention;

[0051] Figure 3 A flowchart of a bridge construction progress monitoring and early warning method provided in an embodiment of this disclosure is shown;

[0052] Figure 4 A schematic diagram of a bridge construction progress monitoring and early warning system provided in an embodiment of this disclosure is shown.

[0053] The accompanying drawings have illustrated specific embodiments of this disclosure, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concepts of this disclosure to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0054] The present disclosure will be further described below with reference to the accompanying drawings. The following embodiments are only used to illustrate the technical solutions of the present disclosure more clearly, and should not be used to limit the scope of protection of the present disclosure.

[0055] The components of the embodiments of the invention described and illustrated herein can typically be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0056] In the following, the terms “comprising,” “having,” and their cognates, which may be used in various embodiments of the invention, are intended only to indicate a particular feature, number, step, operation, element, component, or combination thereof, and should not be construed as excluding, firstly, the presence of one or more other features, numbers, steps, operations, elements, components, or combinations thereof, or adding the possibility of one or more features, numbers, steps, operations, elements, components, or combinations thereof.

[0057] Unless otherwise specified, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which the various embodiments of the invention pertain. Terms (such as those defined in commonly used dictionaries) shall be interpreted as having the same meaning as in their contextual meaning in the relevant technical field and shall not be interpreted as having an idealized or overly formal meaning, unless clearly defined in the various embodiments of the invention.

[0058] In the field of bridge construction, especially for large cable-stayed or suspension bridge projects with semi-floating systems, the structural balance during construction directly determines the safety of the main tower and the overall stability of the bridge. Figure 1 As shown. Typically, a semi-floating system connects the main beam and the main tower via spherical steel supports and viscous dampers. This allows for limited displacement of the main beam under temperature changes or external forces, while also requiring strict maintenance of the symmetrical balance of the left and right cantilever arms during construction. If the construction progress becomes unbalanced, the difference in cantilever lengths on both sides of the main tower will directly cause additional bending moments at the tower base, potentially leading to serious consequences such as tower tilting or concrete cracking.

[0059] This monitoring plan focuses on the core load-bearing structure of the bridge, the main girder system. For example... Figure 2A and 2B As shown, the single-sided main beam adopts an "I"-shaped cross-section, with a horizontal lower flange and a 2% one-way cross slope on the upper flange. The web is a straight web. When the construction of the left and right cantilever segments is not synchronized, the difference in the self-weight of the beam segments will amplify the bending moment at the tower base through the lever arm effect, while the stiffening ribs in the web will change the local stress transmission path. Therefore, accurate monitoring of the main beam construction progress is not only the foundation for ensuring alignment control, but also the key to preventing structural risks.

[0060] Against this backdrop, this solution constructs a progress monitoring and early warning system. By tracking the symmetry of cantilever construction in real time, it transforms progress deviations into mechanical risk indicators, thereby achieving early warning of tower root stress.

[0061] Figure 3 This disclosure provides a flowchart of a bridge construction progress monitoring and early warning method, as shown in the embodiments below. Figure 3 As shown, the process may include the following steps:

[0062] Step S1: Analyze the construction plan and establish a progress baseline sequence for the symmetrical construction segments based on the axis of symmetry of the bridge structure.

[0063] This step is fundamental to building a full-bridge cantilever balance monitoring system, aiming to transform the traditional discrete segment schedule into a baseline sequence with symmetrical segment pairs as monitoring units. Specifically, it is achieved through the following sub-steps:

[0064] Step S1.1: Read the schedule.

[0065] The system reads the bridge construction organization design document from the engineering data center. This document typically defines the construction plan for all segments of the entire bridge in a structured table format. Key fields in the document include segment identifiers, planned start times, and planned completion times. Considering the potential heterogeneity of the identifier systems used by design institutes in actual projects, the system incorporates a multi-mode parsing engine. This engine first matches the directional character features in the identifiers using regular expressions (e.g., the first letter L or Z represents the left side, and R or Y represents the right side), and then identifies the segment position sequence number using a sequence number extraction algorithm. For unconventional identifiers that cannot be automatically parsed, the system triggers a manual calibration interface, allowing engineers to manually map them using visualization tools. The processing of the time field focuses on resolving the time zone unification issue: the original time string (e.g., "September 1, 2025, 14:30 CST") is converted into the ISO 8601 standard format with time zone offset, ultimately generating a Unix timestamp with millisecond precision. All parsed data is indexed by segment identifiers to form the original progress matrix Pseg, where each row corresponds to a unique segment identifier, and the two columns store the standardized planned start time and planned completion timestamp, respectively.

[0066] Step S1.2: Construction of symmetric relation index.

