Incremental Process Research Articles

Statistical inference for lindley random walks with correlated increments

This paper studies statistical inference for a Lindley random walk model when the increment process driving the walk is strictly stationary. Lindley random walks govern customer waiting times in many queueing models and several natural and business processes, including snow depths, frozen soil depths, inventory quantities, etc. The probabilistic properties of a Lindley walk with time-correlated stationary changes are first reviewed. We provide a streamlined argument that the process has a proper limiting distribution when the mean of the incremental changes is negative, and that the Lindley process is strictly stationary when starting from this stationary distribution. Next, the Markov characteristics of the process are explored when the change process has a Markov structure of first or higher order. A derivation of the model's likelihood is given when the change process is a Gaussian autoregressive time series. An efficient particle filtering method for evaluating and optimizing the likelihood with Gaussian changes is then devised and studied via simulation.

A plan to guide rather than to master: the urbanism of negotiation on the Île de Nantes

ABSTRACT Over the last three decades, in France as in other European countries, urban projects have given increasing importance to negotiations between public and private players, and to iterative and incremental design processes. Among the tools implemented by architects and other professionals in France, the plan-guide has progressively challenged the master plan as uncertainty about the means and the ends of projects grew. To understand the rationale of the plan-guide, it is useful to look at the way it was first deployed as the main working tool of Île de Nantes urban renewal project in the early 2000s. That case study sheds light on how urban projects relying on plan-guides became instrumental within neoliberal agendas.

Research on the Theoretical Models of FRP-Confined Gangue Aggregate Concrete Partially Filled Steel Tube Columns

FRP-confined gangue aggregate concrete partially filled steel tubes (CGCPFTs) can not only effectively enhance the performance of coal gangue concrete, but also fully exploit the elastic-plastic mechanical behavior of the steel tubes. However, research on theoretical models that can describe their mechanical properties is yet to be conducted. To fill this gap, theoretical models for structural design and analysis were proposed for CGCPFTs. For the analytical model, based on the available experimental data, a prediction method for the stress–strain behavior of the gangue aggregate concrete in CGCPFTs, which is confined only by FRP and partly confined by both FRP and the steel tubes, was first proposed. Additionally, the condition for the synergetic deformation of the two confined states of gangue aggregate concrete within the CGCPFT was proposed. Based on the condition, an iterative incremental process was developed which subsequently allows for the theoretical calculation of the load–displacement curve for the CGCPFT under monotonic axial compression. For the design model, by introducing the constraint contribution coefficient of the steel tube, the existing closed-loop calculation formula for the stress–strain relationship of FRP-confined concrete was revised. Furthermore, by expressing the axial and lateral stresses of the steel tube as a unified circumferential effect on the concrete, the calculation methods for the ultimate strength and strain in the closed-loop formula were redefined, thus achieving the prediction of the stress–strain behavior of CGCPFTs. The comparison with the test data obtained by the author and their team revealed that both the analysis and design models could provide accurate predictions.

The coupling effect of calcined layered double hydroxides and impressed current cathodic protection system on chloride ion binding and migration of sea sand mortar

Chloride introduced by sea sand and sea water leads to steel bar corrosion. Calcined Layered double hydroxides (CLDH) and impressed current cathodic protection (ICCP) system were used to adsorb chloride ion and control steel bar corrosion in chloride abundant environment. In this research, chloride binding effect of two kinds of CLDH were investigated in simulated concrete pore solution. Besides, chloride absorption capacity and steel bar corrosion resistance of Mg-Al CLDH was explored in mortar. Further, hydration degree of mortar installed ICCP system and migration of ions around the anode and cathode were detected. Results show that Mg-Al CLDH exhibits excellent chloride binding capacity and corrosion resistance. After installing the ICCP system, corrosion of steel bars in mortar mixed Mg-Al CLDH was controlled. And ions in mortar specimen around anode or cathode exhibits migration phenomenon in different degree. A two stage-four step model was established to explain the degradation of hydration products and ion migration in CLDH-ICCP system. Under the ICCP system, Cl− in pore solution increased at early stage and then decreased both around anode and cathode, the increment process was controlled by desorption of Mg-Al CLDH and decreased process was controlled by degradation of hydration products. Therefore, the CLDH-ICCP system was recommended to solve the problem of steel bar corrosion introduced by marine environment.

