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• This paper focused on the reliability of settlement prediction of immersed tunnel. • This paper focused on impact of time-dependent effects on settlement prediction. • A time-dependent expression for foundation modulus is proposed. • Several extreme conditions are considered in the future settlement prediction. The focus of immersed tunnel research has primarily revolved around settlement monitoring or prediction. However, there has been a lack of discussion regarding the time-dependent effects on settlement prediction, which could lead to obvious misjudgments in subsequent maintenance work. This paper addresses this issue by applying the physics-informed machine learning (PIML) algorithm to conduct an inverse analysis of the foundation modulus of the Hong Kong-Zhuhai-Macau Bridge (HZMB) tunnel, thereby identifying its variation tendency with time. The proposed time-dependent expression of foundation modulus demonstrates that the time-dependent effect is related to both post-construction settlement and the accumulation of back silting, which is significant and could persist throughout the entire service life of the HZMB tunnel. In further studies on settlement prediction for the HZMB tunnel, the results show that errors in prediction can exceed 30 mm at certain monitoring points. This error can be mitigated by considering the time-dependent effect, thereby significantly enhancing the reliability of predictions. [ABSTRACT FROM AUTHOR]

This paper reports a new knowledge-driven engineered feature learning approach in response to the Global AI Challenge for Building E&M Facilities held by the Electrical and Mechanical Service Department (EMSD) of the Hong Kong SAR. The results were awarded with a Grand Prize by the competition organizer. A dynamically engineered multi-modal feature learning (DEMMFL) method is proposed for predicting the cooling load of two office buildings. The DEMMFL model is estimated with the Lasso-ridge regression and compared with other well-known methods such as the Lasso. The novel approach applies control system knowledge to engineer useful features and explore load patterns for multi-mode modeling. Deep learning methods including LSTM, GRU, and AutoGluon are implemented for automated machine learning and tested in parallel to compare the performance of the proposed model with existing methods. The proposed model is demonstrated to predict long-term cooling load most accurately using engineered features from weather information only. • A new dynamic feature engineering method for office building cooling load prediction. • Slow and fast dynamic features engineered from available weather data only. • Control-knowledge based feature to learn on-hours dynamic responses. • Multi-modal approach with eight day-type features to learn occupancy loads. • Deep recurrent learning and AutoML methods compared to the proposed method. [ABSTRACT FROM AUTHOR]

Representing building layouts as graphs can extract critical design patterns that would facilitate space syntax analyses as well as design mining and automation but traditional approaches (e.g., non-weighted adjacent graphs) encountered problems in modular buildings, as they are largely shaped under the principle of 'modularity' rather than freeform cast in-situ elements. This paper attempts to develop a novel analytical tool called ModularGraph to represent modular building layouts (MBLs) as graphs considering their unique adjacency, connectivity, and conjoint relationships in a triumvirate. It does so by developing a prototype then applying it to 36 modular buildings for iteration, finetuning, and finalizing. It is found that ModularGraph can effectively translate heterogeneous forms of MBLs into unified graph-based representations with rich graphic and semantic information. This study not only contributes an innovative graph analytic tool for design pattern mining, but also lays a stepping stone towards generative AI for modular building design. • Propose a graph triumvirate structure for modular building layout representation. • Incorporate a weight mechanism to indicate the strength of spatial relationships for better semantic interpretations. • Perform a quantitative analysis on the layouts of 36 modular buildings in Hong Kong using the proposed framework. • Uncover patterns in modular building layout designs exemplified by ModularGraph. • Develop a machine learning model to learn knowledge from layout designs represented by ModularGraph. [ABSTRACT FROM AUTHOR]