[0067] Based on the segment identifiers in the standardized progress matrix, the system performs intelligent pairing of symmetrical segments. The core of this process is a spatial location mapping algorithm. Using the main tower centerline as the axis of symmetry, a one-to-one correspondence is established between the segment with sequence number k on the left (e.g., L1) and the segment with sequence number k on the right (e.g., R1), generating an index pair (L1, R1). For special segments (e.g., tower-beam segments or counterweight zone segments), the system adopts a differentiated processing strategy: the tower-beam segment, located directly above the main tower and without symmetry requirements, is marked as an independent unit, Pair_TL; the counterweight zone segment, according to the counterweight design logic, pairs the left and right segments within the same balancing unit as Pair_BWk. To ensure mapping integrity, the system implements a three-layer verification: first, it checks for unmatched isolated segments; second, it verifies the sequence number continuity, such as L1 should be followed by L2 to avoid sequence number jumps; finally, it checks the rationality of the spatial distance between symmetrical segments, calculating the distance using the bridge BIM model coordinates, and triggering an alarm when the distance exceeds a threshold. All verified symmetric pairs are recorded in the symmetric index matrix Mindx. This matrix not only defines the pairing relationship but also adds topological attribute labels, such as standard cantilever segments and tower-beam connection segments, to provide a basis for subsequent mechanical sensitivity classification.

[0068] Step S1.3: Generation of segment-symmetric pairing time series.

[0069] After obtaining the complete symmetric index relationship, the system reorganizes the original schedule matrix Pseg. Specifically, it traverses each valid segment pair in the symmetric index matrix, such as index item (L1, R1), extracts the planned start time and planned completion time of the left segment L1 from Pseg, and simultaneously extracts the planned time corresponding to the right segment R1. The four time points—left segment start time, left segment completion time, right segment start time, and right segment completion time—are bound to the unique identifier Pair_L1R1 of the symmetric pair, forming a symmetric schedule unit. During this process, the system handles special scenarios in the schedule plan: for continuous beam segments cast in sections, if the design document defines that they include sub-stages, such as rebar tying, formwork installation, and concrete pouring, the system automatically merges the sub-stage times into the overall completion time; for segment groups constructed in parallel, the time of the last completed sub-item is taken as the completion time of that segment. All generated schedule units are arranged in spatial order from the main tower to the cantilever end, forming a preliminary sequence framework.

[0070] Step S1.4: Baseline sequence optimization and engineering adaptation.

[0071] The initial sequence is loaded into the bridge construction digital twin platform for spatiotemporal conflict detection. By overlaying the construction machinery movement path model (e.g., tower crane travel range) and segment installation logical dependencies (e.g., prestressing tensioning sequence), the system automatically identifies segment pairs with resource conflicts or procedural inconsistencies (e.g., two adjacent segment pairs are planned to use the same tower crane at the same time). For conflicting segment pairs, the system constructs a construction network logic diagram, identifies critical and non-critical paths, and calculates the floating time window for non-critical path segment pairs based on the critical path method. The floating time window for non-critical path segment pairs represents the time margin for delaying non-critical procedures. While keeping the total construction period unchanged, the system dynamically offsets the planned start time of non-critical path segment pairs using the floating time window, reallocates construction time periods within the floating time window, and intelligently adjusts the relative time difference within segment pairs, thereby eliminating equipment conflict risks. The optimized sequence was solidified into a symmetrical progress baseline sequence Cseq, which serves as the gold standard for full-bridge balance monitoring. It possesses three key technical characteristics: first, it accurately reflects design intent and resource constraints in the time dimension; second, it strictly adheres to symmetrical pairing relationships in the spatial dimension; and third, it ensures executability through conflict resolution in the engineering dimension. Finally, the sequence is output in standard JSON format, simultaneously generating human-readable Gantt charts and balance status heatmaps, providing intuitive guidance for the construction site.

[0072] In the technical solution of this disclosure, the discrete segment schedule is transformed into a spatial balance benchmark sequence centered on the main tower by intelligently parsing the construction organization design document and constructing symmetrical segment pairs. The system's built-in multi-mode parsing engine is compatible with heterogeneous identification systems, and combined with the conflict detection and dynamic optimization of the digital twin platform, it generates a schedule framework that strictly follows the symmetrical construction logic. This process not only establishes a collaborative benchmark for the construction of the left and right cantilever arms, but also lays a reliable data foundation for subsequent real-time balance monitoring by eliminating equipment conflicts and process contradictions, significantly reducing the chain risks caused by initial plan imbalances.

[0073] Step S2: Collect actual progress data from the construction site, compare it with the progress benchmark sequence, and identify and calculate the progress time difference between symmetrical segments.

[0074] This step aims to accurately compare the dynamically changing actual progress data at the construction site with the symmetrical progress benchmark sequence Cseq established in step S1, thereby quantifying the imbalance between the left and right cantilever construction. The specific implementation process is as follows:

[0075] Step S2.1: Real-time progress data reading and processing.

[0076] The system collects raw progress records from multiple heterogeneous systems at the construction site through standardized data interfaces. These systems include an electronic signature platform for project supervision, a mobile log entry system for construction teams, and an IoT-based segment installation sensor network. Because the time record formats in the actual progress data collected by different systems vary, and the sensor network directly outputs Unix millisecond-level timestamps, the system first performs a unified processing of the time expressions. It identifies common time format patterns using a pre-built regular expression rule library and converts them all to the ISO 8601 standard format with time zone markings (e.g., "2025-09-01T14:30:00+08:00"), and then further converts them to millisecond-level timestamps for internal system processing. To ensure data reliability, the system simultaneously implements multi-layered data cleaning: automatically filtering incomplete records lacking key fields, such as segment identifiers or completion times; detecting time inconsistencies through a logical rule engine, such as completion times being earlier than start times or time reversals with adjacent segments; and reordering time sequences of records out of order due to network latency using a sliding time window sorting algorithm. Finally, all valid data are indexed by segment identifiers and constructed into a real-time progress matrix Rseg. Its matrix structure ensures that each segment corresponds to a row, and each row contains a strictly verified actual start timestamp and actual finish timestamp, thus establishing a high-quality data foundation for subsequent comparisons.

