As-designed Model Research Articles

Correction: Cabral et al. Railway Bridge Geometry Assessment Supported by Cutting-Edge Reality Capture Technologies and 3D As-Designed Models. Infrastructures 2023, 8, 114

In the original publication [...]

Matching design-intent planar, curved, and linear structural instances in point clouds

The lack of timely progress monitoring and quality control contributes to cost-escalation, lowering of productivity, and broadly poor project performance. This paper addressed the challenge of high-precision structural instance segmentation from point clouds by leveraging as-designed IFC models in Scan-vs-BIM contexts. We proposed an automatic method to segment the entire points corresponding to the as-designed instance. The workflow contains: 1) Instance descriptor generation; 2) PROSAC-based shape detection; 3) DBSCAN-based cluster optimization. The method matches design-intent planar, curved, and linear structural instances in complex scenarios including: 1) the as-built point cloud is noisy with high occlusions and clutter; 2) deviations between as-built instances and as-designed models in terms of position, orientation, and scale; 3) both Manhattan-World and non-Manhattan-World instances. The experimental results from five diverse real-world datasets showed excellent performance with mPrecision 0.962, mRecall 0.934, and mIoU 0.914. Benchmarking against state-of-the-art methods showed that the proposed method outperforms all existing ones.

Topological fidelity of additively manufactured AlSi10Mg gyroid structures

Additively manufactured Triply Periodic Minimal Surfaces (TPMS), walled gyroid structures (WGS) in particular, have garnered research and industry attention recently, especially in realizing compact and efficient heat exchangers. Methodologies for topological and geometric fidelity assessment of as-printed samples against their as-designed models are pertinent to predict and evaluate their performance, as deviations can impact heat transfer rates and energy dissipation. In this study, 7 WGS samples with cell sizes and relative densities ranging from 3 mm to 7 mm and 8.5 % to 26.2 % respectively were manufactured via laser powder bed fusion (LPBF) in AlSi10Mg. Geometric (3D) and layer-wise (2D) topological fidelity analyses were done by comparing micro x-ray computed tomography (μ−CT) scans of the WGS samples with corresponding computer-aided designed (CAD) models. Metrics such as the degree of overlap, underprint, overprint, and percentage deviation were defined and quantitatively assessed. Results show bonded partially melted powder particles on downfacing surfaces resulting in higher average surface roughness of up to 41μm in these regions. Topological conformities in terms of the degree of layer-wise overlap, and the degree of overprint and underprint improve as the ratio of the relative density to cell length increases from 2 to 5 (% per mm) for a cell size of 5 mm.

Range Image-Aided Edge Line Estimation for Dimensional Inspection of Precast Bridge Slab Using Point Cloud Data

The accurate estimation of edge lines in precast bridge slabs based on laser scanning is crucial for a geometrical quality inspection. Normally, the as-designed model of precast slabs is used to match with laser scan data to estimate the edge lines. However, this approach often leads to an inaccurate quality measurement because the actually produced slab can be dimensionally different from the as-designed model or the inexistence of the as-designed model. In order to overcome this limitation, this study proposes a novel algorithm that generates and utilizes range images generated from scan points to enhance accuracy. The proposed algorithm operates as follows: first, the scan points are transformed into a range of images, and the corner points of these range images are extracted using a Harris corner detector. Next, the dimensions of the precast bridge slab are computed based on the extracted corner points. Consequently, the extracted corner points from the range images serve as an input for edge line estimation, thereby eliminating the matching errors that could arise when aligning collected scan points to an as-designed model. To evaluate the feasibility of the proposed edge estimation algorithm, a series of tests were conducted on both lab-scale specimens and field-scale precast slabs. The results showed promising accuracy levels of 1.22 mm for lab-scale specimens and 3.10 mm for field-scale precast bridge slabs, demonstrating more accurate edge line estimation results compared to traditional methods. These findings highlight the feasibility of employing the proposed image-aided geometrical inspection method, demonstrating the great potential for application in both small-scale and full-scale prefabricated construction elements within the construction industry, particularly during the fabrication stage.

Fatigue life of 3D-printed porous titanium dental implants predicted by validated finite element simulations.

