Project cost forecasting is a complex and critical process, and it is of paramount importance for the successful implementation of engineering projects. Accurately forecasting project costs can help project managers and relevant decision-makers make informed decisions, thereby avoiding unnecessary cost overruns and time delays. Furthermore, accurately forecasting project costs can make important contributions to better controlling engineering costs, optimizing resource allocation, and reducing project risks. To establish a high-precision cost forecasting model for construction projects in Guangdong Province, based on case data of construction projects in Guangdong Province, this paper first uses the Analytic Hierarchy Process (AHP) to obtain the characteristic parameters that affect project costs. Then, a neural network training and testing dataset is constructed, and a genetic algorithm (GA) is used to optimize the initial weights and biases of the neural network. The GA-BP neural network is used to establish a cost forecasting model for construction projects in Guangdong Province. Finally, by using parameter sensitivity analysis theory, the importance of the characteristic values that affect the project cost is ranked, and the optimal direction for controlling the project cost is obtained. The results showed: (1) The determination coefficient between the forecasting and actual values of the project cost forecasting model based on the BP neural network testing set is 0.87. After GA optimization, the determination coefficient between the forecasting and actual values of the GA-BP neural network testing set is 0.94. The accuracy of the intelligent forecast model for construction project cost in Guangdong Province has been significantly improved after optimization through GA. (2) Based on sensitivity analysis of neural network parameters, the most significant factor affecting the cost of construction projects in Guangdong Province is the number of above-ground floors, followed by the main structure type, foundation structure, above-ground building area, total building area, underground building area, fortification intensity, and building height. The results of parameter sensitivity analysis indicate the direction for cost control in construction projects. The research results of this paper provide theoretical guidance for cost control in construction projects.

Biomass energy is recognized as a clean and sustainable energy source and is leveraged as a key enabler for driving the low-carbon transition of the energy system and achieving sustainable development. The higher heating value of solid biomass fuels (HHV-SBF) is a key parameter in its catalytic conversion process, and HHV-SBF is of great significance for catalyst design and matching, as well as the selection of reaction process parameters. To address the limitations in accuracy and generalization capability of traditional prediction methods for estimating the HHV-SBF, a dataset is constructed in this study that correlates chemical elements, proximate analysis parameters, and biochemical components with the HHV-SBF. Key hyperparameters of the deep neural network (DNN) are optimized using the Bayesian optimization algorithm. A Bayesian optimization-based deep neural network (BO-DNN) model is developed for the intelligent prediction of the HHV-SBF. Results show that the coefficient of determination (R²) of the BO-DNN model reaches 92.6%. Compared to multiple mainstream deep learning algorithms, its performance is improved by approximately 11.61%, and the mean square error is significantly reduced. The BO-DNN model demonstrates excellent generalization capability and stability. The findings of this study provide a theoretical basis for the rapid and accurate prediction of the HHV-SBF.

As a key component for perception of the environment and structural response, the normal operation of sensors is crucial for the safety assessment of pipe-rack offshore platforms. However, in long-term service, sensors on the jacket platform are susceptible to high salt-spray corrosion and strong vibration in marine environments, which can lead to bias, drift, accuracy degradation, gain anomaly, and stuck-at faults. These fault signals can mask real structural damage and cause misjudgment. The existing temporal diagnosis models have difficulty in simultaneously capturing long-range dependencies and the local dynamic evolution in temporal data. Therefore, this paper proposes to construct a sensor fault diagnosis model based on the parallel fusion of a Temporal Convolutional Network (TCN) and Gated Recurrent Unit (GRU). First, five types of sensor fault mathematical models were established, and 10 composite fault conditions were constructed. Second, a dual branch parallel architecture was designed, using sliding windows and time-domain expansion to construct the time-series dataset. The TCN branch relies on causal and dilated convolution to capture the long-term dependencies of signals, while the GRU branch extracts local dynamic evolution features through a dual gate control mechanism. The TCN and GRU branches extract features in parallel and fuse them to complete fault classification. On a numerical model of a pipe-rack offshore platform containing seven triaxial acceleration sensors, this method achieved an accuracy of 97.34% in diagnosing 10 types of composite faults, significantly better than single TCN (89.2%) and single GRU (84.8%). The confusion matrix display can effectively distinguish even when encountering fault types with similar characteristics such as drift + stuck composite faults. In the scaled-model physical experiment, the TCN-GRU sensor fault diagnosis model is validated on a ten-class composite fault dataset constructed by superimposing five types of mathematically synthesized sensor faults onto measured healthy signals collected from a laboratory-scale model (not naturally occurring sensor faults), and achieves an accuracy of 91.18%. This study focuses on the sensor fault diagnosis of offshore platforms with pipeline racks under the influence of typhoons, and proposes an intelligent sensor fault diagnosis model based on TCN-GRU parallel fusion. At the same time, the model was validated through scaled physical experiments, as an auxiliary check to verify the effectiveness of the experimental platform and data-acquisition system.

As an important marginal sea in the western Pacific, sea-level changes in the South China Sea not only respond to global warming but are also regulated by regional ocean dynamics and climate modes, exerting profound impacts on the socioeconomic development and engineering safety of coastal regions. To address the widespread issues of low accuracy and robustness in existing sea-level prediction models when handling nonlinear, multi-scale sequences, as well as the complexity of sea-level change mechanisms in the South China Sea, this study constructs a hybrid model combining Singular Spectrum Analysis and Long Short-Term Memory neural networks (SSA-LSTM). The coral skeletal oxygen isotope ratio (δ¹⁸O) used in this study is a key indicator for characterizing the marine environment, defined as the per mille difference in the ¹⁸O/¹⁶O ratio of a sample relative to a standard. Based on coral δ¹⁸O data from the South China Sea, the sea level from 1850 to 2015 is reconstructed. SSA is then applied to decompose the sea-level data into trend and periodic components. The trend component, accounting for 37.03%, and components 2 to 11, containing major periodic information, are extracted to reconstruct the sea-level series. The reconstructed series retains 95.89% of the original information. The trend component is modeled through curve fitting, while the periodic components are modeled using an LSTM neural network. Optimal hyperparameters for the LSTM are determined through parameter sensitivity analysis. An integrated SSA-LSTM model is constructed to predict sea level in the South China Sea, and its predictions are compared with those from a Singular Spectrum Analysis-Autoregressive Integrated Moving Average (SSA-ARIMA) model. The results indicate that from 1850 to 2015, sea level in the South China Sea exhibits periodic fluctuations with a significant overall upward trend. Specifically, the growth rate from 1921 to 1940 reaches 5.49 mm/yr. Predictions from the SSA-LSTM model are significantly higher than those from the SSA-ARIMA model. The SSA-LSTM model projects that from 2016 to 2035, sea level in the South China Sea will continue to rise at a fluctuating rate of 0.75 mm/yr, with a cumulative rise of approximately 15 mm. This study provides a novel methodology for investigating the mechanisms of sea-level change in the South China Sea and offers a scientific basis for coastal risk management.