Public Health Challenges for 2050 in the Netherlands

Abstract Every 4 years, the National Institute for Public Health and the Environment (RIVM) carries out the Dutch Public Health Foresight Study, commissioned by the Ministry of Health. The ninth edition (PHFS-2024) compiles an outlook on future public health and the health care system in the Netherlands. It combines a business-as-usual Trend Scenario with literature study and a range of qualitative and mixed-methods approaches. The Trend Scenario comprises projections up to 2050 for a broad range of epidemiological indicators. Such as (healthy) life expectancy, disease burden, life style factors. The qualitative and mixed methods approaches supported an incremental intersubjective process. Methods were a focus groups study, stakeholder sessions and workshops. An important innovation in this study was the Citizen Council and Panel. The outcomes show that demographic development, in particular growth of the group people at high age with multimorbidity, will lead to high pressure on the formal and informal health care system but also on e.g. mental health. A life course approach applied shows that decreasing mental health of youth will have long term consequences. Also, overweight will deeply impact older people’s health in the future. Lastly, climate change in combination with e.g. energy transition will deeply affect our society and living environment. Next to these challenges, there will also be opportunities to improve health. Building a climate-proof living environment that also stimulates healthy life styles and social interaction is one of them. However, improving health has linkages with policy areas outside the traditional public health domain as well and requires, firstly, a Health in/for All Policies approach. Secondly a whole-of-society approach in which companies and civil organizations take action is needed. All these efforts can only be effective if people and communities are put at the core of all health policies.

Beam Local Stress Prediction Model for Incremental Launching Construction Based on SSA-SVR Algorithm

In the multi-point incremental launching construction process, without setting temporary piers at the mid-span, it is difficult to ensure the synchronization of the launching equipment at the same pier, which can easily cause the beam to deviate from the design axis, leading to changes in the stress of the beam. Calculating the local maximum stress of the beam when lateral deviation occurs is beneficial for establishing a reasonable and reliable threshold for controlling beam deviations. To address this issue, this paper utilizes a finite element model of the bridge under typical adverse conditions, simulating the lateral deviation and asynchronous jacking of the beam to explore stress variation patterns. The results show that the beam stress increases as the lateral deviation and jacking differences increase, severely affecting the structural safety. To overcome the inefficiency of the finite element modeling method when dealing with multiple working conditions and to establish a reasonable deviation control threshold, this study proposes a beam local stress prediction model under the combined influence of lateral deviation and jacking height differences based on the SSA-SVR algorithm. The model calculates the local maximum stress of the beam for 2500 sets of parameters. An engineering case analysis shows that the prediction model has high calculation accuracy, reliable results, fast computational speed, and high efficiency. Based on the stress prediction results, it is recommended to set the jacking height difference control threshold at 20 mm. When the jacking height difference is less than 20 mm, the local maximum stress value under the combined influence of both parameters should be less than the standard limit. The corresponding lateral deviation control threshold can be dynamically adjusted according to the actual travel difference recorded by the jacking equipment, with the control precision improved to 1 mm.

FaSS-MVS: Fast Multi-View Stereo with Surface-Aware Semi-Global Matching from UAV-Borne Monocular Imagery.

With FaSS-MVS, we present a fast, surface-aware semi-global optimization approach for multi-view stereo that allows for rapid depth and normal map estimation from monocular aerial video data captured by unmanned aerial vehicles (UAVs). The data estimated by FaSS-MVS, in turn, facilitate online 3D mapping, meaning that a 3D map of the scene is immediately and incrementally generated as the image data are acquired or being received. FaSS-MVS is composed of a hierarchical processing scheme in which depth and normal data, as well as corresponding confidence scores, are estimated in a coarse-to-fine manner, allowing efficient processing of large scene depths, such as those inherent in oblique images acquired by UAVs flying at low altitudes. The actual depth estimation uses a plane-sweep algorithm for dense multi-image matching to produce depth hypotheses from which the actual depth map is extracted by means of a surface-aware semi-global optimization, reducing the fronto-parallel bias of Semi-Global Matching (SGM). Given the estimated depth map, the pixel-wise surface normal information is then computed by reprojecting the depth map into a point cloud and computing the normal vectors within a confined local neighborhood. In a thorough quantitative and ablative study, we show that the accuracy of the 3D information computed by FaSS-MVS is close to that of state-of-the-art offline multi-view stereo approaches, with the error not even an order of magnitude higher than that of COLMAP. At the same time, however, the average runtime of FaSS-MVS for estimating a single depth and normal map is less than 14% of that of COLMAP, allowing us to perform online and incremental processing of full HD images at 1-2 Hz.

