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This study aims to achieve superior localization and mapping performance in point cloud degradation scenarios through the effective removal of dynamic obstacles. With the continuous development of various technologies for autonomous vehicles, the LIDAR-based Simultaneous localization and mapping (SLAM) system is becoming increasingly important. However, in SLAM systems, effectively addressing the challenges of point cloud degradation scenarios is essential for accurate localization and mapping, with dynamic obstacle removal being a key component.

This paper proposes a method that combines adaptive feature extraction and loop closure detection algorithms to address this challenge. In the SLAM system, the ground point cloud and non-ground point cloud are separated to reduce the impact of noise. And based on the cylindrical projection image of the point cloud, the intensity features are adaptively extracted, the degradation direction is determined by the degradation factor and the intensity features are matched with the map to correct the degraded pose. Moreover, through the difference in raster distribution of the point clouds before and after two frames in the loop process, the dynamic point clouds are identified and removed, and the map is updated.

Experimental results show that the method has good performance. The absolute displacement accuracy of the laser odometer is improved by 27.1%, the relative displacement accuracy is improved by 33.5% and the relative angle accuracy is improved by 23.8% after using the adaptive intensity feature extraction method. The position error is reduced by 30% after removing the dynamic target.

Compared with LiDAR odometry and mapping algorithm, the method has greater robustness and accuracy in mapping and localization.

Facility management gained profound importance due to the increasing complexity of different systems and the cost of operation and maintenance. However, due to the increasing complexity of different systems, facility managers may suffer from a lack of information. The purpose of this paper is to propose a new facility management approach that links segmented assets to the vital data required for managing facilities.

Automatic point cloud segmentation is one of the most crucial processes required for modelling building facilities. In this research, laser scanning is used for point cloud acquisition. The research utilises region growing algorithm, colour-based region-growing algorithm and Euclidean cluster algorithm.

A case study is worked out to test the accuracy of the considered point cloud segmentation algorithms utilising metrics precision, recall and F-score. The results indicate that Euclidean cluster extraction and region growing algorithm revealed high accuracy for segmentation.

The research presents a comparative approach for selecting the most appropriate segmentation approach required for accurate modelling. As such, the segmented assets can be linked easily with the data required for facility management.

As laser scanning technology becomes readily available and affordable, there is an increasing demand of using point cloud data collected from a laser scanner to create as-built building information modeling (BIM) models for quality assessment, schedule control and energy performance within construction projects. To enhance the as-built modeling efficiency, this study explores an integrated system, called Auto-Scan-To-BIM (ASTB), with an aim to automatically generate a complete Industry Foundation Classes (IFC) model consisted of the 3D building elements for the given building based on its point cloud without requiring additional modeling tools.

ASTB has been developed with three function modules. Taking the scanned point data as input, Module 1 is built on the basis of the widely used region segmentation methodology and expanded with enhanced plane boundary line detection methods and corner recalibration algorithms. Then, Module 2 is developed with a domain knowledge-based heuristic method to analyze the features of the recognized planes, to associate them with corresponding building elements and to create BIM models. Based on the spatial relationships between these building elements, Module 3 generates a complete IFC model for the entire project compatible with any BIM software.

A case study validated the ASTB with an application with five common types of building elements (e.g. wall, floor, ceiling, window and door).

First, an integrated system, ASTB, is developed to generate a BIM model from scanned point cloud data without using additional modeling tools. Second, an enhanced plane boundary line detection method and a corner recalibration algorithm are developed in ASTB with high accuracy in obtaining the true surface planes. At last, the research contributes to develop a module, which can automatically convert the identified building elements into an IFC format based on the geometry and spatial relationships of each plan.

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.

Using building information modelling (BIM) technology, a conventional structure in this study was converted into a green building to measure its energy usage and CO2 emissions.

Digital images of the existing building conditions were captured using unmanned aerial vehicle (UAV), and were fed into Meshroom to generate the building’s geometry for 3D parametric model development. The model for the existing conventional building was created and converted to an energy model and exported to gbXML in Autodesk Revit for a whole building analysis which was carried out in the Green Building Studio (GBS). In the GBS, the conventional building was retrofitted into a green building to explore their energy consumption and CO2 emission.

By comparing the green building model to the conventional building model, the research found that the green building model saved 25% more energy while emitting 46.8% less CO2.

