Jonathan V. Taylar

Selecting materials for low-cost housing requires simultaneous consideration of economic constraints, environmental burdens, technical performance, durability, and implementation feasibility. This article presents a comparative literature-based synthesis of a retained 29-source corpus with a recent-literature emphasis on 2021–2026 rather than a comprehensive historical boundary. One seminal 2019 review was retained for conceptual continuity. Because original search logs, duplicate-removal records, and exclusion lists were unavailable, the corpus is neither exhaustive nor fully reproducible and the article is not presented as a systematic or scoping review. Seven analytical material categories were compared using five author-defined criteria: affordability and life-cycle cost; environmental performance; technical performance and durability; implementation feasibility; and sustainability and circularity relevance. Environmental performance was most strongly represented, whereas complete life-cycle cost, long-term durability, code acceptance, and verified implementation capacity were less consistently documented. Conventional systems retained advantages in standards, supply chains, construction familiarity, and scale. Low-carbon conventional systems offered a potentially pragmatic transition pathway, while earth-, bamboo-, recycled-, plastic-waste-, and bio-based materials showed context-dependent benefits that varied with intended function, climate, treatment, quality control, maintenance, and regulatory acceptance. Direct cross-material ranking was constrained by heterogeneous functional units, life-cycle boundaries, service-life assumptions, transport distances, and maintenance scenarios. A preliminary adaptive material selection framework is proposed using transparent 1–5 scoring, context-specific weighting, evidence-confidence flags, and non-compensatory safety checks. The framework is illustrative, nonoperational, and nonvalidated; expert elicitation, stakeholder consultation, regional case testing, sensitivity analysis, and comparison with actual housing decisions are required before project-level use.

Roof Classification is an approach currently utilized by Southeast Asian countries as a tool in assisting decision makers during the design process of urban policies and development which aims to ease congestion and proper allocation of land properties. However, the Philippines is among the countries who have yet to implement this into its urbanization programs. Data augmentation and SMOTE is performed to address the potential low accuracy issue that limits the existing CNN model for roof classification. A series of experimentation to determine the ideal number of epochs and batch size were performed to acquire the optimal accuracy for the roof classification. Consequently, the model achieved a promising result in terms of its classification performance and accuracy. The CNN model exhibits strong metrics across precision, recall, and F1-score with an average accuracy of 97%.

The convolutional neural network (CNN) is commonly used in visual recognitions and classifications. However, CNN can also be applied as a forecaster that can extract features from spatiotemporal data. This paper proposes a 24h ahead electricity price forecasting method, which integrates CNN with an evolutionary algorithm and utilizes spatiotemporal data. The optimal structure of the CNN network for the locational marginal price (LMP) forecasting was obtained using a genetic algorithm (GA). A gene mapping scheme was initially encoded to represent the search space and the process of selection, mutation, and crossover eliminated structures that did not satisfy the validation fitness function and then competitive individuals were generated. The evolution process uses the root mean square error (RMSE) as the validation fitness function, which is optimzed by training the created CNN network. The proposed gene mapping scheme can be used to design an optimal CNN structure once the mapping between gene binary bits and parameters/hyperparameters of CNN is given. Day-ahead LMP and demand datasets from Pennsylvania-New Jersey-Maryland (PJM) power market were used to demonstrate the evolutionary capability of the proposed method and the finding of optimal CNN structures. Each studied dataset was grouped into 4 subsets corresponding to various seasonal characteristics (different types of situations in real life). Experimental results revealed that the proposed GA-CNN always yielded a higher forecasting accuracy and lower error rates than other forecasting methods.