Grammar and Expectation in Active Dependency Resolution: Experimental and Modeling Evidence From Norwegian.

Filler-gap dependency resolution is often characterized as an active process. We probed the mechanisms that determine where and why comprehenders posit gaps during incremental processing using Norwegian as our test language. First, we investigated why active filler-gap dependency resolution is suspended inside island domains like embedded questions in some languages. Processing-based accounts hold that resource limitations prevent gap-filling in embedded questions across languages, while grammar-based accounts predict that active gap-filling is only blocked in languages where embedded questions are grammatical islands. In a self-paced reading study, we find that Norwegian participants exhibit filled-gap effects inside embedded questions, which are not islands in the language. The findings are consistent with grammar-based, but not processing, accounts. Second, we asked if active filler-gap processing can be understood as a special case of probabilistic ambiguity resolution within an expectation-based framework. To do so, we tested whether word-by-word surprisal values from a neural language model could predict the location and magnitude of filled-gap effects in our behavioral data. We find that surprisal accurately tracks the location of filled-gap effects but severely underestimates their magnitude. This suggests either that mechanisms above and beyond probabilistic ambiguity resolution are required to fully explain active gap-filling behavior or that surprisal values derived from long-short term memory are not good proxies for humans' incremental expectations during filler-gapresolution.

The unique contribution of uncertainty reduction during naturalistic language comprehension

Language comprehension is an incremental process with prediction. Delineating various mental states during such a process is critical to understanding the relationship between human cognition and the properties of language. Entropy reduction, which indicates the dynamic decrease of uncertainty as language input unfolds, has been recognized as effective in predicting neural responses during comprehension. According to the entropy reduction hypothesis (Hale, 2006), entropy reduction is related to the processing difficulty of a word, the effect of which may overlap with other well-documented information-theoretical metrics such as surprisal or next-word entropy. However, the processing difficulty was often confused with the information conveyed by a word, especially lacking neural differentiation. We propose that entropy reduction represents the cognitive neural process of information gain that can be dissociated from processing difficulty. This study characterized various information-theoretical metrics using GPT-2 and identified the unique effects of entropy reduction in predicting fMRI time series acquired during language comprehension. In addition to the effects of surprisal and entropy, entropy reduction was associated with activations in the left inferior frontal gyrus, bilateral ventromedial prefrontal cortex, insula, thalamus, basal ganglia, and middle cingulate cortex. The reduction of uncertainty, rather than its fluctuation, proved to be an effective factor in modeling neural responses. The neural substrates underlying the reduction in uncertainty might imply the brain’s desire for information regardless of processing difficulty.

Creative Teaching in the Context of Educational Reform: Insights from the Perspective of In-service Teachers

Aim. This study aims to elucidate the multifaceted nature of creative teaching within the specific context of educational reform by exploring the nuanced perspectives of in-service teachers through their real-life teaching experiences. Methods. A qualitative approach was adopted, with semi-structured, vignette-based interviews with 19 teachers from elementary, junior high, and senior high schools in Taiwan. Data analysis followed reflexive thematic analysis methods. Results. The findings underscore that in-service teachers perceive creative teaching not as the creation of entirely new educational paradigms but rather as the adoption of unconventional methods. This entails the adaptive and strategic utilisation of diverse teaching methods, the integration of multidisciplinary content, and an enduring commitment to continuous improvement. It is an incremental process involving the continuous absorption of new concepts, the adaptation of new methods, and making progressive adjustments. Conclusion. Enhancing teachers’ creative teaching requires a context-specific understanding of its essence, facilitating targeted promotion and teacher education. This study addresses the research gap by qualitatively investigating teachers’ perspectives on creative teaching and its implementation. The insights gained in this study are crucial in guiding the effective integration of creativity into pedagogical practice within teacher education and professional development endeavours. Cognitive value. This study challenges the conventional notion that creative teaching involves the invention of entirely new methods, arguing instead that creative teaching is primarily concerned with the ways in which teachers implement unconventional approaches, rather than the novelty of those approaches. These findings offer a novel perspective on fostering creative teaching within teacher development endeavours.

