Projection Data Research Articles

The innovation of engineering project management in the context of big data

ABSTRACT With the development of China’s economy and society, the society continues to move forward. Related to this, the scale of China’s engineering construction is also increasing. With the development of the times and the continuous growth of demand, the complexity and specialisation of engineering projects continue to improve. This has put forward more stringent requirements for the construction of engineering projects. As the traditional engineering project construction management in China, the innovation is mainly through the practice of construction projects and the research of the engineering problems that arise. So, in today’s environment of big data background, the innovation of construction project management is to combine with the actual situation of construction projects, from the construction characteristics of the project, using a variety of advanced scientific technology, for the project data collection, calculation and storage applications and other aspects of optimisation and innovation, so that more convenient to achieve The modern construction engineering project management innovation work of diversified needs, for the final completion of the entire construction project can be expected to provide a strong guarantee and support the comprehensive benefits of the goal. The results show that our innovative scheme outperforms other comparative methods and achieves a 23% improvement in project management efficiency.

Simulation on Image Reconstruction of Concrete with Rebars by Filtered Back Projection Algorithm using PHITS and MATLAB Program

This paper describes simulation on image reconstruction of concrete with rebars by filtered back projection algorithm using PHITS and MATLAB program. In PHITS simulation, a source of Cs-137 and NaI (Tl) detector were used. Concrete block with one rebar was carried out with PHITS simulation. The sample of another concrete block with two rebars with different diameters was also conducted. Linear attenuation coefficient values of the concrete were calculated. These phantoms were rotated with 2.8125 degree intervals over 0 to 180° in PHITS simulation. The objects were scanned with the parallel beam method and the projection data were obtained. The data was built into the image using filtered back projection (FBP) algorithm. The ‘spline’ interpolation and ‘Hamming’ filter functions were chosen for the clearest image in image reconstruction. The results indicated that the reconstructed images of the concrete containing one rebar and two rebars can be observed clearly in MATLAB.

From code sharing to sharing of implementations: Advancing reproducible AI development for medical imaging through federated testing

BackgroundThe reproducibility crisis in AI research remains a significant concern. While code sharing has been acknowledged as a step toward addressing this issue, our focus extends beyond this paradigm. In this work, we explore “federated testing” as an avenue for advancing reproducible AI research and development especially in medical imaging. Unlike federated learning, where a model is developed and refined on data from different centers, federated testing involves models developed by one team being deployed and evaluated by others, addressing reproducibility across various implementations. MethodsOur study follows an exploratory design aimed at systematically evaluating the sources of discrepancies in shared model execution for medical imaging and outputs on the same input data, independent of generalizability analysis. We distributed the same model code to multiple independent centers, monitoring execution in different runtime environments while considering various real-world scenarios for pre- and post-processing steps. We analyzed deployment infrastructure by comparing the impact of different computational resources (GPU vs. CPU) on model performance. To assess federated testing in AI models for medical imaging, we performed a comparative evaluation across different centers, each with distinct pre- and post-processing steps and deployment environments, specifically targeting AI-driven positron emission tomography (PET) imaging segmentation. More specifically, we studied federated testing for an AI-based model for surrogate total metabolic tumor volume (sTMTV) segmentation in PET imaging: the AI algorithm, trained on maximum intensity projection (MIP) data, segments lymphoma regions and estimates sTMTV. ResultsOur study reveals that relying solely on open-source code sharing does not guarantee reproducible results due to variations in code execution, runtime environments, and incomplete input specifications. Deploying the segmentation model on local and virtual GPUs compared to using Docker containers showed no effect on reproducibility. However, significant sources of variability were found in data preparation and pre-/post- processing techniques for PET imaging. These findings underscore the limitations of code sharing alone in achieving consistent and accurate results in federated testing. ConclusionAchieving consistently precise results in federated testing requires more than just sharing models through open-source code. Comprehensive pipeline sharing, including pre- and post-processing steps, is essential. Cloud-based platforms that automate these processes can streamline AI model testing across diverse locations. Standardizing protocols and sharing complete pipelines can significantly enhance the robustness and reproducibility of AI models.