[0077] Step S2.2: Match actual time with planned time.

[0078] The core task of this stage is to precisely align the dynamically acquired actual progress with the static baseline plan in the time dimension. The system extracts comparison elements pairwise based on the symmetric progress baseline sequence Cseq generated in step S1. This sequence clearly defines the logical structure and planned time of all symmetric segment pairs in the full bridge. For each symmetric segment pair in the sequence, such as the third cantilever segment identified as Pair_L3R3, the system first retrieves the actual completion timestamps of the left segment L3 and the right segment R3 from the Rseg matrix. Simultaneously, it extracts the theoretical completion time of the same segment pair in the original plan from Cseq. It is worth noting that to ensure the consistency of the comparison time baseline, the system verifies whether the planned time and the actual time are in the same time reference frame. If a time zone or timing standard difference is found, a time baseline calibration procedure will be automatically triggered. The following four key timestamps—the start time of the left segment, the actual completion time of the left segment, the start time of the right segment, and the actual completion time of the right segment—are combined into a structured symmetric progress comparison unit, serving as the smallest data packet describing the equilibrium state of that segment pair. The comparison units of all segments of the entire bridge are assembled into a symmetrical progress comparison matrix Pcomp in spatial order (from the main tower to the cantilever end). This matrix is ​​not only the input carrier for the calculation of progress deviation, but can also generate a heat map of the overall balance status of the bridge through a visualization engine, providing intuitive decision support for engineering managers.

[0079] Step S2.3: Calculation of symmetrical progress difference.

[0080] After obtaining the precisely aligned alignment matrix, the system performs the core imbalance calculation. For each row in the Pcomp matrix (corresponding to a symmetrical segment pair), the schedule deviation is quantified using a specific difference formula: d k =(t left_actual -t left_plan )-(t right_actual -t right_plan ), where t left_actual -t left_plan t represents the absolute delay of the left segment (positive values ​​indicate delay, negative values ​​indicate advance), right_actual -t right_plan This represents the absolute delay of the right-hand segment. (d) k The unit is time (hours), when d k When d is positive, it indicates that the progress on the left side is lagging behind that on the right side (e.g., if the left side is delayed by 2 hours while the right side completes on time, then d). k =±2 hours); when d k When d is negative, it indicates that the progress on the right side lags behind that on the left side; when d k When d1 = 0, it indicates that the left and right progress are completely synchronized. The differences d1, d2, d3, ..., d2 between all symmetrical pairs are calculated. n The calculation results are encapsulated into a symmetrical deviation vector Dsym in segment pairs. Its data structure not only contains the original values ​​but also includes metadata identifying the calculation time point and data source version. This vector will be input into the mechanical risk analysis module in step S3. Its numerical sign (positive / negative) directly indicates the direction of imbalance, the absolute value reflects the severity of imbalance, and zero represents an ideal equilibrium state, forming a fully digital characterization of the balance of cantilever construction.

[0081] In the technical solution of this disclosure, a precise mapping between dynamic data from the construction site and a symmetrical reference sequence is achieved through multi-source heterogeneous system fusion and millisecond-level timestamp alignment technology. The system employs a multi-layer data cleaning mechanism to filter out incomplete records and timing inconsistencies, generating a high-confidence symmetrical progress deviation vector. By quantifying the direction and amplitude of the time difference between the left and right cantilever segments, a heat map of the overall bridge imbalance is formed, enabling project managers to intuitively grasp the imbalance state and providing real-time and accurate input for mechanical risk conversion, effectively addressing the shortcomings of traditional methods in terms of insufficient sensitivity to progress fluctuations.

[0082] Step S3: Based on the aforementioned time difference in progress, estimate the difference in additional mechanical effects caused by the asymmetry in construction progress at key parts of the bridge structure.

[0083] This step is a crucial mechanical transformation step in the bridge construction balance monitoring system. Its goal is to convert the schedule time difference vector Dsym output from step S2 into tower root bending moment differences, which have clear structural safety implications. Compared to existing schedule monitoring methods that only output time delay reports, this embodiment achieves real-time conversion from schedule deviation to bending moment difference. This conversion process establishes a quantitative correlation model between construction progress and structural mechanical response, providing a scientific basis for safety early warning in the construction of semi-floating bridge systems. The specific implementation process is as follows:

[0084] Step S3.1: Construction of the mechanical mapping support set.

[0085] Based on the bridge's construction drawings, structural calculations, and BIM model data, the system constructs a complete mechanical property description unit, namely the mechanical mapping support set Smap, for each symmetrical segment pair defined in step S1. First, the system extracts the cross-sectional geometry and material type of each segment from its design drawings and automatically calculates the self-weight load of each segment under construction conditions using the built-in material property database; this is the segment's equivalent gravity. Second, the system accurately reads the three-dimensional coordinates of the center of gravity of each segment from the BIM model and calculates the horizontal projection distance from its center of gravity to the reference plane for calculating the bending moment at the base of the main tower using a spatial geometric algorithm. This distance is the lever arm of the bending moment generated by the segment's load on the tower base. Finally, the system extracts the planned construction period (the difference between the planned start time and the planned completion time) for each segment pair from the symmetrical progress baseline sequence Cseq generated in step S1, using it as the benchmark for subsequent normalization of time deviations. All these parameters, including segmental equivalent gravity, lever arm, and construction cycle, are organized sequentially and associated with their corresponding symmetrical segment pair identifiers (such as Pair_L1R1), together forming a complete set of input parameters for subsequent moment difference estimation.