Consonant lengthening marks the beginning of words across a diverse sample of languages.

Speech consists of a continuous stream of acoustic signals, yet humans can segment words and other constituents from each other with astonishing precision. The acoustic properties that support this process are not well understood and remain understudied for the vast majority of the world's languages, in particular regarding their potential variation. Here we report cross-linguistic evidence for the lengthening of word-initial consonants across a typologically diverse sample of 51 languages. Using Bayesian multilevel regression, we find that on average, word-initial consonants are about 13 ms longer than word-medial consonants. The cross-linguistic distribution of the effect indicates that despite individual differences in the phonology of the sampled languages, the lengthening of word-initial consonants is a widespread strategy to mark the onset of words in the continuous acoustic signal of human speech. These findings may be crucial for a better understanding of the incremental processing of speech and speech segmentation.

For preschoolers, word knowledge falls on a continuum: A novel framework for capturing the incremental process of word learning

For preschoolers, word knowledge falls on a continuum: A novel framework for capturing the incremental process of word learning

Enhancing Low-Light Medical Imaging through Deep Learning-Based Noise Reduction Techniques

Background/ Objectives: Low-light medical imaging is highly challenging in clinical diagnostics due to increased noise levels that mask or obscure important anatomical details. In this respect, conventional noise reduction methods such as Gaussian filtering and median filtering usually lead to a trade-off between noise suppression and the preservation of important features in an image, thus resulting in poor-quality images. More advanced wavelet-based denoising and Non-Local Means methods exhibit superior noise reduction but remain computationally intensive and introduce artifacts. These challenges come with a need to develop more effective and efficient noise-reduction techniques. Methods: This study proposes an end-to-end deep learning framework for low-light medical image enhancement. We present a comprehensive deep-learning framework to enhance low-light medical images by integrating Convolutional Neural Networks with denoising autoencoders to build a robust noise reduction model. The CNN extracts the feature from the noisy input images, while the autoencoder does so for the reconstruction of clean images through the encoding of a noisy input in a lower-dimensional representation for the reduction of noise while retaining critical information. Findings: This study validates the proposed model through rigorous quantitative metrics such as peak signal-to-noise ratio and structural similarity index. These metrics are designed to provide a full assessment of image quality concerning noise reduction capability and preservation of details related to structure. Our model improves traditional methods in PSNR by about 5 dB on average and SSIM by 0.15, which means better noise reduction and preservation of image details. A comparative analysis of traditional techniques for noise reduction has been included, pointing out the advantages of deep learning approaches. Experimental results depict significant improvements over previous approaches. For instance, the proposed model reduces the noise level by up to 40% and facilitates clear and sharp images by up to 30%. In terms of quantification, these improvements manifest in a PSNR value of 35 dB and an SSIM score of 0.85 compared to 30 dB and 0.70 using traditional techniques. Furthermore, the study illustrates the training dynamics, feature maps, and evolution of images to present the model's incremental learning process. Novelty: This study's findings validate the proposed model's efficacy in enhancing diagnosis accuracy and improving patient outcomes in medical imaging. Keywords: Low-light medical imaging, Noise reduction, Convolutional Neural Networks, Denoising autoencoders, Medical diagnostics

QMWT: Design of an Improved EEG Classification Model via Q-Learning Based Processing of Multispectral Wave Traits