Research on the stability of loess landslides under seismic action based on simplified bishop method

Assessing the stability of loess landslides under seismic action scientifically and reasonably is crucial for reducing earthquake disasters and ensuring the safety of people’s lives and property. To study this issue, we chose the southern part of Fushan County, Shanxi Province as the research area, where loess slopes develop and earthquake impacts are strong. Based on the high-precision DEM images of the study area obtained from the unmanned aerial vehicle photogrammetry, we selected 32 representative profiles by sampling according to our principles, and used the simplified Bishop method popular in engineering for modeling and calculation to evaluate the stability of the entire slopes in the area. The calculation results were then statistically analyzed, and the stability and impact range of the slopes were evaluated by means of the graphical form. The results show that in the study area the value of slope safety coefficient (Fs) is mostly within a range of 0.7–1.35 and the value of the avoidance distance is mostly within a range of 5–20 m under a seismic force of 0.20 g, and the slope safety coefficient is mainly determined by the slope angle, and the avoidance distance is mainly determined by the slope height, which is in line with the conclusions of previous research conclusions. In the paper we make a useful attempt for quantitative evaluation of regional seismic loess landslides, and the fact proves that the approach is feasible and efficient, and it can provide quantitative data for major construction projects to avoid landslide disasters.

Molecular Clouds in the Outer Milky Way Disk: Sample, Integrated Properties, and Radial Trends with Galactocentric Radius

We present a catalog of 32,162 12CO molecular clouds that covers the northern outer Galactic plane (l = [15°, 165°] and l = [195°, 230°], and b = [−5.°25, +5.°25]). The catalog was produced using a DBSCAN algorithm applied to the Milky Way Imaging Scroll Painting project data. We systematically analyze both the integrated properties and scaling relationships of these molecular clouds across different Galactocentric radii (8 < R GC < 26 kpc). An interesting finding is that each cloud property’s mean, median, and maximum values generally decrease systematically with increasing R GC, approximated quantitatively by exponential functions. The mass and size spectra for the entire sample show power-law indices of γ = −2.00 ± 0.13 and an upper mass limit of M 0 = (4.5 ± 2.8) × 106 M ⊙, and γ = −3.68 ± 0.48 with an upper size limit of R 0 = 144 ± 80 pc, respectively. These spectra exhibit steeper slopes and reduced upper limits as R GC increases. The derived scaling relations are M=12.00Reff2.41±0.003 , σv=0.25Reff0.66±0.003 , and α vir = 37.2M −0.40 ± 0.002 for the whole outer Galaxy clouds. These scaling relations show slightly steeper correlations observed in more distant locations and notably deviate from Larson’s relations. We find that 44.3% of the molecular mass resides in gravitationally bound structures, a proportion significantly higher than systematic studies from the CfA survey and external galaxies. Moreover, comparisons among different R GC subsamples also reveal a decrease in the amount of bound mass with increasing R GC. The scale length of the molecular disk is estimated to be approximately 2 kpc. These results, based on a constant CO conversion factor of XCO=2.0×1020cm−2Kkms−1−1 , may be slightly altered by variations in X CO. In summary, our findings provide robust evidence for the influence of Galactic evolution on molecular cloud properties, at least in the outer Galaxy region studied.

Exploring the impact of information technology integration capability on project management performance in Chinese construction industry: a moderated mediation analysis

PurposeThe purpose of this study is to explore whether information technology (IT) integration capability and data sharing can improve project management performance in China’s construction industry under the background of global informatization. Moreover, the authors explore the moderating role of relational governance between IT integration capability and data sharing.Design/methodology/approachA theoretical model based on the research hypotheses proposed in this study was developed, and a questionnaire survey was conducted with 205 professionals. The data collected were analyzed by the structural equation modeling (SEM) technique.FindingsThe results indicate that IT integration capability has a significant and positive impact on project management performance and data sharing. Moreover, data sharing has a significant and positive impact on project management performance, and it plays a mediating role between IT integration capability and project management performance. In addition, relational governance significantly influences the mediating effect of data sharing.Research limitations/implicationsThe data used in this study is from Chinese scenarios, so the research conclusions and application effects based on this are bound to have certain regional limitations. So, a larger sample size from other countries could be selected to test the model. Besides, there are many factors that affect project management performance improving, and the theoretical model proposed in this study may not be fully considered. Therefore, follow-up researchers can consider bringing more suitable variables into their research studies, so that the theoretical research studies can be more in line with the actual project management practice.Originality/valueThis research’s value is as follows: Firstly, this paper broadens the understanding of how IT integration capability, data sharing and relational governance affects project management performance and enriches the literature in the construction management field under the background of global informatization. Secondly, this research provides a detailed governance solution to improve project management performance.