[0086] Step S3.2: Calculation of schedule deviation participation factor.

[0087] After obtaining the complete mechanical mapping support set Smap, the system begins the conversion from schedule deviation to mechanical participation factor. For each symmetrical segment pair, the system reads the time difference d calculated in step S2. k The ratio of this ratio to the planned construction period of the segment is then calculated to obtain a dimensionless relative schedule deviation ratio coefficient r. k The coefficient r k The physical meaning of this coefficient lies in its representation of the current actual schedule deviation relative to the entire planned construction period. To ensure the engineering rationality and numerical stability of the calculation results, the system uses this proportionality coefficient r. k Saturation limiting was applied: when r kWhen the absolute value is greater than 1, it indicates that the schedule deviation has exceeded one complete construction cycle, and the system limits it to -1 or 1. The value after this processing is the final schedule deviation participation factor e. k Its value range is strictly limited to the interval [-1, 1]. k A positive value indicates a delay on the left side of the schedule, while a negative value indicates a delay on the right side. The absolute value directly reflects the severity of the imbalance. k As a core conversion factor, it quantifies the weighting of time difference in the mechanical response, addressing the pain point of risk accumulation and amplification. The participation coefficient e of all segment pairs... k They are combined into a vector Epart, which serves as a key intermediate variable for crossing from the time domain to the mechanical domain.

[0088] Step S3.3: Calculation and correction of bending moment difference between single segments.

[0089] The system performs quantitative calculations of the bending moment difference at this stage. For each symmetrical segment pair, its original contribution value ΔM to the bending moment difference at the root of the main tower is calculated. k0 Based on the fundamental principles of mechanics of materials, the formula is: segmental equivalent gravity × lever arm × schedule deviation participation factor e. k The calculations yielded the following results. Subsequently, the system introduced a dual correction mechanism to improve estimation accuracy, making it more closely reflect the complex stress characteristics of the semi-floating system. The first correction is a segment type sensitivity correction, assigning different weighting coefficients α based on the importance of the segment's spatial location: for tower-beam segments directly acting on the main tower, due to their shortest force transmission path and most direct impact, a higher correction coefficient (1.2 to 1.5) is assigned; for standard cantilever segments, a baseline coefficient of 1.0 is used. The second correction is a construction stage boundary condition correction, using different boundary condition coefficients β to correct the moment transfer efficiency based on dynamic construction conditions such as whether the spherical support is in a temporary locked state and whether the viscous damper has been activated. Finally, the corrected moment difference contribution ΔM of this segment pair... k =ΔM k0 ×α×β. The moment difference contribution of all segments of the bridge is summarized to form the tower root moment difference vector Bmom. This vector is the final output of this step, which accurately quantifies the additional moment introduced at the root of the main tower due to the asymmetry of the construction progress.

[0090] Step S3.4: Validation and output of estimation results.

[0091] To ensure the reliability of the estimation results, the system compares and verifies the final generated tower root bending moment difference vector Bmom with the allowable bending moment value specified in the bridge design documents for the corresponding construction stage. This allowable bending moment value is a threshold that takes into account design parameters such as concrete safety factor and steel reinforcement strength. The system highlights segment pairs whose estimated bending moment differences are close to or exceed the allowable value and identifies them as high-risk units. All verified bending moment difference data, corresponding segment pair identifiers, calculation timestamps, and risk identifiers are encapsulated into a structured data package and output to step S4 for subsequent critical state determination and early warning analysis, thus forming a complete closed loop from data acquisition and mechanical calculation to risk early warning.

[0092] In the technical solution of this disclosure, a quantitative conversion model from schedule time difference to tower root bending moment difference is established based on the mechanical properties of the semi-floating system. By extracting the equivalent gravity, lever arm, and construction cycle parameters of the segments, and combining the dual correction of boundary conditions and segment sensitivity, the abstract schedule deviation is transformed into a bending moment difference with clear structural safety significance. This model accurately captures the additional bending moment transmission path under the flexible boundary of the spherical support, realizes the scientific assessment of the stress on the main tower root due to construction asymmetry, provides a mechanical core for risk classification and early warning, and breaks through the bottleneck of existing technologies that cannot quantify mechanical influence.

[0093] Step S4: Integrate the current state, trend and spatial distribution sensitivity of the additional mechanical effect difference to conduct a multi-dimensional risk coupling assessment, and generate graded early warning information based on the assessment results.

[0094] Step S4.1: Critical state determination.

[0095] This step is the decision-making stage for bridge construction safety early warning. It aims to accurately classify mechanical risks by dynamically comparing the measured values ​​of the tower root bending moment difference with structural safety thresholds. The system integrates design specifications, real-time monitoring data, and construction log events to construct a triple verification mechanism, ensuring the accuracy of critical state determination and the operability of the project. The specific implementation process is as follows:

[0096] Step S4.1.1: Allow value matrix construction and alignment.