Introduction: Porous dental implants represent a promising strategy to reduce failure rate by favoring osseointegration or delivering drugs locally. Incorporating porous features weakens the mechanical capacity of an implant, but sufficient fatigue strength must be ensured as regulated in the ISO 14801 standard. Experimental fatigue testing is a costly and time-intensive part of the implant development process that could be accelerated with validated computer simulations. This study aimed at developing, calibrating, and validating a numerical workflow to predict fatigue strength on six porous configurations of a simplified implant geometry. Methods: Mechanical testing was performed on 3D-printed titanium samples to establish a direct link between endurance limit (i.e., infinite fatigue life) and monotonic load to failure, and a finite element model was developed and calibrated to predict the latter. The tool was then validated by predicting the fatigue life of a given porous configuration. Results: The normalized endurance limit (10% of the ultimate load) was the same for all six porous designs, indicating that monotonic testing was a good surrogate for endurance limit. The geometry input of the simulations influenced greatly their accuracy. Utilizing the as-designed model resulted in the highest prediction error (23%) and low correlation between the estimated and experimental loads to failure (R2 = 0.65). The prediction error was smaller when utilizing specimen geometry based on micro computed tomography scans (14%) or design models adjusted to match the printed porosity (8%). Discussion: The validated numerical workflow presented in this study could therefore be used to quantitatively predict the fatigue life of a porous implant, provided that the effect of manufacturing on implant geometry is accounted for.

Quality assurance for building components through point cloud segmentation leveraging synthetic data

Quality Assurance and Quality Control (QA/QC) play a crucial role in the building project life cycle, especially during construction, as discrepancies between as-built structures and as-designed models can lead to cost overruns and schedule delays. Ensuring building quality is of utmost importance, but traditional manual inspections suffer from errors, consume time, and incur significant expenses. This paper describes a deviation detection method for building components using synthetic point clouds and semantic segmentation models. The method entails training a three-dimensional semantic segmentation model using synthetic point clouds generated from BIM to label each point with an object class, resulting in a mean IoU of 41.1% in semantic segmentation. Subsequently, real point clouds collected onsite are segmented using the same model and then compared with the synthetic point cloud to assess the disparities between the building components of the as-designed and as-built structures. This approach can improve the efficiency of QA/QC by reducing the manual workload of field inspectors.

Railway Bridge Geometry Assessment Supported by Cutting-Edge Reality Capture Technologies and 3D As-Designed Models

Documentation of structural visual inspections is necessary for its monitoring, maintenance, and decision about its rehabilitation, and structural strengthening. In recent times, close-range photogrammetry (CRP) based on unmanned aerial vehicles (UAVs) and terrestrial laser scanners (TLS) have greatly improved the survey phase. These technologies can be used independently or in combination to provide a 3D as-is image-based model of the railway bridge. In this study, TLS captured the side and bottom sections of the deck, while the CRP-based UAV captured the side and top sections of the deck, and the track. The combination of post-processing techniques enabled the merging of TLS and CRP models, resulting in the creation of an accurate 3D representation of the complete railway bridge deck. Additionally, a 3D as-designed model was developed based on the design plans of the bridge. The as-designed model is compared to the as-is model through a 3D digital registration. The comparison allows the detection of dimensional deviation and surface alignments. The results reveal slight deviations in the structural dimension with a global average value of 9 mm.

Intelligent Construction Monitoring Method for Large and Complex Steel Structures Based on Laser Point Cloud

Large and complex steel structures play a vital role in building construction. However, deviations between the design model and the actual construction state are inevitable, which seriously affects the quality and safety of building construction. In our study, an intelligent construction monitoring method for large and complex steel structures based on laser point cloud is proposed. Firstly, three-dimensional laser scanning technology is introduced to capture accurate and complete spatial information on steel structures. Then, considering the inconsistency of the coordinate system between the design model and the laser point cloud, the building information model (BIM) is converted into the point cloud model, and the datum unification of the two types of the point cloud is achieved by adopting a coarse-to-fine registration strategy. Finally, the spatial information of steel structures is extracted from the laser point cloud based on the as-designed model, and the distance deviation between the two models is analyzed to reflect the actual construction state. To demonstrate the applicability of the proposed method, the steel structures’ point cloud of the stadium and the high-speed railway station is captured by the terrestrial three-dimensional laser scanner. The experimental results demonstrate that the method can extract the deviation between the design model and the actual construction, to provide accurate data sources for the intelligent fine construction of steel structures.