Evolutionary States and Triplicity of Four Massive Semidetached Binaries with Long-term Decreasing Orbital Periods in the LMC

The massive semidetached binary with a long-term decreasing orbital period may involve a rapid mass-transfer phase in Case A, and thus, they are good astrophysical laboratories for investigating the evolution of massive binary stars. In this work, by using the long-term observational light curves from the Optical Gravitational Lensing Experiment project and other data in the low-metallicity Large Magellanic Cloud, four semidetached massive binaries with long-term decreases in the orbital periods are detected from 165 EB-type close binaries. It is found that the more massive component in S07798 is filling its Roche lobe, where the period decrease is caused by mass transfer from the primary to the secondary. However, the other three (S03065, S12631, S16873) are semidetached binaries with a lobe-filling secondary where the mass transfer between the components should cause the period to increase if the angular momentum is conservative. The long-term period decreases in these three systems may be caused by angular momentum loss. Additionally, the orbital periods of three systems (S03065, S07798, S16873) are detected to show cyclic variation with periods shorter than 11 yr, which can be plausibly explained by the presence of close-in third bodies in these massive binaries. Based on all of these results, it is suggested that the detected four semidetached binaries almost have multiplicity. The companion stars are crucial for the origin and evolution of these massive close binaries.

Interfacial shear properties between water-castable engineered cementitious composites (WECC) and concrete substrate

To characterize the shear performance and bonding mechanism of the interface between a water-castable engineered cementitious composite (WECC) and the concrete substrate, single-sided shear tests were conducted. The effects of age (3 days, 7 days, 14 days, and 28 days) and roughness (0 mm, 0.91 mm, and 1.87 mm) on the macroscopic performance of the interface were studied. Computed tomography (CT) was used to quantitatively analyze the changes in the mesoscopic void structure of the three interfaces at different ages, and scanning electron microscopy (SEM) was used to analyze the microscopic structure and hydration products of the interfaces. A three-zone, double-layer, two-interface bonding model for WECC–concrete substrate interfaces in water media is established, elucidating the bonding mechanism. On the basis of the CT test results and single-sided shear test results, an interfacial shear stressslip model and a peak shear stress prediction model including macro- and mesoscale parameters are developed. As the roughness and age increase, the interfacial peak shear stress gradually increases. When the roughness is 1.87 mm, the interfacial peak shear stress is the highest. The interfacial peak shear stress of W1.87–28 is 60.65 % greater than that of W1.87–14 and 34.25 % greater than that of W0.91–28. The results reveal the structure of the interface between WECC and concrete panels and provide early-age interfacial bonding data for WECC reinforcement projects.

Intelligent Forecast Model for Project Cost in Guangdong Province Based on GA-BP Neural Network

Project cost forecasting is a complex and critical process, and it is of paramount importance for the successful implementation of engineering projects. Accurately forecasting project costs can help project managers and relevant decision-makers make informed decisions, thereby avoiding unnecessary cost overruns and time delays. Furthermore, accurately forecasting project costs can make important contributions to better controlling engineering costs, optimizing resource allocation, and reducing project risks. To establish a high-precision cost forecasting model for construction projects in Guangdong Province, based on case data of construction projects in Guangdong Province, this paper first uses the Analytic Hierarchy Process (AHP) to obtain the characteristic parameters that affect project costs. Then, a neural network training and testing dataset is constructed, and a genetic algorithm (GA) is used to optimize the initial weights and biases of the neural network. The GA-BP neural network is used to establish a cost forecasting model for construction projects in Guangdong Province. Finally, by using parameter sensitivity analysis theory, the importance of the characteristic values that affect the project cost is ranked, and the optimal direction for controlling the project cost is obtained. The results showed: (1) The determination coefficient between the forecasting and actual values of the project cost forecasting model based on the BP neural network testing set is 0.87. After GA optimization, the determination coefficient between the forecasting and actual values of the GA-BP neural network testing set is 0.94. The accuracy of the intelligent forecast model for construction project cost in Guangdong Province has been significantly improved after optimization through GA. (2) Based on sensitivity analysis of neural network parameters, the most significant factor affecting the cost of construction projects in Guangdong Province is the number of above-ground floors, followed by the main structure type, foundation structure, above-ground building area, total building area, underground building area, fortification intensity, and building height. The results of parameter sensitivity analysis indicate the direction for cost control in construction projects. The research results of this paper provide theoretical guidance for cost control in construction projects.