Active school commuting makes a vital contribution to physical activity, thus improving the health and well-being for children and adolescents. The built environment is widely acknowledged as a factor that can affect travel behavior. However, few studies assess the influence of micro-level streetscape features on active school commuting using street view images. Additionally, existing research often presupposes that the relationship between built environment and walking to school is linear or generalized linear. Using data from the 2016 census and street view images in Hong Kong, this paper investigates the non-linear relationship between the micro-level streetscape features and propensity of walking to school with several machine learning methods. A refined evaluation of streetscape features is conducted through semantic segmentation, object detection and perceptual color analysis. The results show that the non-linear model facilitates a deeper understanding of the genuine relationship between streetscape features and propensity of walking to school. Grass view index, mean saturation of buildings and number of traffic lights have greater significance in predicting walking propensity. Moreover, some of the streetscape features have salient threshold effects, indicating that environmental interventions would only be effective within a specific range. These findings can provide nuanced and fine-grained guidance for building a walkable, children-friendly and sustainable city. • Machine learning techniques are applied to explore the non-linear relationship. • Streetscape features exhibit salient non-linear and threshold effects on walking propensity. • The hierarchy of priorities and most effective ranges for environmental intervention are revealed. [ABSTRACT FROM AUTHOR]

Multipath and non-light-of-sight (NLOS) reception greatly deteriorate the GNSS positioning accuracy in urban areas. Currently, most of the attempts using machine learning in this area focus on signal status prediction (LOS, multipath, NLOS). This paper exploits the capability of deep learning in multipath/NLOS mitigation. A new input feature, the single-differenced residual map is proposed, which has a high correlation with the user location and is very effective in multipath/NLOS mitigation. Combining the domain knowledge in GNSS, features of residual maps from different satellites are extracted by the proposed network and generate the heat map to indicate the user location. The proposed network can significantly improve the positioning accuracy of 84% of the epochs in the dense urban to 5-meter level. In addition, our network has a superior generalization ability, reducing the error of 90% of the epochs to 7 m level in a new scenario. • A deep learning network structure to directly estimate urban GNSS positioning. • A new GNSS feature, single-differenced residual maps, is proposed to improve the positioning accuracy of the neural network. • The proposed network is validated and tested using real GNSS raw data collected in urban canyons at Hong Kong. [ABSTRACT FROM AUTHOR]

In the era of big data, data is increasingly driving the construction waste management (CWM) for minimizing the impacts on the environment and recycling construction materials. However, missing data, led by various information barriers, often undermines the decision-making and hinders effective CWM. This paper applies aggregated behavior-based Machine Learning (ML) methods to handling the project-level 'Missing Not At Random' (MNAR) data by using aggregated waste generation behaviors as a case study. First, we define a set of 821 waste generation behavioral features based on waste big data, then screen the indicative and decisive behaviors using automatic feature selection. Then, the most predictive ML method, trained via data of 2,451 construction projects in 2011–2016 in Hong Kong, is selected for handling the MNAR data. The experiments showed that the prediction of project missing data was satisfactory (validation F 1 = 0.87, test F 1 = 0.80). The contribution of this paper is to pinpoint the potential of waste big data in portraying project behaviors for more value-added applications, at the same time, to present a handling method for MNAR data that is automatic, fast, and low-cost from the CWM practitioner's perspective. [Display omitted] [ABSTRACT FROM AUTHOR]

• Predicting departure delay in parallel may cause ambiguity in decision making. • A novel hierarchical integrated machine learning model helps to improve prediction. • The historical data provided by an international airline was used for the study. • The model is the best alternative to regression and multiclass classification. Flight delays may propagate through the entire aviation network and are becoming an important research topic. This paper proposes a novel hierarchical integrated machine learning model for predicting flight departure delays and duration in series rather than in parallel to avoid ambiguity in decision making. The paper analyses the proposed model using various machine learning algorithms in combination with different sampling techniques. The highly noisy, unbalanced, dispersed, and skewed historical high dimensional data provided by an international airline operating in Hong Kong was used to demonstrate the practical application of the model. The result shows that for a 4-h forecast horizon, a constructive neural network machine learning algorithm with the Synthetic Minority Over Sampling Technique-Tomek Links (SMOTETomek) sampling technique was able to achieve better average balanced recall accuracies of 65.5%, 61.5%, 59% for classifying delay status and predicting delay duration at thresholds of 60 min and 30 min, respectively. Similarly, for minority labels, the precision-recall and area under the curve showed that the proposed model achieved better results of 32.44% and 35.14% compared to the parallel model of 26.43% and 21.02% for thresholds of 60 min and 30 min, respectively. The effect of different sampling techniques, sampling approaches, and estimation mechanisms on prediction performance is also studied. [ABSTRACT FROM AUTHOR]