[0097] The system first extracts key control parameters from the structural calculation report during the bridge construction phase. By parsing the allowable bending moment table for the construction phase in the design documents, which dynamically defines the upper limit of the tower root bending moment (Mallow) based on the cantilever advancement length of the main girder, the system establishes a mapping rule for allowable values ​​across all bridge segment pairs. This allowable value is derived from the safety factor calculation results of concrete compressive strength, steel reinforcement ratio, and temporary construction load combinations, and has a clear structural mechanics basis. For each symmetrical segment pair (e.g., Pair_L3R3) output in step S3, the system determines the corresponding cantilever length interval based on its spatial coordinates, and then retrieves the matching Mallow value from the allowable value mapping table. The retrieval results are organized into a structured allowable value set Hallow, where each element is associated with a specific segment pair identifier and an allowable bending moment value. Simultaneously, the system uses the measured tower root bending moment difference ΔM output in step S3. k Align each segment spatially with Hallow to generate an alignment verification matrix Acrit containing three elements: segment pair identifier, measured bending moment difference ΔM. k The design allowable value Mallowk is used. This matrix is ​​rendered as a hyperbola comparison chart of actual value and allowable value through the visualization engine of the digital twin platform, enabling engineering managers to intuitively understand the stress distribution of the entire bridge.

[0098] Step S4.1.2: Safety margin calculation and preliminary judgment.

[0099] After obtaining the precisely aligned parity check matrix, the system performs a security state quantification assessment. For each segment pair of cells in Acrit, the assessment is performed using the physical formula R. k =Mallowk-|ΔM k | Calculate the safety margin, which represents the remaining safety space above the current allowable upper limit of the bending moment difference. When R k When R > 0, it indicates that the structure is still in the safe zone; when R k When the value is less than 0, it indicates that the mechanical state has exceeded the design allowable range. Based on this calculation result, the system generates a preliminary judgment indicator: if the absolute value of the measured bending moment difference does not exceed the allowable value (i.e., |ΔM)... k |≤Mallowk), marked C k =0 (not critical state); if the measured value exceeds the allowable upper limit (i.e., |ΔM) k |>Mallowk), then mark C k =1 (critical state). All judgment results are integrated into the preliminary judgment result table Pjudge, whose data rows include five core fields: segment pair identifier, measured bending moment difference, allowable value, safety margin, and critical identifier. This table also records the historical decay trend of the safety margin, providing a baseline reference for subsequent trend analysis.

[0100] Step S4.1.3: Limit check and grade subdivision.

[0101] To distinguish between general overlimit and emergency risk states, the system loads the ultimate bearing capacity parameter library Hlimit in the bridge collapse simulation report. This library defines the fracture moment threshold Mlimitk for each segment pair, and its value is usually 1.5 to 2.2 times the allowable value, obtained by simulating through a finite element model under extreme working conditions (such as 3 segments lagging on one side). Based on the Pjudge table, the system implements a refined three-level critical state classification: when the absolute value of the measured moment difference is lower than the allowable value (|ΔM k |<Mallowk), it is marked as L k =0 (not critical); when it exceeds the allowable value but does not reach the fracture threshold (Mallowk<|ΔM k |≤Mlimitk), it is marked as L k =1 (critical state); when it breaks through the fracture threshold (|ΔM k |>Mlimitk), it is marked as L k =2 (ultimate critical state). The finally generated enhanced criterion table Lcrit contains seven-dimensional data: segment pair identification, measured value, allowable value, limit value, safety margin, critical identification, and critical level. The system synchronously retrieves interference events (such as strong wind alerts or equipment vibrations) in the construction log. When an associated interference source is detected for a segment pair with L k ≥1, a confidence annotation (such as instantaneous overlimit caused by strong wind) is automatically added to effectively distinguish real structural risks from accidental interference.

[0102] Step S4.1.4: Generation of the critical decision matrix.

[0103] Integrating all the above analysis data, the system generates the full-bridge critical state decision matrix Jcrit. The rows of this matrix record the complete risk profiles corresponding to each segment pair, and the column fields systematically organize seven core parameters, including segment pair identification, measured moment difference, allowable value, limit value, safety margin, critical identification, and critical level. The matrix data is pushed to the bridge digital twin platform in real time through the API interface, and a tower root risk heat map is rendered on the three-dimensional BIM model. The red area in the map identifies the segment pairs with L k =2, the orange identifies L k =1, and the green identifies L k =0. This visualization result enables the engineering team to quickly locate high-risk areas and provides decision-making targets for targeted reinforcement. The matrix data is also written into the distributed database to support historical state backtracking and multi-dimensional statistical analysis.

[0104] Step 4.2: Detection of trend continuity.

[0105] This step is crucial for dynamic risk perception within the bridge construction safety early warning system. It aims to effectively distinguish between short-term construction fluctuations and long-term structural risk trends by analyzing the evolution of the tower root bending moment difference over time. The specific implementation process is as follows:

[0106] Step S4.2.1: Time serialization and alignment.

