INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue II, February 2026
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From Empirical Construction to Intelligent Automation: State of the
Art in Foundation Design for Single-Family Housing
Dulce Aurora Velázquez Vázquez
Universidad La Salle Bajío. Avenida Universidad 602, Lomas del Campestre, 37150. León de los
Aldama, GTO.
DOI:
https://doi.org/10.51583/IJLTEMAS.2026.15020000026
Received: 16 February 2026; Accepted: 23 February 2026; Published: 05 March 2026
ABSTRACT
Single-family housing represents a critical sector in urban development, where the structural safety of
foundations is essential to ensure habitability and the service life of buildings. This article presents a narrative
review of the state of the art focused on the evolution of strip footing design methodologies, transitioning from
empirical methods to advanced computational tools. The limitations of traditional construction practices and the
accessibility barriers of current commercial software are analyzed. Likewise, the emerging potential of artificial
intelligence and automated calculation is examined as solutions to optimize structural and economic efficiency.
The findings suggest the existence of a technological gap in the social housing sector, which can be mitigated
through the development of customized, low-cost tools that integrate machine learning techniques.
Keywords: Foundation Design Automation, Artificial Intelligence in Civil Engineering, Structural Safety in
Social Housing
INTRODUCTION
General Context
Housing is a fundamental pillar for social development and stability, representing one of the greatest assets for
families in urban and peri-urban contexts. In Mexico, the growth of cities has driven the demand for single-
family housing, where the choice of the foundation system is decisive for structural safety and building
durability. Among the predominant construction systems, strip footings stand out as one of the most widely used
solutions due to their constructive simplicity, ease of execution, and, in theory, relatively low cost. However, the
apparent simplicity of this type of foundation contrasts with the complexity of soil-structure interaction, which
requires rigorous technical analysis that is often overlooked in common practice.
Problem Statement
Despite the structural relevance of foundations, in the single-family housing sector—especially in social housing
or self-construction—the design of strip footings faces a problematic dichotomy. On one hand, a significant
percentage of projects are developed under empirical practices, based on the builder’s experience or “rules of
thumb” that do not consider the specific geotechnical variables of the site nor the actual loads of the building.
This often results in undersized foundations, compromising structural safety against differential settlements or
soil failures, or oversized foundations, unnecessarily increasing costs in a context where resource optimization
is critical.
On the other hand, although there have been significant advances in structural calculation software, the
commercial tools available (such as CYPECAD, ETABS, or Robot Structural Analysis) present important entry
barriers for their widespread adoption in small-scale projects. These barriers include high license costs, the need
for specialized hardware, and a steep learning curve. Consequently, the structural design process remains highly
dependent on manual execution and the subjectivity of the designer, introducing variability and potential risk of
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue II, February 2026
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human error. The lack of accessible, automated tools adapted to local regulations to evaluate the structural
efficiency of foundations represents an urgent technological gap to address.
Justification of the Review
Given this scenario, it is imperative to understand the evolution of design methodologies to identify where the
limitations of current approaches lie and where future solutions should be directed. The analysis of existing
literature shows how civil engineering has transitioned from manual analytical methods to computational
modeling, and how this transition has left behind sectors with lower economic capacity. Conducting a state-of-
the-art review not only serves to diagnose technological stagnation in foundation design for single-family
housing but also lays the groundwork to justify the integration of emerging technologies. In this context,
automated calculation and artificial intelligence (AI) emerge as viable alternatives to democratize access to safe
and efficient structural designs.
Objective of the Article
The objective of this article is to analyze the state of the art in foundation design for single-family housing,
examining the evolution from empirical and manual methodologies to the use of specialized software. Through
this narrative review, the aim is to identify gaps in accessibility and efficiency of current tools, supporting the
need to develop automated systems based on artificial intelligence that optimize the design of strip footings,
aligned with sustainability and structural safety principles required by the current housing sector.
METHODOLOGY
This article is developed under a narrative literature review approach (state of the art), which allows synthesizing
the historical and technological evolution of structural design methodologies. This approach is the most suitable
for identifying trends, knowledge gaps, and the theoretical context necessary to understand the current problem
of foundations in single-family housing.
Search Strategy
For information collection, academic databases and recognized scientific repositories in engineering and
architecture were consulted, such as Google Scholar, Redalyc, Scopus, Dialnet, and ScienceDirect. The search
was limited to articles published in indexed journals, postgraduate theses, and book chapters, prioritizing
documents published between 2012 and 2025, to ensure the relevance of information with current regulations
and technologies.
The search equation was built using Boolean operators combining the following descriptors or keywords, derived
from the base research project: “Structural design,” “Shallow foundations,” “Strip footings,” “Single-family
housing,” “Automated structural calculation,” “Engineering software” (CYPECAD, ETABS), “Structural
efficiency,” and “Civil engineering.”
Selection Criteria
The selection of documents for this review was carried out through a filtering process based on inclusion and
exclusion criteria defined a priori, with the objective of ensuring the relevance of the information to the study’s
aim:
Inclusion criteria:
Theoretical and experimental studies addressing the design, calculation, or analysis of shallow
foundations, specifically strip footings.