TOWARDS REALISTIC NUMERICAL MODELLING OF THIN STRUT-BASED 3D-PRINTED STRUCTURES

AbstractThe as-built geometry and material properties of parts manufactured using Additive Manufacturing (AM) can differ significantly from the as-designed model and base material properties. These differences can be more pronounced in thin strut-like features (e.g., in a lattice structure), making it essential to incorporate them when designing for AM and predicting their structural behaviour. Therefore, the aim of this study is to develop a numerical model with realistic characteristics based on a thin strut-based test artefact and to use it accurately for estimating its compressive strength. Experiments on test samples produced by selective laser sintering in PA 1101, are used to calculate geometrical deviations, Young's modulus, and yield strength, which are used to calibrate the numerical model. The experimental and numerical results show that the numerical model incorporating geometrical and material deviations can accurately predict the peak load and the force-displacement behaviour. The main contributions of this paper include the design of the test artefact, the average geometrical deviation of the struts, the measured material data, and the developed numerical model.

A Digital Twin for Monitoring the Construction of a Wind Farm

Digital twins (DTs) represent an emerging technology that allows interaction between assets and their virtual replicas and enclose geometry from modeling procedures and dynamism from AI. DTs serve different purposes, e.g., testing how devices behave under diverse conditions or monitoring processes and supporting improvement. However, until now, the use of DTs for monitoring constructions has been limited, as they are frequently used only as a high-quality 3D digital representation without connecting to other systems, dynamic analysis, or simulation. This work proposes creating a DT for monitoring the construction of a wind farm. It draws a comparison between the as-designed models (from the design phase) and the as-built models (that represent the actual construction at different times). As a result, the DT can help to control deviations that may occur during construction. The authors propose using Unreal Engine to create an interface that includes as-designed models obtained from the building information modeling (BIM) and as-built models corresponding to different steps during the construction. The result is a video game-type interactive application with a timeline tool that allows going through the construction stages recorded in the as-built models and comparing them to the as-designed model.

Design framework for managing the life cycle of Kinematic Digital Twins

Mass customisation calls for new innovative solutions such as the Digital Twin (DT) concept. DT enhances the classical sequential design-manufacture-usage life cycle into a more flexible concept that can handle the changes stemming from the physical realization of the nominal design. One of the greatest challenges of realizing a Kinematic Digital Twin (KinDT), where movement modelling is of upmost importance, is the synchronisation of the as-built model with the as-designed model. Consequently, this paper proposes a design framework to support the synchronisation and feedback information within the DT. The usage of the framework is demonstrated in two robotic applications.

Review of energy performance gap and solutions in residential buildings

The energy consumption in the world due to economic development, rising population, and technological developments is growing, which has led to an increase in global warming. Therefore, there is a strong need to develop new strategies to reduce energy consumption. Buildings account for about 40% of global energy consumption. Since occupancy time in residential buildings is longer than in commercial and office buildings, residential buildings have a more significant impact on energy consumption. Due to this issue, efforts are being made to optimize energy consumption in residential buildings. Evaluating the performance of a building through building certificates that include as-designed and in-operation is essential for improving energy efficiency. Building energy simulations must be performed before construction to ensure that energy consumption in buildings is acceptable. However, it is observed that the energy consumption of the building after construction is higher than what was designed. This difference is defined as the energy performance gap, which indicates extra energy consumption or failure to comply with energy standards in the operation of the building. There are several reasons for the increased energy consumption of an operating building compared to the designed model. So far, studies have been conducted to determine the factors affecting the energy performance gap, but it is necessary to analyze these studies comprehensively. This article investigates the causes of energy performance gaps and ways to reduce this difference through a comprehensive literature review study. In this way, it is possible to achieve solutions in the building certification criteria that minimize the difference in energy consumption between the as-designed model and the in-operation mode of the building. This article can help decision-makers select the certificate that best fits their purposes.