Open Source Adoption for Digital Transformation and Data Management During the COVID-19 Crisis

With COVID-19-led business disruption and businesses expediting their move towards digital transformation, Industry experts observed a phenomenon of increased adoption of Open Source Software and database management systems to digitalize the business while keeping costs low. This research has studied this phenomenon using a three-step approach: using the collective intelligence of Twitter without the context of COVID-19, Twitter data with the context of COVID-19, and empirical validation with actual monthly downloads data of open source projects from SourceForge in Pre-COVID and COVID-19 time periods. The research finds that although the COVID-19 pandemic has triggered a digital transformation and the use of database management systems in many organizations, but there is no statistically significant increased use that can be attributed to a crisis response due to the pandemic.

Earthwork Network Architecture (ENA): Research for Earthwork Quantity Estimation Method Improvement with Large Language Model

Accurate earthwork quantity estimation is essential for effective project planning and cost management in the Architecture, Engineering, and Construction (AEC) industry. Traditional methods for quantity takeoff are often time-consuming and susceptible to human error, particularly when working with unstructured datasets such as CAD drawings. This study introduces the Earthwork Network Architecture (ENA), a novel deep learning framework that incorporates Large Language Models (LLMs), Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM) networks, and Transformers to automate and enhance the accuracy of earthwork quantity estimation. We assume that if LLMs can be trained effectively using such unstructured construction dataset, the effects such as improved accuracy and the challenges of LLMs can be clearly examined. Among the architectures tested, the LLM-based ENA demonstrated superior performance, achieving faster convergence, greater loss reduction, and higher classification accuracy, with a Quantity Takeoff Classification accuracy of 97.17%. However, the LLMs required significantly more computational resources compared with other models. These findings suggest that LLMs, typically used in natural language processing, can be effectively adapted for complex AEC datasets. This study lays the groundwork for future AI-driven solutions in the AEC industry, underscoring the potential of LLMs and Transformers to automate the quantity takeoff process and manage multimodal data in construction projects.

TMAA-net: tensor-domain multi-planal anti-aliasing network for sparse-view CT image reconstruction

Objective.Among various deep-network-based sparse-view CT image reconstruction studies, the sinogram upscaling network has been predominantly employed to synthesize additional view information. However, the performance of the sinogram-based network is limited in terms of removing aliasing streak artifacts and recovering low-contrast small structures. In this study, we used a view-by-view back-projection (VVBP) tensor-domain network to overcome such limitations of the sinogram-based approaches.Approach.The proposed method offers advantages of addressing the aliasing artifacts directly in the 3D tensor domain over the 2D sinogram. In the tensor-domain network, the multi-planal anti-aliasing modules were used to remove artifacts within the coronal and sagittal tensor planes. In addition, the data-fidelity-based refinement module was also implemented to successively process output images of the tensor network to recover image sharpness and textures.Main result.The proposed method showed outperformance in terms of removing aliasing artifacts and recovering low-contrast details compared to other state-of-the-art sinogram-based networks. The performance was validated for both numerical and clinical projection data in a circular fan-beam CT configuration.Significance.We observed that view-by-view aliasing artifacts in sparse-view CT exhibit distinct patterns within the tensor planes, making them effectively removable in high-dimensional representations. Additionally, we demonstrated that the co-domain characteristics of tensor space processing offer higher generalization performance for aliasing artifact removal compared to conventional sinogram-domain processing.

Signs and portents: determining the long-term association of New Deal era redlining with contemporary racial/ethnic compositions

This study explores the impact of New Deal-era redlining in the form of Home Owners Loan Corporation (HOLC) evaluations on the racial and ethnic makeup of U.S. cities decades later. We draw upon a sample of 172 cities across the United States from the Mapping Decline project and historical Census data collected by the Integrated Public Use Microdata Series (IPUMS) and the Longitudinal Tract Database (LTDB), addressing a gap in the literature by directly measuring whether the HOLC grades predict the subsequent local distribution of Black, Hispanic, and Asian populations. Our findings reveal a discernible yet diminishing connection between HOLC redlining and future Black populations, with the relationship with future Hispanic populations varying depending on whether the HOLC grade was a C or D. Only some correlation is observed between HOLC redlining and future Asian populations. This study highlights the intricate and evolving dynamics of race influenced by historical housing policies.

