The construction industry has been associated with inefficiencies. In contrast, Off-Site Construction (OSC) is a modern method of construction that has demonstrated significant improvements over conventional on-site methods. Despite that, OSC represents a tiny portion of the construction industry with a limited rate of diffusion and acceptance. One reason for that is associated with the lack or immaturity of OSC-related research and innovation benchmarking. This benchmarking helps in expanding OSC implementation as a component in driving and directing OSC research as well as roadmapping and measuring the innovation advancements. Hence, this study was intended to contribute to the OSC benchmarking by mapping innovation that paves the road towards building a strategic research and innovation roadmap in OSC. Among different innovation types, this study is limited to two types: technology-oriented and OSC method-oriented innovation. Unlike the traditional roadmaps in the literature, the envisioned roadmap design for OSC innovation in this study is based on maturity modelling. This design includes four components: framework, maturity scales, benchmarks, and targets. However, the focus of the current stage is on the developing the mapping components (framework and maturity scales). Consequently, two sets of frameworks and maturity models were developed to realize the two identified innovation types in OSC. The applicability of these frameworks and scales was demonstrated through hypothetical examples and a case study that is limited to technology-oriented research in the Canadian context. Accordingly, the subsequent case study scope embraces the last three research community meetings (2015-2019) relevant to our study in the indicated context. Based on this case study, the framework was found easy to understand, simple to implement, scalable, applicable across different contexts, and facilitates capturing benchmarks and targets. This confirms promising benefits of the developed frameworks and their effectiveness in roadmapping OSC innovation.

Twitter has attracted the attention of many re-searchers owing to the fact that every tweet is, by default, public in nature which is not the case with Facebook. In this paper, we present sentiment analysis of tweets written in English, belonging to different telecommunication companies in Saudi Arabia. We apply different machine learning algorithms such as k nearest neighbor algorithm, Artificial Neural Networks (ANN), Na¨ive Bayesian etc. We classified the tweets into positive, negative and neutral classes based on Euclidean distance as well as cosine similarity. Moreover, we also learned similarity matrices for kNN classification. CfsSubsetEvaluation as well as Information Gain was used for feature selection. The results of CfsSubsetEvaluation were better than the ones obtained with Information Gain. Moreover, kNN performed better than the other algorithms and gave 75.4%, 76.6% and 75.6% for Precision, Recall and F-measure, respectively. We were able to get an accuracy of 80.1%with a symmetric variant of kNN while using cosine similarity. Furthermore, interesting trends wrt days, months etc. were also discovered.

The use of disparity information is computationally coste-ffective for 3D-assisted mapping applications. However, off-nadir satellite images over dense urban areas suffer from severe occlusion. Therefore, large gaps will be created due to the unsuccessful matching in the occluded areas. Commonly, gap interpolation produces misleading information that destroys the quality of the subsequent information extraction application. Hence, the more reliable solution is to fill the occlusion gaps by supplementary data. However, disparity maps are the co-relation of one stereo pair. Hence, supplementary disparity maps cannot be directly generated and applied. Thus, this paper introduces the Double Projection Plane (DPP) method for constructing disparity proportionality among multi-view stereo satellite images and calculating analytically the transferring scales required for producing supplementary disparity data. Accordingly, based on multi-view stereo satellite images, this method is promising to provide gap-and-outlier-free disparity maps that have the potential to replace the need for elevation models to some extent.

Digital Twins (DTs) of buildings can generate large volumes of dynamic data from various sources (e.g., sensors and IoT devices), enabling real-time representation of physical building states in a digital environment. Although machine learning (ML) techniques are increasingly used to predict building energy consumption from this DT data, existing approaches often lack scalability in handling data growth (data scalability) and/or adapting to evolving data patterns (model scalability). This study aims to address both drawbacks by developing a scalable cloud-based framework for the prediction of the building energy consumption. A key contribution to the field is the inclusion of a “monitoring and maintenance” module, which continuously evaluates model performance and triggers retraining only when needed. This enables timely adaptation of the ML model while avoiding unnecessary retraining and the associated computational costs. The framework was implemented and tested in a case study of a commercial building for 90 days, demonstrating its applicability. In a practical setting, the developed model could detect anomalies in time when the accuracy declined below the set threshold (70%) for five days and prevented unnecessary retraining of ML models. The findings support the feasibility of using cloud-based approaches to implement scalable ML models for energy prediction in buildings.

Third-dimension information is of a great importance for several remote sensing applications, such as building detection. The main data-source for these applications is very high resolution (VHR) satellite images which allow detailed mapping of complex environments. Stereo VHR satellite images allow the extraction of two correlated types of third-dimension information: disparity and elevation information. While the disparity is measured directly, the elevation information is derived computationally. To measure the disparity information, two overlapped images are matched. However, for the backward and forward off-nadir VHR stereo images, building facades occlude areas and hence create many data gaps. When the disparity is required to represent only the off-terrain objects, interpolation and normalization techniques are typically used. However, in dense urban environments, these techniques destroy the quality of the generated data. Therefore, this paper proposes a registration-based technique to measure the disparity of the above-ground objects. The technique includes constructing epipolar images and registering them using common terrain- level features to allow direct disparity mapping for the off-terrain objects. After the implementation, the negative effects of occlusion in the off-nadir VHR stereo images are mitigated through direct disparity mapping of the above-ground objects and bypassing the interpolation and normalization steps.