An approach for monitoring prefabricated building construction based on feature extraction and point cloud segmentation

PurposeConstruction schedule delays and quality problems caused by construction errors are common in the field of prefabricated buildings. The effective monitoring of the construction project process is one of the key factors for the success of a project. How to effectively monitor the construction process of prefabricated building construction projects is an urgent problem to be solved. Aiming at the problems existing in the monitoring of the construction process of prefabricated buildings, this paper proposes a monitoring method based on the feature extraction of point cloud model.Design/methodology/approachThis paper uses Trimble X7 3D laser scanner to complete field data collection experiments. The point cloud data are preprocessed, and the prefabricated component segmentation and geometric feature measurement are completed based on the PCL platform. Aiming at the problem of noisy points and large amount of data in the original point cloud data, the preprocessing is completed through the steps of constructing topological relations, thinning, and denoising. According to the spatial position relationship and geometric characteristics of prefabricated frame structure, the segmentation algorithm flow is designed in this paper. By processing the point cloud data of single column and beam members, the quality of precast column and beam members is measured. The as-built model and as-designed model are compared to realize the visual monitoring of construction progress.FindingsThe experimental results show that the dimensional measurement accuracy of beam and column proposed in this paper is more than 95%. This method can effectively detect the quality of prefabricated components. In the aspect of progress monitoring, the visualization of real-time progress monitoring is realized.Originality/valueThis paper proposed a new monitoring method based on feature extraction of the point cloud model, combined with three-dimensional laser scanning technology. This method allows for accurate monitoring of the construction process, rapid detection of construction information, and timely detection of construction quality errors and progress delays. The treatment process based on point cloud data has strong applicability, and the real-time point cloud data transfer treatment can guarantee the timeliness of monitoring.

Automated BIM Reconstruction of Full-Scale Complex Tubular Engineering Structures Using Terrestrial Laser Scanning

Due to the accumulation of manufacturing errors of components and construction errors, there are always deviations between an as-built complex tubular engineering structure (CTES) and its as-designed model. As terrestrial laser scanning (TLS) provides accurate point cloud data (PCD) for scanned objects, it can be used in the building information modeling (BIM) reconstruction of as-built CTESs for life cycle management. However, few studies have focused on the BIM reconstruction of a full-scale CTES from missing and noisy PCD. To this end, this study proposes an automated BIM reconstruction method based on the TLS for a full-scale CTES. In particular, a novel algorithm is proposed to extract the central axis of a tubular structure. An extended axis searching algorithm is applied to segment each component PCD. A slice-based method is used to estimate the geometric parameters of curved tubes. The proposed method is validated through a full-scale CTES, where the maximum error is 0.92 mm.

A deep learning-based indoor acceptance system for assessment on flatness and verticality quality of concrete surfaces

Flatness and verticality quality assessment (FVQA) must be strictly controlled during the indoor acceptance testing. Existing FVQA methods using terrestrial laser scanning (TLS) rely on the as-designed model to assist in locating inspected objects, while sophisticated deep learning techniques excel in the object recognition. Therefore, based on the TLS and deep learning technique, this study presents a general indoor acceptance system including the indoor semantic segmentation, component surface segmentation, and FVQA. In particular, a dataset consisting of 145 sets of room point cloud data (PCD) is created to train the adopted point-based neural network. An image processing-based method is proposed to check the flatness and verticality of identified concrete surfaces. Experiments are conducted on two completed residences to verify the practicality and convenience of the proposed indoor acceptance system by comparing the calculated flatness and verticality with the field measurements. The experimental results show that the deep learning technique used in this study has a good recognition accuracy (over 85%) for large concrete surfaces in residential PCD, and the proposed indoor acceptance system provides more rigorous inspection results compared with the manual inspection method.

A semantic framework for on-site evacuation routing based on awareness of obstacle accessibility

The dynamic nature of construction sites increases the complexity of evacuation routing in emergency situation. The existing emergency response in construction site adopts manual management for monitoring which requires a lot of manpower and is prone to error. The Building Information Modeling (BIM) based evacuation routing methods is usually implemented by as-designed model without reflection of real scene. Therefore, this paper proposes an obstacle-aware framework for construction site to efficiently generate evacuation routing based on BIM, Computer Vision and Semantic Web, maintaining the consistency of model and real scene. The methodology is tested in a real construction site. Experiment shows that the awareness of obstacle accessibility supported by the developed semantic model can improve the accuracy of evacuation routing. Comparing with the existing evacuation routing methods, the proposed obstacle-aware framework improves the accuracy of evacuation routing and further provides a reference for other applications that need environmental awareness.