The study concluded that green building reduces energy consumption, thereby reducing the emission of CO2 into the environment. It is recommended that buildings should be simulated at the design stage to know their energy consumption and carbon emission performance before construction.

Occupant satisfaction, operation cost and environmental safety are essential for sustainable or green buildings. Green buildings increase the standard of living and enhance indoor air quality.

This investigation aided in a pool of information on how to use BIM methodology to retrofit existing conventional buildings into green buildings, showing how green buildings save the environment as compared to conventional buildings.

Recent United Nations Climate Change Conferences recognise extreme climate change of heatwaves, floods and droughts as threatening risks to the resilience and success of public–private partnership (PPP) infrastructure projects. Such conferences together with available project reports and empirical studies recommend project managers and practitioners to adopt smart technologies and develop robust measures to tackle climate risk exposure. Comparatively, artificial intelligence (AI) risk management tools are better to mitigate climate risk, but it has been inadequately explored in the PPP sector. Thus, this study aims to explore the tools and roles of AI in climate risk management of PPP infrastructure projects.

Systematically, this study compiles and analyses 36 peer-reviewed journal articles sourced from Scopus, Web of Science, Google Scholar and PubMed.

The results demonstrate deep learning, building information modelling, robotic automations, remote sensors and fuzzy logic as major key AI-based risk models (tools) for PPP infrastructures. The roles of AI in climate risk management of PPPs include risk detection, analysis, controls and prediction.

For researchers, the findings provide relevant guide for further investigations into AI and climate risks within the PPP research domain.

This article highlights the AI tools in mitigating climate crisis in PPP infrastructure management.

This article provides strong arguments for the utilisation of AI in understanding and managing numerous challenges related to climate change in PPP infrastructure projects.

This article sets out to show how principles and questions about method that underlie a way of interpreting the discipline of conservation and restoration can find results in research and studies, aiming at achieving even conscious reuse process. The occasion is the very recent research performed on the former Church of Saints Gerolamo and Francesco Saverio in Genoa, Italy, the Jesuit church annexed to the 17th-century College of the order. It is a small Baroque jewel in the heart of the ancient city, former University Library and actually abandoned, forgotten for years, inaccessible and awaiting a new use.

The two-year work carried out on the monumental building was conducted according to a study and research methodology developed and refined over the years within the activities of the School of Specialisation in Architectural Heritage and Landscape of the University of Genoa. It is a multidisciplinary and rigorous approach, which aims to train high-level professionals, up-to-date and aware of the multiple problems that interventions on existing buildings, especially of a monumental nature, involve.

The biennal study has been carried out within the activities of the Post-Graduate Programme in Architectural Heritage and Landscape of the University of Genoa. The work methodology faces the challenges of the contemporary complexity, raised by the progressive broadening of the concept of cultural “heritage” and by the problems of its conservation, its active safeguard and its reuse: safety in respect of seismic risk, fire and hydro geological instability, universal accessibility – cognitive, physical and alternative – resource efficiency, comfort and savings in energy consumption, sustainability, communication and involvement of local communities and stakeholders.

The goals of the work were the following: understanding of the architectural heritage, through the correlated study of its geometries, elements and construction materials, surfaces, structures, spaces and functions; understanding of the transformations that the building has undergone over time, relating the results of historical reconstructions from indirect sources and those of direct archaeological analysis; assessment of the state of conservation of the building recognising phenomena of deterioration, damage, faults and deficits that affect materials, construction elements, systems and structures; identification of the causes and extent of damage, faults and deficits, assessing the vulnerability and level of exposure of the asset to the aggression of environmental factors and related risks; evaluation of the compatibility between the characteristics of the available spaces, the primary needs of conservation, the instance of regeneration and possible new uses; the definition of criteria and guidelines for establishing the planning of conservation, restoration and redevelopment interventions.

Time of flight (ToF) imaging is a promising emerging technology for the purposes of crop identification. This paper aim to presents localization system for identifying and localizing asparagus in the field based on point clouds from ToF imaging. Since the semantics are not included in the point cloud, it contains the geometric information of other objects such as stones and weeds other than asparagus spears. An approach is required for extracting the spear information so that a robotic system can be used for harvesting.