Exploring the Application of Large Language Models Based AI Agents in Leakage Detection of Natural Gas Valve Chambers

Leakage problems occur from time to time due to the large number of equipment and complex processes during oil and gas production and transportation. The traditional detection methods highly depend on manpower with large workload and are prone to missed and false alarms, which seriously affects the efficiency and safety of oil and gas production and transportation. With the continuous improvement of information technology and the rapid advancement of artificial intelligence (AI), the research on leakage detection technology based on AI methods has attracted more and more attention. This paper discusses the application scenarios of an AI agent based on the recently emerged large language model (LLM) technology in oil and gas production leakage detection: (1) Compared with the traditional leakage detection methods, this paper innovatively employs a combination of AI-based diagnostics and infrared temperature measurement technologies to develop a specialized small model for oil and gas leakage detection, which has been proven to significantly improve the accuracy of detecting industrial venting events in natural gas valve chambers; (2) By employing retrieval-augmented generation (RAG) technology, a knowledge vector library is constructed, utilizing a series of leakage-related documents, assisting the LLM to carry out knowledge questioning and inference. Compared with the traditional SimBERT, the accuracy can be improved by about 15% in the Q&amp;A search ability test. The correct rate is about 70% higher than the SimBERT in the Chinese complex reasoning quiz. Also, it can still remain stable under high load conditions, with the interruption rate of retrieval closing to zero. (3) By combining the specialized small model and the knowledge Q&amp;A tool, the natural gas valve chambers’ leakage detection AI agent based on the open-source LLM model was designed and developed, which preliminarily achieved the leakage detection based on the specialized small model, and the automatic processing of the retrieval reasoning process based on the knowledge Q&amp;A tool and the intelligent generation of corresponding leakage disposal scheme. The effectiveness of the method has been verified by actual project data. This article conducts preliminary explorations into the in-depth applications of AI agents based on LLMs in the oil and gas energy industry, demonstrating certain positive outcomes.

Source-detector trajectory optimization for FOV extension in dental CBCT imaging

In dental imaging, Cone Beam Computed Tomography (CBCT) is a widely used imaging modality for diagnosis and treatment planning. Small dental scanning units are the most popular due to their cost-effectiveness. However, these small systems have the limitation of a small field of view (FOV) as the source and detector move at a limited angle in a circular path. This often limits the FOV size. In this study, we addressed this issue by modifying the source-detector trajectory of the small dental device. The main goal of this study was to extend the FOV algorithmically by acquiring projection data with optimal projection angulation and isocenter location rather than upgrading any physical parts of the device. A novel algorithm to implement a Volume of Interest (VOI) guided trajectory is developed in this study based on the small dental imaging device's geometry. In addition, this algorithm is fused with a previously developed off-axis scanning method which uses an elliptical trajectory, to compensate for the existing constraints and to further extend the FOV. A comparison with standard circular trajectory is performed. The FOV of such a standard trajectory is a circle of 11 cm diameter in the axial plane. The proposed novel trajectory extends the FOV significantly and a maximum FOV of 19.5 cm is achieved with the Structural Similarity Index Measure (SSIM) score ranging between (≈98-99%) in different VOIs. The study results indicate that the proposed source-detector trajectory can extend dental imaging FOV and increase imaging performance, which ultimately results in more precise diagnosis and enhanced patient outcomes.

Optimizing Parameters for Enhanced Iterative Image Reconstruction Using Extended Power Divergence

In this paper, we propose a method for optimizing the parameter values in iterative reconstruction algorithms that include adjustable parameters in order to optimize the reconstruction performance. Specifically, we focus on the power divergence-based expectation-maximization algorithm, which includes two power indices as adjustable parameters. Through numerical and physical experiments, we demonstrate that optimizing the evaluation function based on the extended power-divergence and weighted extended power-divergence measures yields high-quality image reconstruction. Notably, the optimal parameter values derived from the proposed method produce reconstruction results comparable to those obtained using the true image, even when using distance functions based on differences between forward projection data and measured projection data, as verified by numerical experiments. These results suggest that the proposed method effectively improves reconstruction quality without the need for machine-learning techniques in parameter selection. Our findings also indicate that this approach is useful for enhancing the performance of iterative reconstruction algorithms, especially in medical imaging, where high-accuracy reconstruction under noisy conditions is required.

A Representative Profile of MSW Graduates in the United States.