Research comparing traditional design methodologies with the use of specialized software.
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
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Articles discussing the challenges of single-family housing, social housing, or self-construction in urban
contexts.
Publications in Spanish and English.
Exclusion criteria:
Documents focused exclusively on deep foundations (piles, drilled shafts) or on high-rise buildings
whose design mechanics are not comparable to single-family housing.
Opinion articles without technical or scientific support.
Documents that do not present a clear methodology of analysis or bibliographic review.
Table 1 summarizes the inclusion and exclusion criteria applied in the selection of documents for this review.
Table 1. Inclusion and Exclusion Criteria for Literature Selection
Criteria Type
Description
Inclusion
Criteria
- Theoretical and experimental studies addressing the design, calculation, or analysis of shallow
foundations, specifically strip footings.
- Research comparing traditional design methodologies with the use of specialized software.
- Articles discussing single-family housing, social housing, or self-construction in urban
contexts.
- Publications in Spanish and English.
Exclusion
Criteria
- Documents focused exclusively on deep foundations (piles, drilled shafts) or high-rise
buildings whose design mechanics are not comparable to single-family housing.
- Opinion articles without technical or scientific support.
- Documents lacking a clear methodology of analysis or bibliographic review.
Analysis Procedure
Once the relevant documents were identified—including previous references collected in the research protocol—
a critical reading of the abstracts was conducted, followed by a full-text review. The information was organized
and synthesized into an analysis matrix that allowed categorization of the findings into three main thematic axes
for the development of the article:
Traditional and empirical methods: Identification of manual practices and associated risks.
Current computational tools: Analysis of advantages, disadvantages, and access barriers
(cost/licenses).
Need for optimization: Identification of the technological gap that justifies new solutions.
Evolution of Design Methodologies: State of the Art
Structural design of foundations has undergone a significant transformation in recent decades, moving from
manual and approximate procedures to high-precision computational models. This evolution responds to the
increasing complexity of seismic-resistant regulations and the need to optimize resources in construction. Below,
the three predominant stages in the context of single-family housing are analyzed.
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
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Figure 1. Evolution of foundation design methodologies for single-family housing. The diagram illustrates
the transition from empirical methods (rules of thumb, builder experience, generic tables) to commercial
software (CYPECAD, ETABS, Robot Structural Analysis), and finally to AI and automation-based tools
(machine learning, metaheuristics, open-source programming).
Traditional and Empirical Methods
Historically, the design of strip footings in single-family housing, especially in self-construction and social
housing sectors, has relied on empirical methods. These practices are based on the builder’s experience, the use
of “rules of thumb,” or the application of generic tables that do not consider the variability of soil properties.
Although these methods offer an initial advantage in terms of speed and low design cost, they present critical
deficiencies in structural safety. The absence of rigorous analysis of bearing capacity and differential settlements
often leads to undersized foundations, prone to failure during seismic events or changes in soil conditions. The
persistence of empiricism is a documented problem in various contexts, where the lack of technical supervision
results in severe pathologies in buildings. Recent studies highlight that geotechnical uncertainty, combined with
the lack of precise data, makes empirical methods unsafe to guarantee the service life of housing.
The Era of Commercial Software (CAD/BIM) and Structural Analysis
With the rise of technology, civil engineering adopted specialized software tools that allowed modeling structural
behavior with greater accuracy. Current literature shows widespread use of platforms such as CYPECAD,
ETABS, and Robot Structural Analysis for building analysis.
Authors such as Huaraca Ramos (2018) have conducted comparative analyses using Robot Structural Analysis
and ETABS to evaluate the behavior of self-built housing, demonstrating that the use of these tools is essential
to detect structural pathologies ignored by empirical methods. Likewise, De Alba Quintero et al. explored
architectural modeling through software such as Cype Architecture, integrating geometric design with structural
calculation.
In the geotechnical and seismic-resistant field, software precision becomes vital. Recent studies, such as Salcedo
Quispe (2025), emphasize the importance of using advanced software for the analysis of clay soils and the design
of connected foundation beams. Similarly, Vilema Condo (2014) and Ore Cardenas et al. (2022) used
CYPECAD to ensure safety against seismic loads and to evaluate behavior in saturated soils, respectively.
However, the adoption of these technologies is not without challenges. Sacks et al. (2018) point out that, although
Building Information Modeling (BIM) and structural software have improved coordination, problems of
interoperability and the need for highly skilled labor persist.
In addition, Ahmed et al. (2022) emphasize in their studies on single-family housing that, although software
increases precision, the learning curve and license costs remain significant barriers for widespread
implementation in small-scale projects. This confirms that current technology is efficient but not necessarily
accessible or democratic.