A digital twin approach for geometric quality assessment of as-built prefabricated façades

We develop a new framework for geometric quality assessment of as-built prefabricated façades during construction process using a 3D as-designed model and a 3D as-built semantic model. The framework is based on a digital twin approach which facilitates an automatic quantitative comparison between the 3D as-built digital replica, reconstructed from the 3D as-built lidar point cloud, and the 3D as-designed model. The framework consists of automated correspondence identification and element-based comparison on three new criteria, which ensure comprehensive measurement of the geometric quality of as-built façades regarding accuracy, completeness, and correctness. Experiments on a synthetic façade system and a prefabricated façade of an actual construction project demonstrate the ability of the framework to detect inconsistencies and perform a quantitative evaluation and localization of geometric errors in as-built prefabricated façades in an efficient and timely manner. Ultimately, the proposed framework enables efficient visual quality assessment during construction process for prefabricated construction.

An experimental and numerical investigation of compressive response of designed Schwarz Primitive triply periodic minimal surface with non-uniform shell thickness

Recently, Schwarz Primitive triply periodic minimal surface (P-TPMS) structures have emerged as high-value engineering structures for a wide range of applications. The elastic modulus and ultimate strength of P-TPMS architecture are tunable and superior. Herein, the influence of structural porosity and shell thickness on the compressive response of a P-TPMS lattice structure has been separately studied. Moreover, an enhanced design method, based on local shell thickening, is proposed to obtain a lightweight structure with superior mechanical properties. A comparison between primary and enhanced architectures is carried out by using experimental characterization and finite element analysis (FEA). It has been demonstrated that the enhanced structure renders higher relative elastic modulus and ultimate strength than primary P-TPMS structures. In addition, P-TPMS structural models are reconstructed from micro X-ray tomography (μ-CT) images and compared with as-designed models. The results reveal that selective laser sintering (SLS) is a promising fabrication route to achieve desired geometric accuracy. In addition, the comparison of experimental and FEA results indicates that the proposed enhanced design method is effective and reliable to obtain shell-based lattices with better mechanical properties.

Topology optimization with accessibility constraint for multi-axis machining

In this paper, we present a topology optimization (TO) framework to enable automated design of mechanical components while ensuring the result can be manufactured using multi-axis machining. Although TO improves the part’s performance, the as-designed model is often geometrically too complex to be machined and the as-manufactured model can significantly vary due to machining constraints that are not accounted for during TO. In other words, many of the optimized design features cannot be accessed by a machine tool without colliding with the part (or fixtures). The subsequent post-processing to make the part machinable with the given setup requires trial-and-error without guarantees on preserving the optimized performance. Our proposed approach is based on the well-established accessibility analysis formulation using convolutions in configuration space that is extensively used in spatial planning and robotics. We define an inaccessibility measure field (IMF) over the design domain to identify non-manufacturable features and quantify their contribution to non-manufacturability. The IMF is used to penalize the sensitivity field of performance objectives and constraints to prevent formation of inaccessible regions. Unlike existing discrete formulations, our IMF provides a continuous spatial field that is desirable for TO convergence. Our approach applies to arbitrary geometric complexity of the part, tools, and fixtures, and is highly parallelizable on multi-core architecture. We demonstrate the effectiveness of our framework on benchmark and realistic examples in 2D and 3D. We also show that it is possible to directly construct manufacturing plans for the optimized designs based on the accessibility information.

Selective laser melted Fe-Mn bone scaffold: microstructure, corrosion behavior and cell response

Iron metal possesses good biocompatibility and excellent mechanical strength, though it degrades too slowly. In this work, selective laser melting (SLM) was applied to fabricate iron-manganese (Fe-Mn) biodegradable scaffold. Results shown Fe-Mn scaffold exhibited a uniform pore structure with a porosity of 66.72 ± 2.3%, which highly matched with as-designed model. Phase analysis revealed Fe-Mn scaffold mainly contained α-Fe, martensitic and austenitic phases. Due to the potential difference among these different phases, galvanic corrosion occurred in Fe matrix. In addition, a small amount of Mn distributed at grain boundaries also contributed to the formation of galvanic corrosion. Thus, the corrosion rate increased from 0.09 ± 0.02 mm/year to 0.23 ± 0.05 mm/year. The scaffold exhibited suitable mechanical properties with a yield strength of 137 ± 8.4 MPa, an ultimate strength of 221.7 ± 10.9 MPa. Moreover, cell assays demonstrated its good cytocompatibility. Taking these positive results into consideration, SLM processed Fe-Mn scaffold was a promising material for bone repair application.