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Displacement measurement in large-scale structures (such as excavation walls) is one of the most important applications of close-range photogrammetry, in which achieving high precision requires extracting and accurately matching local features from convergent images. The purpose of this study is to introduce a new multi-image pointing (MIP) algorithm is introduced based on the characteristics of the geometric model generated from the initial matching. This self-adaptive algorithm is used to correct and improve the accuracy of the extracted positions from local features in the convergent images.

In this paper, the new MIP algorithm based on the geometric characteristics of the model generated from the initial matching was introduced, which in a self-adaptive way corrected the extracted image coordinates. The unique characteristics of this proposed algorithm were that the position correction was accomplished with the help of continuous interaction between the 3D model coordinates and the image coordinates and that it had the least dependency on the geometric and radiometric nature of the images. After the initial feature extraction and implementation of the MIP algorithm, the image coordinates were ready for use in the displacement measurement process. The combined photogrammetry displacement adjustment (CPDA) algorithm was used for displacement measurement between two epochs. Micro-geodesy, target-based photogrammetry and the proposed MIP methods were used in a displacement measurement project for an excavation wall in the Velenjak area in Tehran, Iran, to evaluate the proposed algorithm performance. According to the results, the measurement accuracy of the point geo-coordinates of 8 mm and the displacement accuracy of 13 mm could be achieved using the MIP algorithm. In addition to the micro-geodesy method, the accuracy of the results was matched by the cracks created behind the project’s wall. Given the maximum allowable displacement limit of 4 cm in this project, the use of the MIP algorithm produced the required accuracy to determine the critical displacement in the project.

Evaluation of the results demonstrated that the accuracy of 8 mm in determining the position of the points on the feature and the accuracy of 13 mm in the displacement measurement of the excavation walls could be achieved using precise positioning of local features on images using the MIP algorithm.The proposed algorithm can be used in all applications that need to achieve high accuracy in determining the 3D coordinates of local features in close-range photogrammetry.

Some advantages of the proposed MIP photogrammetry algorithm, including the ease of obtaining observations and using local features on the structure in the images rather than installing the artificial targets, make it possible to effectively replace micro-geodesy and instrumentation methods. In addition, the proposed MIP method is superior to the target-based photogrammetric method because it does not need artificial target installation and protection. Moreover, in each photogrammetric application that needs to determine the exact point coordinates on the feature, the proposed algorithm can be very effective in providing the possibility to achieve the required accuracy according to the desired objectives.

Introduction The cracking of external building walls due to differential settlement or foundation failure has traditionally been monitored using ‘tell‐tales’, placed across the cracks, which are inspected for movement at frequent intervals. This technique is satisfactory if an indication of movement at individual locations on the structure is sufficient, but it is not very satisfactory if either an overall picture of the cracking or the change in the crack pattern with time is required.

This article presents a case study on the Heritage Building Information Modeling (H-BIM) application in a historic village in Bursa, Turkey. The study addresses how tailor-made and highly structured H-BIM approaches can effectively be implemented in preservation applications for historic vernacular buildings in the rural architecture context.

Using inexpensive digital photogrammetry techniques tightly combined with an object-oriented BIM ontology, parametric meta-modeling and object/system propagation methods, the study employed a holistic H-BIM approach for capturing the materiality, building object behaviors and indigenous construction principles of a characteristic vernacular house that were synthesized in a parametric H-BIM model. The followed stages, steps and connected methods were systematized and articulated in a prototypical H-BIM implementation framework.

The study findings suggested that the developed parametric H-BIM approach can return effective results with the combined use of low-cost and practical digital photogrammetry with BIM methods. The flexibility and adaptability of the parametric H-BIM implementation framework facilitated the synthesis of a comprehensive H-BIM model and allowed an in-depth evaluation of local architectural heritage with its physical, spatial and environmental characteristics. The proposed H-BIM approach also provided significant documentation and system-specific assessment benefits for preserving the vernacular examples which are prone to extinction especially due to structural and systemic deterioration.

The study proposes a feasible, practical and replicable H-BIM implementation methodology for vernacular preservation applications. The knowledge-embedded H-BIM approach, flows and techniques presented in this study provide a holistic and systematic H-BIM framework – with the integrated use of digital photogrammetry and parametric meta-modeling methods – that has the potential for the democratization of H-BIM applications in education and practice.