A real-time convolutional neural network (CNN)-based method is used for filtering the point cloud generated by a ToF camera, allowing subsequent processing methods to operate over smaller and more information-dense data sets, resulting in reduced processing time. The segmented point cloud can then be split into clusters of points representing each individual spear. Geometric filters are developed to eliminate the non-asparagus points in each cluster so that each spear can be modelled and localized. The spear information can then be used for harvesting decisions.

The localization system is integrated into a robotic harvesting prototype system. Several field trials have been conducted with satisfactory performance. The identification of a spear from the point cloud is the key to successful localization. Segmentation and clustering points into individual spears are two major failures for future improvements.

Most crop localizations in agricultural robotic applications using ToF imaging technology are implemented in a very controlled environment, such as a greenhouse. The target crop and the robotic system are stationary during the localization process. The novel proposed method for asparagus localization has been tested in outdoor farms and integrated with a robotic harvesting platform. Asparagus detection and localization are achieved in real time on a continuously moving robotic platform in a cluttered and unstructured environment.

Monitoring of the quality of precast concrete (PC) components is crucial for the success of prefabricated construction projects. Currently, quality monitoring of PC components during the construction phase is predominantly done manually, resulting in low efficiency and hindering the progress of intelligent construction. This paper presents an intelligent inspection method for assessing the appearance quality of PC components, utilizing an enhanced you look only once (YOLO) model and multi-source data. The aim of this research is to achieve automated management of the appearance quality of precast components in the prefabricated construction process through digital means.

The paper begins by establishing an improved YOLO model and an image dataset for evaluating appearance quality. Through object detection in the images, a preliminary and efficient assessment of the precast components' appearance quality is achieved. Moreover, the detection results are mapped onto the point cloud for high-precision quality inspection. In the case of precast components with quality defects, precise quality inspection is conducted by combining the three-dimensional model data obtained from forward design conversion with the captured point cloud data through registration. Additionally, the paper proposes a framework for an automated inspection platform dedicated to assessing appearance quality in prefabricated buildings, encompassing the platform's hardware network.

The improved YOLO model achieved a best mean average precision of 85.02% on the VOC2007 dataset, surpassing the performance of most similar models. After targeted training, the model exhibits excellent recognition capabilities for the four common appearance quality defects. When mapped onto the point cloud, the accuracy of quality inspection based on point cloud data and forward design is within 0.1 mm. The appearance quality inspection platform enables feedback and optimization of quality issues.

The proposed method in this study enables high-precision, visualized and automated detection of the appearance quality of PC components. It effectively meets the demand for quality inspection of precast components on construction sites of prefabricated buildings, providing technological support for the development of intelligent construction. The design of the appearance quality inspection platform's logic and framework facilitates the integration of the method, laying the foundation for efficient quality management in the future.

The purpose of this study is to explore the suggestions that construction processes could be considerably improved by integrating building information modelling (BIM) with 3D laser scanning technologies. This case study integrated 3D laser point cloud scans with BIM to explore the effects of BIM adoption on ongoing construction project, whilst evaluating the utility of 3D laser scanning technology for producing structural 3D models by converting point cloud data (PCD) into BIM.

The primary data acquisition adopted the use of Trimble X7 laser scanning process, which is a set of data points in the scanned space that represent the scanned structure. The implementation of BIM with the 3D PCD to explore the precision and effectiveness of the construction processes as well as the as-built condition of a structure was precisely captured using the 3D laser scanning technology to recreate accurate and exact 3D models capable of being used to find and fix problems during construction.

The findings indicate that the integration of BIM and 3D laser scanning technology has the tendency to mitigate issues such as building rework, improved project completion times, reduced project cost, enhanced interdisciplinary communication, cooperation and collaboration amongst the project duty holders, which ultimately enhances the overall efficiency of the construction project.

The acquisition of data using 3D laser scanner is usually conducted from the ground. Therefore, certain aspects of the building could potentially disturb data acquisition; for example, the gable and sections of eaves (fascia and soffit) could be left in a blind spot. Data acquisition using 3D laser scanner technology takes time, and the processing of the vast amount of data acquired is laborious, and if not carefully analysed, could result in errors in generated models. Furthermore, because this was an ongoing construction project, material stockpiling and planned construction works obstructed and delayed the seamless capture of scanned data points.

These findings highlight the significance of integrating BIM and 3D laser scanning technology in the construction process and emphasise the value of advanced data collection methods for effectively managing construction projects and streamlined workflows.