3D structural component recognition and modeling method using color and 3D data for construction progress monitoring

Construction progress monitoring has been recognized as one of the key elements that lead to the success of a construction project. By performing construction progress monitoring, corrective measures and other appropriate actions can be taken in a timely manner, thereby enabling the actual performance to be as close as possible to the desired outcome even if the construction performance significantly deviates from the original plan. However, current methods of data acquisition and its use in construction progress monitoring have tended to be manual and time consuming. This paper proposes an efficient, automated 3D structural component recognition and modeling method that employs color and 3D data acquired from a stereo vision system for use in construction progress monitoring. An outdoor experiment was performed on an actual construction site to demonstrate the applicability of the method to 3D modeling of such environments, and the results indicate that the proposed method can be beneficial for construction progress monitoring.     

An object-based 3D walk-through model for interior construction progress monitoring

The complicated nature of interior construction works makes the detailed progress monitoring challenging. Current interior construction progress monitoring methods involve submission of periodic reports and are constrained by their reliance on manually intensive processes and limited support for recording visual information. Recent advances in image-based visualization techniques enable reporting construction progress using interactive and visual approaches. However, analyzing significant amounts of as-built construction photographs requires sophisticated techniques. To overcome limitations of existing approaches, this research focuses on visualization and computer vision techniques to monitor detailed interior construction progress using an object-based approach. As-planned 3D models from Building Information Modeling (BIM) and as-built photographs are visualized and compared in a walk-through model. Within such an environment, the as-built interior construction objects are decomposed to automatically generate the status of construction progress. This object-based approach introduces an advanced model that enables the user to have a realistic understanding of the interior construction progress.     

Structure‐aware 3D reconstruction for cable‐stayed bridges: A learning‐based method

A powerful deep learning‐based three‐dimensional (3D) reconstruction method for reconstructing structure‐aware semantic 3D models of cable‐stayed bridges is proposed herein. Typically, conventional bridge semantic 3D model reconstruction methods are not robust when low‐quality point clouds are used. Furthermore, they are suited particularly for their respective fields and less generalized for cable‐stayed bridges. Hence, a structure‐aware learning‐based cable‐stayed bridge 3D reconstruction framework is proposed. The encoder part of the network uses both multiview images and a photogrammetric point cloud as input, whereas the decoder part uses a recursive binary tree network to model a high‐level structural relation graph and low‐level 3D geometric shapes. Two actual cable‐stayed bridges are employed as examples to evaluate the proposed method. Test results demonstrate that the proposed method successfully reconstructs the bridge model with structural components and their relations. Quantitative results indicate that the predicted models achieved an average F₁ score of 99.01%, a Chamfer distance of 0.0259, and a mesh‐to‐cloud distance of 1.78 m. The achieved result is similar to that obtained using the manual reconstruction approach in terms of component‐wise accuracy, and it is considerably better than that obtained using the manual approach in terms of spatial accuracy. In addition, the proposed recursive binary tree network is robust to noise and partial scans. The potential applications of the obtained 3D bridge models are discussed.     

Vibration‐based semantic damage segmentation for large‐scale structural health monitoring

Toward reduced recovery time after extreme events, near real‐time damage diagnosis of structures is critical to provide reliable information. For this task, a fully convolutional encoder–decoder neural network is developed, which considers the spatial correlation of sensors in the automatic feature extraction process through a grid environment. A cost‐sensitive score function is designed to include the consequences of misclassification in the framework while considering the ground motion uncertainty in training. A 10‐story‐10‐bay reinforced concrete (RC) moment frame is modeled to present the design process of the deep learning architecture. The proposed models achieve global testing accuracies of 96.3% to locate damage and 93.2% to classify 16 damage mechanisms. Moreover, to handle class imbalance, three strategies are investigated enabling an increase of 16.2% regarding the mean damage class accuracy. To evaluate the generalization capacities of the framework, the classifiers are tested on 1,080 different RC frames by varying model properties. With less than a 2% reduction in global accuracy, the data‐driven model is shown to be reliable for the damage diagnosis of different frames. Given the robustness and capabilities of the grid environment, the proposed framework is applicable to different domains of structural health monitoring research and practice to obtain reliable information.     

A deep‐learning‐based computer vision solution for construction vehicle detection

This paper aims at providing researchers and engineering professionals from the first step of solution development to the last step of solution deployment with a practical and comprehensive deep‐learning‐based solution for detecting construction vehicles. This paper places particular focus on the often‐ignored last step of deployment. Our first phase of solution development involved data preparation, model selection, model training, and model validation. Given the necessarily small‐scale nature of construction vehicle image datasets, we propose as detection model an improved version of the single shot detector MobileNet, which is suitable for embedded devices. Our study's second phase comprised model optimization, application‐specific embedded system selection, economic analysis, and field implementation. Several embedded devices were proposed and compared. Results including a consistent above 90% mean average precision confirm the superior real‐time performance of our proposed solutions. Finally, the practical field implementation of our proposed solutions was investigated. This study validates the practicality of deep‐learning‐based object detection solutions for construction scenarios. Moreover, the detailed information provided by the current study can be employed for several purposes such as safety monitoring, productivity assessments, and managerial decision making.     

