Based on the PSO-ELM model, we analyze the key elements of safety input to respond to accidents and construct and evaluate its resource input optimization scheme. Based on the PSO-ELM cost prediction model, we analyze the key safety inputs for accident response and construct and evaluate the optimal allocation of resources. The results show that improving the technical level of component lifting is the key point of safety management in the construction of assembled buildings; increasing the strength of safety inspection before delivery of components, enhancing the technical performance of component safety status identification, and reasonably planning the frequency of using special transportation vehicles for components are effective ways to achieve the balance of safety, schedule, and cost of the project.
Traditional residential lighting systems have the problem of high energy consumption. Based on artificial neural network (ANN), combined with particle swarm optimization algorithm, and genetic algorithm to optimize the initial weights and thresholds, an improved ANN prediction model for residential energy-saving lighting is proposed, and an actual residential lighting project is taken as an example to verify it. The results show that the proposed method can quickly predict the number of residential lighting lamps under the premise of meeting the standard illumination of residential lighting. The prediction accuracy can reach 98.45%, which has the characteristics of high prediction accuracy and small error. Compared with the ANN model and ANFIS model, the average relative error of the proposed prediction model is reduced by 2.29% and 0.87%, respectively, which has certain effectiveness and superiority. It provides a new idea for residential energy-saving lighting.
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