As the United States faces burgeoning behavioral health needs and a growing recognition of the inextricable link between social conditions and health outcomes and quality-of-life indicators, a current representative profile of the MSW workforce with respect to sociodemographic and employment characteristics can provide a reference point for ongoing assessments of that workforce. The profile also can establish a baseline by which efforts to bolster the diversity and strength of the MSW workforce can be grounded. The core aim of the current study was to generate a nationally representative, descriptive profile of MSW graduates in the United States using a sample of 1,028 MSW graduates (representing a subpopulation of 691,061 individuals) from the 2021 National Survey of College Graduates. Weighted univariate and bivariate analyses were conducted to generate a descriptive profile of MSW graduates with respect to sociodemographic, education, and employment characteristics. Study findings can help employers, health services researchers, policymakers, and practitioners understand the context of the MSW workforce as it relates to diversity, labor workforce projection data, and student loan and salary considerations. This information can guide future policy and training goals to support the future vitality of the social work profession.

Geological Features and Ore Formation Pattern of Jinzhong Bauxite Mine, Shanxi Province, China

Bauxite is located in the lower part of the Benxi Formation, and the prospecting project shows that the distribution of bauxite ore bodies in the area is discontinuous, and there are differences in the thickness of the ore bodies. To have a clearer understanding of the bauxite ore bodies and metallogenic pattern in the census area, this study has made use of the prospecting project data, sample analysis results, etc., and combined with the geological and tectonic background of the area, the stratigraphic spreading characteristics of the bauxite ore body spatial spreading, ore characteristics and ore controlling factors in the area have been explored and researched. The bauxite ore bodies in the study area are stratified but the ore-bearing rock system is influenced by the Ordovician paleokarst geomorphology and intermittently distributed horizontally, which can be distinguished into three different ore bodies. The bauxite ore body and Shanxi-style iron ore are associated with hard clay ore, in which the ore minerals are mainly monohydrate duralumin. The natural ore type can be divided into dense bauxite and oolitic bauxite. Bauxite in this area mainly originated from the basal Ordovician carbonate rocks, with a combination of iron-rich and aluminum-rich layers, and is a typical carbonate rock sedimentary deposit of ancient weathering crust.

Low-dose radiographic inspection of welding by a novel aperiodic reverse stochastic resonance method

Abstract Low-dose radiographic inspection is a growing trend in industry to minimize radiation risks to humans and the environment. However, reduction in radiation dose often introduces significant noise, which affects image quality and hinders accurate identification of subtle defects. This study addresses this issue by introducing a novel phenomenon called aperiodic reverse stochastic resonance (ARSR), observed in nonlinear systems excited by aperiodic binary signals. ARSR enables simultaneous amplitude amplification and reversal of signals under specific noise conditions. Leveraging ARSR, we propose an image denoising framework for low-dose radiographic inspections. First, a set of projection data is obtained by using Radon transform to reduce the dimensionality of x-ray images from different angles. Then, the projection data is modulated based on the ARSR system. Finally, the image is reconstructed based on the inverse Radon transform. Simulations and experimental comparison results in welding applications validate the effectiveness of the framework, demonstrating significant improvements in image quality for low-dose radiographic defect detection. Unlike advanced methods such as Gaussian filtering, BM3D, and DnCNN, which operate at the pixel level, ARSR performs denoising at the projection data stage, reducing noise impact, preserving original information, and focusing on physical data processing during imaging. This approach enhances the detection of subtle defects, highlighting the potential of stochastic resonance in image processing.

Monitoring thin membranes for wrinkles using single-camera photogrammetry

This study presents a simple method for measuring the amplitude and wavelength of wrinkles in thin membranes by photogrammetry using a single camera. The proposed method extends an equation for computing wrinkle amplitude and wavelength from tension–field theory to obtain the average deformation in a wrinkled region. Meanwhile, single-camera photogrammetric measurements are leveraged to project wrinkles onto a planar surface. The proposed method utilizes the correlation between the solution from tension–field theory and wrinkle projection data to estimate the wrinkle amplitude and wavelength from images captured by a single camera. The method was applied to a rectangular, wrinkled membrane subjected to a shear load to demonstrate its effectiveness. The results indicated that the wrinkle amplitude and wavelength estimated by this technique were in close agreement with actual wrinkling phenomena. This approach offers a simple solution for monitoring membrane wrinkling when the installation space for measurement equipment is limited.