基于FLACK~(3D)的隧道监测数值模拟

基于西施坡隧道,按照隧道开挖与支护顺序,分析监测数据,采用FLAC3D软件对隧道开挖与支护全过程进行数值模拟。分析结果表明,数值模拟与信息化施工监测数据吻合较好,其土体计算参数选择合理,数值模拟方法正确,为类似隧道工程的变形预测及设计提供参考据。     

基于3D GIS的露天矿山开采监管系统开发及应用

传统的矿山开采监管手段主要依靠人工实地测绘,存在费时费力、测绘成果单一且不直观等不足。本文基于3D GIS、5G通讯、物联网及大数据等技术,同时采用多源数据可视化引擎、分级索引调度的高效渲染、高性能矢量数据索引及云端一体化等关键技术,设计并开发了矿山开采监管应用系统,实现了露天矿山开采可视化监管,提高了监管智能化水平,可为其他省市开展矿山监管提供参考和借鉴。     

基于三维模型技术的城市地铁施工期地面沉降分析

应用GIS专业软件ArcView中的空间分析和三维分析模块,以南京地铁某车站施工为例,采用水准测量法和Peck公式法分别进行地表沉降离散数据采集,然后选取反距离权重法（IDW）进行空间插值拟合出地面变形的DEM趋势面,并进行综合三维分析,以更加直观、生动的方式为项目决策者和施工人员提供技术支持和决策依据。     

基于3D GIS的建筑变形自动化监测与管理系统的研究与开发

本文针对大型和高层建筑物的变形监测问题,阐述了研究并开发建筑物变形信息自动化采集和管理系统的原理和方法,并通过实例论证了系统构建方案的可行性和实用性.     

A Kinect‐Based System for 3D Reconstruction of Sewer Manholes

A system for automatic 3D modeling of sewer manholes using two Microsoft Kinect sensors is presented. The hardware design is based on a hand‐held scanner that includes two Kinect sensors, a mini‐PC and LED‐based illumination bars. The scanning and reconstruction software has been implemented using open‐source software toolkits. The information from the two Kinect sensors is merged into a single 3D model. Reconstruction is performed by adapting an existing RGB‐D SLAM‐based algorithm by filtering out nonconsistent correspondences and an alternative graph‐based optimization, which results in reconstruction errors of the order of 1 cm. The final result obtained is a complete system with a hand‐held scanner and management software which is highly competitive with respect to present commercial systems, using much cheaper technology and open‐source toolkits with low reconstruction errors.     

Chip‐based duplex real‐time PCR for water quality monitoring concerning Legionella pneumophila and Legionella spp.

Based on biomolecular methods, rapid and selective identification of human pathogenic water organisms becomes an important issue. Legionella spp., are pathogenic water bacteria with worldwide significance. Prevalent detection methods for these microorganisms are time and/or cost intensive. We describe a detection setup and relating DNA assay. A miniaturized real‐time polymerase chain reaction (real‐time PCR) for direct on‐line discrimination of Legionella pneumophila and Legionella spp. was established and integrated into a real‐time PCR‐chip‐system. The PCR‐chip device combines a temperature controlling unit and a fluorescence intensity measurement. It was designed to achieve rapid amplification, using an approach of real‐time fluorescence read out with the intercalating dye EvaGreen® and melting curve analysis, without requiring multiple probes. The presented results exhibit reproducibility and good sensitivity, showing that the setup is suitable for robust, rapid and cost‐efficient detection and monitoring of a variety of Legionella spp.in urban water samples.     

Non‐Uniform B‐Spline Surface Fitting from Unordered 3D Point Clouds for As‐Built Modeling

The three‐dimensional mapping of the built environment is of particular importance for engineering applications such as monitoring work‐in‐progress and energy performance simulation. The state‐of‐the‐art methods for fitting primitives, non‐uniform B‐Spline surface (NURBS) and solid geometry to point clouds still fail to account for all the topological variations or struggle with mapping of physical space to parameter space given unordered, incomplete, and noisy point clouds. Assuming an input of points that can be described by a single non‐self‐intersecting NURBS, this article presents a new method that leverages segmented point clouds and outputs NURBS surfaces. It starts by successively fitting uniform B‐Spline curves in two‐dimensional as planar cross‐sectional cuts on each surface. An intermediate B‐Spline surface is then computed by globally optimizing and lofting over the cross‐sections. This surface is used to parameterize the points and perform final refinement to a NURBS. For cylindrical segments such as pipes, a new supervised method is also introduced to string the fitted segments, identify connection types, standardize the connections, and then refine them using NURBS optimization. Experimental results show the applicability of the proposed methods for as‐built modeling purposes.     