[0107] The system first reconstructs the critical decision matrix Jcrit output in step 4.1 using a time series. For each symmetrical segment pair, the system extracts its complete state data for continuous monitoring times q = 1, 2, ..., Q, including the measured bending moment difference ΔM. k (q) Design allowable value Mallow k (q), Critical Level L k (q) and other parameters. A timestamp alignment engine transforms discrete sampling points into a continuous sequence with fixed intervals. For data loss due to network latency or equipment failure, the system uses a linear interpolation algorithm to automatically repair data based on valid values ​​before and after the data loss. Finally, a time-aligned sequence matrix Jseries is generated, where each row contains fields such as segment pair identifier, timestamp, moment difference, allowable value, and critical level, forming the spatiotemporal foundation dataset for trend analysis.

[0108] Step S4.2.2: Normalization and direction identifier extraction.

[0109] Based on time-series data, the system performs two key feature transformations. First, it calculates the normalization severity: for each segment pair at time q, using the formula... The bending moment difference is converted into a dimensionless index; a value greater than 1 indicates exceeding the limit, and a value less than 1 indicates safety. Next, the direction of imbalance is indicated: when ΔM... k When (q)≥0, mark Z. k (q) = +1 (left-side lag), when ΔM k When (q) < 0, mark Z. k (q) = -1 (right-hand lag). The above results are integrated into a normalized base matrix Nbase, whose data rows contain four elements: segment pairs, timestamps, severity, and orientation identifiers. This matrix is ​​updated in real time through a distributed storage engine, supporting millisecond-level time range retrieval.

[0110] Step S4.2.3: Window accumulation and dwell statistics.

[0111] The system employs a sliding time window mechanism (window length W = 6 time steps, time step = 4 hours) for dynamic trend aggregation. For each segment pair at time q, five types of core calculations are performed:

[0112] 1. Cumulative Severity within a Window: Calculates the sum of all severity levels within a window. This represents the total amount of recent imbalance;

[0113] 2. Number of consecutive steps exceeding the limit: The number of steps that continuously satisfy S at the end of the statistics window. k The number of time steps N with (h)≥1 k (q) reflects the duration of risk residence;

[0114] 3. Directional persistence ratio: The proportion of adjacent time points within the calculation window where the direction label remains consistent. Quantify the cumulative trend of one-sided lag;

[0115] 4. Minimum safety margin for the window: Captures the minimum safety margin within the window. Identify the most dangerous situation;

[0116] 5. Maximum Critical Level within a Window: Extracts the maximum critical level within a window. Mark whether a limit state has occurred.

[0117] All calculation results are encapsulated into a window statistics matrix Wstat, whose row structure includes seven types of fields: segment pair identifier, timestamp, cumulative severity, number of consecutive out-of-limit steps, directional persistence ratio, minimum window safety margin, and maximum window critical level.

[0118] Step S4.2.4: Generate the trend accumulation matrix.

[0119] The system performs multi-dimensional enhancement processing on the window statistics matrix. First, it calculates the derived metric: average window severity. Directional continuity intensity Among them, P k (q) represents the proportion of adjacent steps with consistent directional labels within the sliding window, quantifying the cumulative trend of one-sided lag. The directional consistency strength, with a value range of [0,1], quantifies the persistence of one-sided hysteresis. The average directional indicator value is set within the window. Then, construction phase metadata (such as wind speed and temperature) is loaded. When an external disturbance event is detected, a confidence decay marker is added to the corresponding timestamp. Finally, a trend accumulation matrix Tacc is generated, whose rows contain fields such as segment pairs, timestamps, cumulative severity, number of consecutive exceedances, directional persistence intensity, average severity, and disturbance markers. This matrix is ​​rendered as a dynamic heatmap using a digital twin platform. Red contour lines in the heatmap indicate high-cumulative-risk areas, providing spatial location and trend prediction for engineering interventions.

[0120] Step 4.3: Spatial Coupling Early Warning Generation.

[0121] This step is the final decision-making stage for bridge construction safety early warning. The specific implementation process is as follows:

[0122] Step S4.3.1: Construction of segment position weight matrix.

[0123] The system first defines spatial weight coefficients for each symmetrical segment based on the structural sensitivity zoning rules in the bridge design documents. The core principle is based on the mechanical properties of a semi-floating system: the closer a segment is to the main tower, the more significant the impact of construction imbalance on the tower root bending moment. In practice, the system extracts the three-dimensional spatial distance between the segment's centroid and the main tower root from the BIM model, and combines this with key area markers (such as the tower-beam connection area and the counterweight balance area) annotated by the structural engineer to generate an initial weight allocation scheme. For example, the tower-beam segment directly above the main tower (such as Pair_TL) directly transmits the load and is assigned the highest weight coefficient of 1.5-2.0; the counterweight area segment (such as Pair_BW1) which bears the counterweight function is assigned a medium weight of 1.3-1.6; and the standard cantilever segment takes the baseline weight of 1.0. After the weight assignment is completed, the system performs reverse verification using a finite element model: simulating single-segment lag conditions to verify the correlation between the weight coefficients and the tower root bending moment increment. The final output spatial weight matrix Wpos has a data structure that includes three fields: segment pair identifier, segment type, and calculated weight value, providing a geospatial basis for risk coupling.

[0124] Step S4.3.2: Calculation of risk coupling index.