Electroencephalogram (EEG) signals represent various wave patterns that assist in identification of normal & abnormal brain activities. These wave patterns consist of alpha waves, which indicate relaxation phases, beta waves, that indicate normal brain rhythms & can be disturbed due to cortical & other damages, delta waves, which are dominant in infants during sleep, & theta waves, which represent irregular metabolic & hydrocephalus activities in adults. A combination of these waves is capable of representing multiple brain conditions for both adolescents & adults. Various models have been proposed by researchers to analyze these signals, and most of them work on single or bi-domain features, which limits their classification performance. Most of these models are also static, and do not incorporate continuous feedback & incremental processes. Due to which their precision & recall performance is either constant or reduces w.r.t. newer evaluations. To overcome these limitations, this text proposes design of a novel EEG classification model that uses Q-Learning for classification of multispectral feature sets. The model extracts Mel Frequency Cepstral Coefficients (MFCC), and iVector features from raw EEG data, which assists in multispectral representation of these signals. The extracted features are classified via Q-Learning based Recurrent Neural Network (RNN) classifier, that combines Gated Recurrent Unit (GRU), and Long-Short-Term Memory (LSTM) based feature sets. Due to extraction of MFCC, the GRU & LSTM Models are able to identify power spectral variation, cepstral variations, spectrogram patterns, DCT (Discrete Cosine Transform) variations, etc. While, due to iVector, entropy variations are recognized and processed for better accuracy levels. Thus, both LSTM & GRU Models assist in augmenting extracted features, which improves feature variance for better classification performance. Results of these classifications are feedback into the training set via a correlation-based analysis layer, that assists in continuously improving precision & recall performance for different evaluations. Due to incorporation of this layer, the model is capable of improving precision by 8.5%, recall by 8.3%, and accuracy by 4.9% when compared with various state-of-the-art models. It was also observed that this performance incrementally improves w.r.t. number of evaluations, which assists in deploying the model for real-time applications.

A novel meshless radial basis reproducing kernel particle approach for 3D thermo-mechanical analysis of mushy zone phase-change problems

This paper presents a three-dimensional thermo-elastoplastic computational model for the analysis of phase change problems involving the mushy zone. The model utilizes a novel meshless radial basis reproducing kernel particle approach, incorporating a high-order kernel that integrates Gaussian and cosine functions. An energy balance equation for the entire domain is formulated using the effective heat capacity method, based on the enthalpy formulation. The governing equations are discretized using the Galerkin weak form to compute thermal residual stresses, with essential boundary conditions enforced through the penalty method. Plastic deformation is characterized using the von Mises criterion with isotropic hardening, and the strain term resulting from temperature-dependent material properties is incorporated into the incremental solution process. Determination of the plastic strain increment is based on the Prandtl–Reuss flow rule. The accuracy and efficiency of the proposed meshless approach were rigorously evaluated through numerical examples encompassing two- and three-dimensional problems. The computational results were compared against available analytical and validated numerical solutions. This comprehensive assessment substantiated the robustness and precision of the developed technique. The case studies presented elucidate the capacity of the novel meshless model to effectively predict temperature distributions and residual stress fields within mushy zone phase change phenomena, accommodating both regular and irregular geometries under various boundary conditions. Notably, the model demonstrated consistent stability and high accuracy across the spectrum of simulated scenarios, thus underscoring its potential as a valuable tool for investigating complex phase change processes.

Software modernization powered by dynamic language product lines

Legacy software poses a critical challenge for organizations due to the costs of maintaining and modernizing outdated systems, as well as the scarcity of experts in aging programming languages. The issue extends beyond commercial applications, affecting public administration, as exemplified by the urgent need for COBOL programmers during the COVID-19 pandemic. In response, this work introduces a modernization approach based on dynamic language product lines, a subset of dynamic software product lines. This approach leverages open language implementations and dynamically generated micro-languages for the incremental migration of legacy systems to modern technologies. The language can be reconfigured at runtime to adapt to the execution of either legacy or modern code, and to generate a compatibility layer between the data types handled by the two languages. Through this process, the costs of modernizing legacy systems can be spread across several iterations, as developers can replace legacy code incrementally, with legacy and modern code coexisting until a complete refactoring is possible. By moving the overhead of making legacy and modern features work together in a hybrid system from the system implementation to the language implementation, the quality of the system itself does not degrade due to the introduction of glue code. To demonstrate the practical applicability of this approach, we present a case study on a COBOL system migration to Java. Using the Neverlang language workbench to create modular and reconfigurable language implementations, both the COBOL interpreter and the application evolve to spread the development effort across several iterations. Through this study, this work presents a viable solution for organizations dealing with the complexity of modernizing legacy software to contemporary technologies. The contributions of this work are (i) a language-oriented, incremental refactoring process for legacy systems, (ii) a concrete application of open language implementations, and (iii) a general template for the implementation of interoperability between languages in hybrid systems.