基于Civil3D的BIM路线全自动识别与生成技术

二次开发是针对需求提高BIM软件工作效率的有效途径。针对道路翻模工作效率低,重复工作量大的问题,基于Civil3D软件,利用C#. net语言开发了辅助翻模插件,可以自动识别CAD图纸中的图形参数,并自动生成BIM路线模型。     

基于3D技术的工厂动力设备实时监控画面开发应用

文章概述工厂动力设备三维实时监控画面开发应用过程,先对组成动力设备进行3D建模,把建好设备3D场景模型导入到一款具有内置3D引擎的平台软件中,开发出360度可随意缩放、随意旋转的三维实时设备模型,监控软件通过实时数据驱动的三维动态效果,直观形象地反映设备的生产状态以及运行情况。     

3D AR-based modeling for discrete-event simulation of transport operations in construction

This study proposes a 3D visualized modeling method for DES of transport operations in construction. The 3D simulation model built is a virtual field scene with property settings. AR technology was further applied to allow the use of a real-world image as the modeling background, which pictorially presents the current status of the real site as a visual basis for modeling. A typical transport operation was analyzed to determine the component classes for modeling. Then the visual representation and attributes of each modeling component class were proposed, along with modeling rules to build the 3D simulation model. A prototype system with STROBOSCOPE as the simulation engine was developed for presenting the proposed modeling method. A set of transformation rules was proposed for converting a 3D simulation model to a STROBOSCOPE input file. The system automatically extracts the simulation output and animates the 3D model to visually demonstrate the simulation result.     

Appearance-based material classification for monitoring of operation-level construction progress using 4D BIM and site photologs

This paper presents a new appearance-based material classification method for monitoring construction progress deviations at the operational-level. The method leverages 4D Building Information Models (BIM) and 3D point cloud models generated from site photologs using Structure-from-Motion techniques. To initialize, a user manually assigns correspondences between the point cloud model and BIM, which automatically brings in the photos and the 4D BIM into alignment from all camera viewpoints. Through reasoning about occlusion, each BIM element is back-projected on all images that see that element. From these back-projections, several 2D patches are sampled per element and are classified into different material types. To perform material classification, the expected material type information is derived from BIM. Then the image patches are convolved with texture and color filters and their concatenated vector-quantized responses are compared with multiple discriminative material classification models that are relevant to the expected progress of that element. For each element, a quantized histogram of the observed material types is formed and the material type with the highest appearance frequency infers the appearance and thus the state of progress. To validate, four new datasets of incomplete and noisy point cloud models are introduced which are assembled from real-world construction site images and BIMs. An extended version of the Construction Material Library (CML) is also introduced for training/testing the material classifiers. The material classification shows an average accuracy of 92.4% for CML image patches of 100 × 100 pixels. The experiments on those four datasets show an accuracy of 95.9%, demonstrating the potential of appearance-based recognition method for inferring the actual state of construction progress for BIM elements.     

基于AutoLISP的三维模型快速建立与精度分析

建筑物的三维模型是数字城市重建的重要组成部分,而建筑物高精度三维模型的快速建立一直是三维数字仿真亟待解决的问题。本文通过无协作目标电子全站仪采集建筑的三维坐标,基于AutoLISP实现了数据的导入、建筑物墙面的拟合、特征点的投影及门窗等构件的自动生成,从而快速生成建筑物的三维模型,并对生成的建筑模型进行精度分析。基于AutoLISP构建建筑物的三维模型,大大提高了建模速度;并能有效控制三维建筑模型的质量。     

A Stereo‐Matching Technique for Recovering 3D Information from Underwater Inspection Imagery

Underwater inspections stand to gain from using stereo imaging systems to collect three‐dimensional measurements. Although many stereo‐matching algorithms have been devised to solve the correspondence problem, that is, find the same points in multiple images, these algorithms often perform poorly when applied to images of underwater scenes due to the poor visibility and the complex underwater light field. This article presents a new stereo‐matching algorithm, called PaLPaBEL (Pyramidal Loopy Propagated BELief) that is designed to operate on challenging imagery. At its core, PaLPaBEL is a semiglobal method based on a loopy belief propagation message passing algorithm applied on a Markov random field. A pyramidal scheme is adopted that enables wide disparity ranges and high‐resolution images to be handled efficiently. For performance evaluation, PaLPaBEL is applied to underwater stereo images captured under various visibility conditions in a laboratory setting, and to synthetic imagery created in a virtual underwater environment. The technique is also demonstrated on stereo images obtained from a real‐world inspection. The successful results indicate that PaLPaBEL is well suited for underwater application and has value as a tool for the cost‐effective inspection of marine structures.     

Integrating 3D laser scanning and photogrammetry for progress measurement of construction work

Progress reporting is an essential management function for successful delivery of construction projects. It relies on tangible data collected from construction job sites, which is then used to compare actual work performed to that planned. One method used to collect actual work data is 3D laser scanning, where the construction site is scanned at different times to generate data, which can then be used to estimate the quantities of work performed within the time interval considered between two successive scans. Photogrammetry is another method for data collection where the geometrical properties of an object on site are generated from its photo image. This paper presents a method, which integrates 3D scanning and photogrammetry in an effort to enhance the speed and accuracy of data collection from construction sites to support progress measurement and project control. The application of the proposed method is demonstrated using a building presently under construction.