[0125] The system integrates the trend cumulative matrix Tacc and the spatial weight matrix Wpos to perform a three-dimensional risk coupling calculation. First, spatial enhancement processing is performed: the window average severity U... k (q) and spatial weight W k Multiplication amplifies the impact of imbalances in key areas; secondly, it quantifies the duration over time: using the number of consecutive steps exceeding the limit, N. k (q) divided by the residency threshold H d (Station threshold H) d =0.3 × total construction steps (total construction steps refer to the total number of construction steps for all bridge segments), reflecting the duration of risk accumulation; finally, a directional penalty mechanism is introduced: the directional persistence intensity D is... k (q) Multiplied by the direction sensitivity coefficient θ (∈[0.5,1.0], default 0.8, calibrated via bridge BIM model) to reinforce the risk weight of unilateral lag. The three calculation results are expressed by the formula: The risk is integrated into a risk coupling index, with a higher value indicating a higher overall risk. ω1, ω2, and ω3 represent the spatial correlation weight, time cumulative weight, and direction-sensitive weight, respectively. This index is encapsulated as a risk coupling matrix Rcoup, supporting querying the full-bridge risk distribution heatmap by timestamp.

[0126] Step S4.3.3: Threshold mapping and classification determination.

[0127] Based on a historical accident case database and structural collapse simulation data, the system establishes a dynamic hierarchical threshold system. First, the mechanical parameters of typical accident scenarios are loaded, and an initial threshold is set based on expert experience: when S... k A yellow warning is issued when (q) < 1.2, indicating a slight imbalance; 1.2 ≤ S k When (q) < 1.8, an orange alert is marked, indicating a moderate risk; S k A red alert is triggered when (q) ≥ 2.4, indicating a structural emergency. The final output is a graded result matrix Ggrade, whose data rows contain four elements: timestamp, segment pair identifier, risk coupling value, and alert level. The alert color scale is rendered in real time through a digital twin platform.

[0128] Step S4.3.4: Full-bridge coupling early warning output.

[0129] The system aggregates the status of all segments to generate the final action command. The core decision-making rule is: when ≥1 red segment pair is detected, the entire bridge enters a red alert state, and construction on the lagging side is automatically suspended; when there are no red segments but the orange segment percentage is ≥20%, the entire bridge is marked with an orange warning, triggering the construction rhythm adjustment plan; otherwise, the bridge remains in a yellow monitoring state. The warning results are output synchronously through three channels:

[0130] 1. BIM Visualization: Render 3D warning light effects in the bridge model. Red segments display pulsed red light, orange displays amber light, and yellow displays light yellow outlines.

[0131] 2. Physical warning: LED warning lights in the tower crane cab at the construction site illuminate in the same color as the bridge's overall warning level;

[0132] 3. Instruction push: Automatically generate work orders containing reinforcement plans and push them to the mobile terminals of construction teams.

[0133] The final encapsulated warning result package Wbal contains a data structure including timestamps, full bridge level, detailed segment status table, and emergency response links, supporting historical backtracking and accountability.

[0134] The technical solution of this disclosure integrates critical state determination, trend continuity detection, and spatial sensitivity weighting to construct a spatiotemporally coupled active defense system. By analyzing the time accumulation effect and directional persistence of bending moment difference, short-term fluctuations and long-term structural risks are identified; combined with segmental spatial location weights and dynamic threshold grading, red / orange / yellow three-color early warning instructions are generated. The system synchronously triggers BIM three-dimensional light effect warnings, on-site physical signals, and reinforcement work orders, forming a closed-loop management from risk perception to engineering intervention, significantly improving the structural stability assurance capability of semi-floating bridges during cantilever construction.

[0135] In summary, addressing the shortcomings of existing technologies that isolate and monitor single-segment progress while neglecting left-right symmetry, this invention employs segmental symmetry pairing and dynamic baseline generation technology. By intelligently analyzing the construction plan and constructing a spatial equilibrium sequence, the system transforms discrete segmental information into a collaborative monitoring unit centered on the main tower, changing the traditional coarse-grained approach of simply recording time nodes in the schedule. This mechanism enables the construction team to perceive the collaborative status of the left and right cantilever arms in real time, fundamentally eliminating the risk of tower root moment imbalance caused by unilateral lag. Addressing the pain point of existing technologies' inability to quantify the impact of schedule deviations on structural mechanics, this invention establishes a precise conversion engine from time difference to moment difference. Based on the unique spherical support force transmission characteristics and viscous damper boundary conditions of the semi-floating system, the system achieves a scientific conversion of schedule asymmetry into additional tower root moment through parametric modeling of equivalent gravity, lever arm, and construction cycle time, upgrading the early warning mechanism from a simple time-delay alarm to a structural stress safety early warning. Addressing the systemic defects of misjudging short-term fluctuations and failing to alarm over long-term accumulation, this invention constructs a multi-dimensional risk coupling analysis framework. By integrating a triple mechanism of critical state determination, trend continuity detection, and spatial sensitivity weighting, the system can identify both sudden exceedances such as unilateral stagnation caused by equipment failure and gradual risks such as cumulative effects with lag. Combined with BIM 3D lighting warnings and construction command linkage, a closed-loop management system is formed from risk perception to engineering intervention.