PENINGKATAN KUALITAS HUNIAN PADA PERMUKIMAN PADAT DI RW 02, KELURAHAN KRENDANG, JAKARTA BARAT

The settlement in RW 02, Krendang sub-district, Tambora district, West Jakarta, is a dense and unorganized urban settlement resulting from an unplanned incremental growth process. Apart from poor environmental sanitation conditions, this settlement has a low quality of residential space with very limited natural lighting and ventilation due to the closeness of the buildings and the lack of open space. This settlement is the location for implementing a community service program which aims to help the West Jakarta government deal with the city's slum problem by involving community participation to improve the quality of residential space. The initial program in 2023 aims to open people's minds about the importance of healthy residential spaces, and to make this happen can be done using simple, low-cost methods. In the next stage, sustainable programs will be continued until a model of healthy, low-cost housing is realized in this settlement. A series of community service activities have been carried out with the culmination of counseling activities for social treatment to build collective insight and awareness among residents. The counseling took place at the community hall, attended by RW administrators as well as RT representatives and community leaders within RW 02. The counselor team used pictures of simulation designs for the renovation of multi-purpose buildings and pre-school children's education in RW 02 as counseling aids. Based on pre-test and post-test data through filling out questionnaires by participants, it shows a significant increase in collective awareness regarding the program objectives.

Deep Quasi-Recurrent Self-Attention with Dual Encoder-Decoder in Biomedical CT Image Segmentation.

Developing deep learning models for accurate segmentation of biomedical CT images is challenging due to their complex structures, anatomy variations, noise, and unavailability of sufficient labeled data to train the models. There are many models in the literature, but the researchers are yet to be satisfied with their performance in analyzing biomedical Computed Tomography (CT) images. In this article, we pioneer a deep quasi-recurrent self-attention structure that works with a dual encoder-decoder. The proposed novel deep quasi-recurrent self-attention architecture evokes parameter reuse capability that offers consistency in learning and quick convergence of the model. Furthermore, the quasi-recurrent structure leverages the features acquired from the previous time points and elevates the segmentation quality. The model also efficiently addresses long-range dependencies through a selective focus on contextual information and hierarchical representation. Moreover, the dynamic and adaptive operation, incremental and efficient information processing of the deep quasi-recurrent self-attention structure leads to improved generalization across different scales and levels of abstraction. Along with the model, we innovate a new training strategy that fits with the proposed deep quasi-recurrent self-attention architecture. The model performance is evaluated on various publicly available CT scan datasets and compared with state-of-the-art models. The result shows that the proposed model outperforms them in segmentation quality and training speed. The model can assist physicians in improving the accuracy of medical diagnoses.

Intelligent Agents for Data Exploration

Data Exploration is an incremental process that helps users express what they want through a conversation with the data. Reinforcement Learning (RL) is one of the most notable approaches to automate data exploration and several solutions have been proposed. We first summarize some RL solutions that were built for different applications. In this context, various data exploration operators are leveraged including traditional roll-up and drill-down operations and text-based operations. An RL agent is trained to generate the best policy according to a hand-crafted reward function. The benefit of training RL policies for specific data exploration tasks has been demonstrated more than once for exploring finding a needle in a haystack, for serendipitous galaxy exploration, for helping a customer land on a satisfactory product, for helping a conference chair build a program committee in a stepwise fashion, for summarizing large datasets, etc. With the advent of Large Language Models and their ability to reason sequentially, it has become legitimate to ask the question: would LLMs and AI planning outperform an RL policy in data exploration? More specifically, would LLMs help circumvent retraining for new tasks and striking a balance between specificity and generality? This led us to designing LLM-powered approaches that introduce a new way of thinking about data exploration.

Local Deformation Analysis and Optimization of Steel Box Girder during Incremental Launching

Due to the significant weight of the steel box girder and the substantial gap between temporary piers, the stress concentration and localized deformation of the girder’s bottom plate are pronounced during the incremental launching construction process. In this study, the multi-scale finite element method was employed to simulate the incremental launching process of the steel box girder, analyze the pattern of localized deformation in the bottom plate contact area, and propose corresponding optimization control measures. Additionally, the entire incremental launching process was monitored, and the improvement in localized deformation before and after the optimization of the incremental launching scheme was analyzed. The results illustrate that the original incremental launching scheme led to substantial localized deformation in the bottom plate contact area of the steel box girder. Optimal control measures were implemented for the four incremental launching construction schemes. The optimal scheme significantly mitigated the localized deformation of the steel box girder’s bottom plate, with an average decrease in deformation of 48.32%. These findings can provide robust technical guidance and data support for the control of localized deformation in large-span steel box girders during the incremental launching construction process.