The need to digitise is an awareness that is shared across our community globally, and yet the probability of the intersection between resources, expertise and institutions are not as prospective. A strategic view towards the long-term goal of cultivating and digitally upskilling the younger generation, building a community and creating awareness with digital activities that can be beneficial for cultural heritage is necessary.

The work involves distributing tasks between stakeholders and local volunteers. It uses close-range photogrammetry for reconstructing the entire heritage site in 3D, and outlines achievable digitisation activities in the crowdsourced, close-range photogrammetry of a 19th century Cheah Kongsi clan temple located in George Town, a UNESCO World Heritage Site in Penang, Malaysia.

The research explores whether loosely distributing photogrammetry work that partially simulates an unorganised crowdsourcing activity can generate complete models of a site that meets the criteria set by the needs of the clan temple. The data acquired were able to provide a complete visual record of the site, but the 3D models that was generated through the distributed task revealed gaps that needed further measurements.

Key lessons learned in this activity is transferable. Furthermore, the involvement of volunteers can also raise awareness of ownership, identity and care for local cultural heritage.

Key lessons learned in this activity is transferable. Furthermore, the involvement of volunteers can also raise awareness of identity, ownership, cultural understanding, and care for local cultural heritage.

The value of semi-formal activities indicated that set goals can be achieved through crowdsourcing and that the new generation can be taught both to care for their heritage, and that the transfer of digital skills is made possible through such activities. The mass crowdsourcing activity is the first of its kind that attempts to completely digitise a cultural heritage site in 3D via distributed activities.

Introduction This paper is a follow‐up to one published in Structural Survey in 1985 under the title of ‘Monitoring crack propagation using close‐range photogrammetry’. In that initial paper, some of the equipment and techniques used in close‐range photogrammetry were outlined and details were given of an application of the method to monitor the propagation of cracks within one of the external walls of a detached house which was undergoing movement due to poor construction of the foundations.

The relative orientation (RO) is an important step on photogrammetric processes of stereoscopic images. The relationship between the stereoscopic images is constructed by tie (conjugate) points. Many automatic tie point selection methods have been introduced by photogrammetry community so far. The scale invariant feature transform (SIFT) and speeded‐up robust features (SURF) are frequently used for automatic tie point selection from stereoscopic images. However, any research has been performed related to RO errors (y‐parallaxes) on SIFT and SURF extracted tie points. The purpose of this paper is to compute errors on tie points and investigate their distributions on the model area in terms of size.

The experimental studies were performed on an historical building as it enables more tie points for investigation. While a couple of the stereoscopic images include rich details, the other has poor details. The image orientation and tie point selection accuracy were evaluated by root mean square and y‐parallaxes, respectively. The relationship between y‐parallaxes of tie points and their distances from centre of the images were investigated.

SIFT and SURF have a large number of tie points according to manual method. The y‐parallaxes on tie points have uniform distribution for two methods. There are relations between the precision of the SIFT and SURF keypoints and their distances from the centre of the image. Moreover, the accuracy of the RO and size of the y‐parallaxes on tie points depend on matching accuracy of the keypoints. Furthermore, although there are a few tie points that have large y‐parallax especially by the SURF, RO could be performed with high accuracy thanks to numerous tie points.

Stereoscopic images of close‐range photogrammetry have different scale and rotations, unlike aerial photogrammetry. Manual selection of tie points is time consuming and tedious. Furthermore, if the measurement surface has no implicit entities, enough tie points from the images cannot be selected by manually. However, tie point selection can be performed by SIFT and SURF automatically, even if there are scale, noise and rotation between the images.

The current technique used to measure construction is the conventional total station method. However, the conventional method is time-consuming and could not be used to create a photo-realistic three-dimensional (3D) model of an object. Furthermore, the Canseleri building is located at a slope. The paper aims to discuss this issue.

The aim of this study is to assess the geometric accuracy of a 3D model using unmanned aerial vehicle (UAV) images. There are two objectives in this study. The first is to construct a 3D model of the Canseleri building using UAV images. The second objective is to validate the 3D model of the Canseleri building based on actual measurements.