[0136] In summary, this invention provides structured input for mechanical mapping through symmetrical monitoring, injects a physical core into risk analysis through the mechanical model, and achieves precise grading of early warnings through a spatiotemporal coupling mechanism. By linking progress monitoring, mechanical transformation, and risk decision-making, semi-floating bridge systems can proactively balance the contradiction between structural stress and construction efficiency during the cantilever construction phase. This avoids serious accidents such as main tower tilting and concrete cracking, and reduces construction delays through dynamic construction adjustments, fundamentally improving the safety assurance capabilities of major bridge projects throughout their entire lifecycle.

[0137] Figure 4 This is a schematic diagram of a bridge construction progress monitoring and early warning system according to an embodiment of this disclosure. The system is used to run the bridge construction progress monitoring and early warning methods described in the above embodiments. (Refer to...) Figure 4 The system may include:

[0138] Symmetrical schedule baseline building unit: configured to analyze the construction plan, using the axis of symmetry of the bridge structure as a reference, to establish a schedule baseline sequence for symmetrical construction segments;

[0139] Real-time progress comparison unit: configured to collect actual progress data at the construction site, compare it with the progress benchmark sequence, identify and calculate the progress time difference between symmetrical segments;

[0140] Mechanical effect conversion unit: configured to estimate the difference in additional mechanical effects caused by the asymmetry in construction progress at key parts of the bridge structure based on the aforementioned schedule time difference;

[0141] Multi-dimensional early warning decision unit: configured to integrate the current state, changing trend and spatial distribution sensitivity of the additional mechanical effect difference, perform multi-dimensional risk coupling assessment, and generate graded early warning information based on the assessment results.

[0142] According to embodiments of this disclosure, an electronic device is also provided, which may include a processor, a communications interface, a memory, and a communication bus, wherein the processor, the communications interface, and the memory communicate with each other via the communication bus. The processor can invoke logical instructions stored in the memory to execute the methods described above.

[0143] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, and can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this disclosure. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0144] On the other hand, this disclosure also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the methods provided by the above methods.

[0145] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0146] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0147] It should be understood that the above embodiments are only used to illustrate the technical solutions of this disclosure, and not to limit them; although this disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this disclosure.

Claims

1. A bridge construction progress monitoring and early warning method, characterized in that, The method comprises: Resolving the construction plan to establish a progress reference sequence for symmetrically constructed segments based on the symmetry axis of the bridge structure; Collecting actual progress data at the construction site and comparing it with the progress reference sequence to identify and calculate the progress time difference between symmetric segments; Based on the progress time difference, estimate the additional mechanical effect difference at the key parts of the bridge structure caused by asymmetric construction progress; Fusing the current state, change trend and spatial distribution sensitivity of the additional mechanical effect difference, performing multi-dimensional risk coupling assessment, and generating graded warning information according to the assessment results; The method further comprises: According to the results of the risk coupling assessment, output different levels of warning signals, and the warning signals at least include yellow warning, orange warning and red warning; According to the type of warning signal, trigger three-dimensional visual light effect rendering, on-site physical warning and construction instruction linkage response. The system comprises: ​ ​ 2. The bridge construction progress monitoring and early warning method according to claim 1, characterized in that, ​ ​ ​ ​ 3. The bridge construction progress monitoring and early warning method according to claim 2, characterized in that, ​ ​ ​ ​ 4. The bridge construction progress monitoring and early warning method according to claim 1, characterized in that, ​ ​ ​ ​ ​ ​ 5. The bridge construction progress monitoring and early warning method according to claim 4, characterized in that, ​ ​ ​ 6. A bridge construction progress monitoring and early warning system, characterized in that, ​ The symmetric progress reference construction unit is configured to analyze the construction plan to establish a progress reference sequence for symmetrically constructed segments based on the symmetry axis of the bridge structure; The real-time progress comparison unit is configured to collect actual progress data of the construction site, compare the actual progress data with the progress reference sequence, and identify and calculate the progress time difference between the symmetric segments; The mechanical effect conversion unit is configured to estimate the additional mechanical effect difference at the key positions of the bridge structure caused by the asymmetric construction progress based on the progress time difference; The multi-dimensional early warning decision unit is configured to fuse the current state, change trend and spatial distribution sensitivity of the additional mechanical effect difference, perform multi-dimensional risk coupling assessment, and generate graded early warning information according to the assessment result; The estimation of the additional mechanical effect difference at the key positions of the bridge structure caused by the asymmetric construction progress based on the progress time difference includes: Mapping the progress time difference to a progress deviation participation coefficient; Calculating the bending moment difference of the key positions of the bridge based on the participation coefficient, segment equivalent gravity and force arm parameters; The calculation of the bending moment difference of the key positions of the bridge further includes: Correcting the bending moment difference according to the importance weight coefficient of the spatial position of the segment; According to the locking state of the spherical support and the activation state of the viscous damper, the bending moment difference is corrected by using the structure boundary condition coefficient.

7. An electronic device, comprising: The electronic device includes a memory and at least one processor, the memory stores a computer program, and the processor is used to execute the computer program to implement the bridge construction progress monitoring and early warning method of any one of claims 1-5.

8. A computer storage medium, characterized in that, It stores a computer program, and the computer program is executed to implement the bridge construction progress monitoring and early warning method according to any one of claims 1-5.

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