The paper studies the horizontal global, direct-beam and sky-diffuse solar irradiance data measured in Hong Kong from 2008 to 2013. A machine learning algorithm was employed to predict the horizontal sky-diffuse irradiance and conduct sensitivity analysis for the meteorological variables. Apart from the clearness index (horizontal global/extra atmospheric solar irradiance), we found that predictors including solar altitude, air temperature, cloud cover and visibility are also important in predicting the diffuse component. The mean absolute error ( MAE ) of the logistic regression using the aforementioned predictors was less than 21.5 W/m 2 and 30 W/m 2 for Hong Kong and Denver, USA, respectively. With the systematic recording of the five variables for more than 35 years, the proposed model would be appropriate to estimate of long-term diffuse solar radiation, study climate change and develope typical meteorological year in Hong Kong and places with similar climates. [ABSTRACT FROM AUTHOR]

This paper describes the prediction of settlements induced by urban area tunneling using five machine learning (ML) algorithms. The settlement database, which was collected from a subway tunnel project in Hong Kong, consisted of 253 settlement measurements and 32 settlement influencing factors. The Bayesian optimization-based hyperparameter tuning was applied to efficiently explore optimal combinations and to enhance prediction performance. The optimal hyperparameters were selected by considering the three-fold cross-validation (CV) result of training data. The performance of the developed model was evaluated by comparing the root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) values. The extreme gradient boosting algorithm demonstrated the highest prediction accuracy with RMSE, MAE, and R2 values of 1.606, 1.331, and 0.835, respectively. • Tunneling-induced surface settlements predicted with five machine learning algorithms. • Bayesian optimization demonstrated excellent hyperparameter tuning search ability. • Prediction performance of trained models validated using three-fold cross-validation. • Consistency of prediction results validated with acceptable residual errors. • XGB model achieved the highest prediction performance with RMSE of 1.606. [ABSTRACT FROM AUTHOR]

Abstract: In recent years, Gaussian process (GP) models have been popularly studied to solve hard machine learning problems. The models are important due to their flexible non-parametric modeling abilities using Mercer kernels and the Bayesian framework for probabilistic inference. In this paper, we propose a sparse GP regression (GPR) model for tourism demand forecasting in Hong Kong. The sparsification procedure of the GPR model not only decreases the computational complexity but also improves the generalization ability. We experiment the proposed model with monthly demand data that are relevant to Hong Kong’s tourism industry, and compare the performance of the sparse GPR model with those of various kernel-based models to show its effectiveness. The proposed sparse GPR model shows that its forecasting capability outperforms those of the ARMA model and the two state-of-the-art SVM models. [Copyright &y& Elsevier]

Abstract: Harmful algal blooms, which are considered a serious environmental problem nowadays, occur in coastal waters in many parts of the world. They cause acute ecological damage and ensuing economic losses, due to fish kills and shellfish poisoning as well as public health threats posed by toxic blooms. Recently, data-driven models including machine-learning (ML) techniques have been employed to mimic dynamics of algal blooms. One of the most important steps in the application of a ML technique is the selection of significant model input variables. In the present paper, we use two extensively used ML techniques, artificial neural networks (ANN) and genetic programming (GP) for selecting the significant input variables. The efficacy of these techniques is first demonstrated on a test problem with known dependence and then they are applied to a real-world case study of water quality data from Tolo Harbour, Hong Kong. These ML techniques overcome some of the limitations of the currently used techniques for input variable selection, a review of which is also presented. The interpretation of the weights of the trained ANN and the GP evolved equations demonstrate their ability to identify the ecologically significant variables precisely. The significant variables suggested by the ML techniques also indicate chlorophyll-a (Chl-a) itself to be the most significant input in predicting the algal blooms, suggesting an auto-regressive nature or persistence in the algal bloom dynamics, which may be related to the long flushing time in the semi-enclosed coastal waters. The study also confirms the previous understanding that the algal blooms in coastal waters of Hong Kong often occur with a life cycle of the order of 1–2 weeks. [Copyright &y& Elsevier]