The close-range photogrammetry method, using the UAV platform, was able to produce a 3D building model. The results show that the errors between the actual measurement and the generated 3D model were less than 4 cm. The accuracy of the 3D model achieved in this study was about 0.015 m, compared to total station measurements.

Accuracy assessment was done by comparing the estimated measurement of the 3D model with the direct measurement. The differences between the measured values with actual values could be compared. Based on this study, the 3D building model gave a reliable accuracy for specific applications.

This paper aims to discuss the scanning methodology depending on the close-range photogrammetry technique, which is appropriate for the precise three-dimensional (3D) modelling of objects in millimetres, such as the dimensions and structures in sub-millimetre scale.

The camera was adjusted to be tilted around the horizontal axis, while coded dot targets were used to calibrate the digital camera. The experiment was repeated with different rotation angles (5°, 10°, 15°, 20°, 25°, 30°, 50° and 60°). The images were processed with the PhotoModeler software to create the 3D model of the sample and estimate its dimensions. The features of the sample were measured using high-resolution transmission electron microscopy, which has been considered as a reference and the comparative dimensions.

The results from the current study concluded that changing the rotation angle does not significantly affect the results, unless the angle of imagery is large which prevent achieving about 20: 30% overlap between the images but, the more angle decreases, the more number of images increase as well as the processing duration in the programme.

Develop an automatic appropriate for the precise 3D modelling of objects in millimetres, such as the dimensions and structures in sub-millimetre scale using photogrammetry.

This paper aims to propose an automatic imaging network design to improve the efficiency and accuracy of automated construction progress monitoring. The proposed method will address two shortcomings of the previous studies, including the large number of captured images required and the incompleteness and inaccuracy of generated as-built models.

Using the proposed method, the number of required images is minimized in two stages. In the first stage, the manual photogrammetric network design is used to decrease the number of camera stations considering proper constraints. Then the image acquisition is done and the captured images are used to generate 3D points cloud model. In the second stage, a new software for automatic imaging network design is developed and used to cluster and select the optimal images automatically, using the existing dense points cloud model generated before, and the final optimum camera stations are determined. Therefore, the automated progress monitoring can be done by imaging at the selected camera stations to produce periodic progress reports.

The achieved results show that using the proposed manual and automatic imaging network design methods, the number of required images is decreased by 65 and 75 per cent, respectively. Moreover, the accuracy and completeness of points cloud reconstruction is improved and the quantity of performed work is determined with the accuracy, which is close to 100 per cent.

It is believed that the proposed method may present a novel and robust tool for automated progress monitoring using unmanned aerial vehicles and based on photogrammetry and computer vision techniques. Using the proposed method, the number of required images is minimized, and the accuracy and completeness of points cloud reconstruction is improved.

To generate the points cloud reconstruction based on close-range photogrammetry principles, more than hundreds of images must be captured and processed, which is time-consuming and labor-intensive. There has been no previous study to reduce the large number of required captured images. Moreover, lack of images in some areas leads to an incomplete or inaccurate model. This research resolves the mentioned shortcomings.

This main purpose of this study is to summarize the experience at the Construction Technology and Management Center (CTMC) to develop a Digitalizing Construction Monitoring (DCM) system by integrating 3DAutoCAD drawings and digital images. The objective of this paper is to propose a framework model for the DCM system and discuss in detail the steps involved for developing and calculating the 3D coordinate values from 2D digital images.

As digital images are one of the major sources of information from site, the process of measuring the project progress from images is quite challenging. This study used Photogrammetry techniques to extract the information from digital images, which can be concisely defined as the science of calculating 3D object coordinates from images, with PhotoModeler pro‐version software. Issues pertaining to the quality of the 3D model derived from 2D digital images are also discussed.

A framework model for DCM was proposed and different phases were discussed. A pilot case study on Larkin Mosque Car Parking Project was conducted to check the validity of using Photogrammetry techniques to extract 3D coordinate values by using PhotoModeler Software. Preliminary results show that significant control has been achieved to extract 3D coordinate values from 2D digital images, which and can be integrated into the digitalized system to automate the construction project monitoring process.

The techniques discussed in this paper are used for monitoring the project progress systematically. The results of this study will be incorporated to develop a fully automated project progress monitoring system, which can be updated automatically as the project progresses automatically.