[Display omitted] • The communal areas can have different levels of energy performance in the 15 types of public residential buildings under study. • Spatial design features can evidently affect the energy consumption in the communal areas. • The ability to harvest daylight serves as a key determinant of energy efficiency. • Both econometric and machine learning techniques are proved to be effective to generate reliable findings. In this paper, we construct a unique database for 1228 residential buildings in Hong Kong to investigate how the spatial features of these residential buildings affect the electricity consumption in the communal area. We choose Hong Kong for this analysis as the city owns a large number of standard-type residential buildings managed by the public institution, which could be affected strongly by environmental policies. Both the machine learning method, based on the Least Absolute Shrinkage and Selection Operator (LASSO), and econometric regressions are adopted to analyse the data. We first utilize the machine learning LASSO technique to identify the most relevant factors for the subsequent econometric analysis. Our results show that the electricity demand for relatively low consumption building types, such as Twin Tower, is 6% lower than that of the high consumption building types. Newly constructed buildings usually belong to the medium consumption types, with the estimated monthly electricity consumption per apartment in communal areas to be around 50.2 kWh on average in 2020. These findings shed light on the nexus between spatial features and energy use for complex buildings, potentially contributing to the better crafting of energy-saving policy and the improvement of residential building programmes. [ABSTRACT FROM AUTHOR]

Landslide susceptibility assessment is essential for regional landslide risk assessment and mitigation. Most past studies involved cell-based analysis that takes landslide incidents as geo-spatial points. Nevertheless, given that a landslide is a two-dimensional polygon on maps and a three-dimensional object in the real world, an object-wise assessment is more logical. Fusing with artificial intelligence (AI) techniques, this paper proposes a novel AI-powered object-based landslide susceptibility assessment method to address this issue. First, landslide and non-landslide objects are defined based on an optimal object size determined by statistics of historical landslides. Next, landslide and non-landslide samples are constructed by integrating geoenvironmental data layers derived from multi-source data. Subsequently, AI techniques are applied to learn susceptibility prediction based on the prepared samples. To illustrate the proposed method, a comprehensive case study of Hong Kong is conducted, in which six AI algorithms are evaluated including logistic regression (area under curve, AUC = 0.949), random forest (AUC = 0.951), LogitBoost (AUC = 0.958), convolutional neural network (CNN) (AUC = 0.966), bidirectional long short-term memory architecture of recurrent neural network (BiLSTM-RNN) (AUC = 0.966), and CNN-LSTM (AUC = 0.972), among which the BiLSTM-RNN and CNN-LSTM algorithms are applied in landslide susceptibility assessment for the first time. Results confirm that the proposed object-based method outperforms the traditional cell-based method significantly. Equally importantly, the case study produced the first set of AI-based territory-wide landslide susceptibility maps for Hong Kong. These maps can be used as a fundamental tool for quantifying natural terrain landslide risk and identifying susceptible zones where landslide mitigation measures may be needed. • A novel artificial intelligence and object-based landslide susceptibility method is proposed and validated. • The first set of territory-wide landslide susceptibility maps for Hong Kong is produced based on AI techniques. • CNN-LSTM and BiLSTM-RNN are successfully applied to landslide susceptibility analysis for the first time. • The proposed AI-powered object-based method outperforms the cell-based method significantly. [ABSTRACT FROM AUTHOR]