Empirical Methods
(Rules of thumb,
builder experience,
generic tables)
Commercial software
(CAD/BIM)
(CYPECAD, ETABS,
Robot Structural
Analysis))
AI and Automation-
Based Tools
(Machine Learning,
Metaheuristics, Open-
source programming)
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Towards Optimization and Artificial Intelligence
Faced with the limitations of cost and accessibility of commercial software, recent scientific literature points to
a new era based on automated calculation and artificial intelligence (AI). The integration of machine learning
algorithms and evolutionary computation allows optimizing structural design, seeking not only safety but also
economic efficiency—an essential aspect in social housing.
In this context, Shahin (2015) and Zhang et al. (2021) conducted comprehensive reviews on the application of
machine learning techniques in geotechnical engineering, concluding that these tools are superior to empirical
methods for predicting soil bearing capacity and foundation behavior. Specifically, Sadrossadat et al. (2020)
demonstrated that artificial neural networks can predict the load capacity of strip and isolated footings with high
precision, minimizing the need for costly field tests and reducing the margin of human error.
Likewise, structural optimization through metaheuristics has gained ground. Kaveh and Javadi (2022) proposed
advanced algorithms for the design of shallow foundations, achieving significant reductions in concrete and steel
volume, which directly translates into cost savings. This line of research supports the premise of the present
project: automation is not only a matter of convenience but a technical tool to maximize structural efficiency.
Therefore, the trend observed in the state of the art suggests that the future of foundation design for single-family
housing lies in the development of customized tools, based on open-source code (such as Python) and enhanced
by AI. These solutions would overcome the economic barriers of commercial software, offering professionals
and students the ability to perform optimal, safe, and regulation-compliant designs without incurring high
licensing costs, thus filling the gap identified in current methods.
Table 2 summarizes the comparative characteristics of empirical methods, commercial software, and AI-based
tools in foundation design.
Table 2. Comparative Analysis of Foundation Design Methodologies
Aspect
Empirical Methods
AI and Automation-Based Tools
Cost
Low initial cost; no licenses
Low-cost if open-source; scalable
solutions
Accessibility
Widely used in self-
construction
Potentially accessible to students and
small projects
Precision
Low; based on rules of
thumb
Very high; predictive models and
optimization algorithms
Risks
Structural failures due to
under/oversizing
Dependence on data quality and
algorithm calibration
Examples
Local builder practices,
generic tables
Neural networks, metaheuristics,
Python-based open-source tools
DISCUSSION
The findings derived from the literature review reveal a significant disparity between technological advances in
structural engineering and their practical application in single-family social housing. The critical analysis of
these methodologies suggests that the central problem does not lie in the lack of technical knowledge, but rather
in the inefficiency of technology transfer mechanisms to the housing construction sector. The discussion is
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structured around three fundamental axes: the paradox of accessibility, the potential of structural efficiency
through algorithms, and the need for contextualized automation.
Figure 2. Conceptual scheme of the technological gap in foundation design. The diagram illustrates the
contrast between unsafe empiricism (rules of thumb, lack of precision, structural safety gap) and
inaccessible high technology (high-cost software, barriers to small builders), proposing accessible
automation (low-cost tools, optimized and safe designs) as a bridging solution.
The examination of available tools reveals a concerning contradiction in the current market. On one hand, there
is high-precision software capable of modeling complex behaviors with great accuracy, ensuring regulatory
compliance. On the other hand, these platforms present entry barriers—economic and related to the learning
curve—that make them unattainable for small builders and social housing projects.
This situation generates a “structural safety gap”: those with resources have access to optimized and safe designs,
while lower-income sectors are forced to rely on empirical or simplified practices, exposing themselves to failure
risks. The literature consulted indicates that the problem is not the lack of technology per se, but its
democratization. Current commercial software is designed for large corporate projects and complex buildings,
leaving a void in the single-family housing market. In this context, the proposal to develop low-cost automated
systems is not merely a technical improvement, but a necessary measure to balance access to structural safety in
society.
Beyond accessibility, comparative analysis shows that traditional manual calculation methodologies are reaching
a limit of optimization. Manual designs tend to be excessively conservative (oversizing elements to compensate
for uncertainty) or, conversely, risky due to lack of knowledge of real geotechnical variables. Both scenarios
negatively impact housing economics: the former due to high material costs and the latter due to latent repair or
collapse costs.
In contrast, the integration of optimization algorithms and machine learning represents a qualitative leap. The
reviewed studies consistently demonstrate that advanced computational techniques, such as metaheuristics and
neural networks, are capable of processing a greater number of geotechnical and structural variables to find
footing configurations that use exactly the necessary material—no more, no less. This suggests that adopting
these tools not only improves safety but also has the direct potential to reduce foundation costs, making quality
housing viable in resource-limited contexts.
Finally, reflection on existing tools points to the need for solutions that are not only powerful but also relevant.
The vast majority of available structural software follows a “black box” logic or requires expert interpretation
of results, distancing the average user from the design process. In addition, these programs are often generic,
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designed for international standards that do not always align with specific regulations or local construction
practices.
Therefore, the natural evolution of structural design should aim toward contextualized automation. This implies
the development of customized tools that integrate the power of artificial intelligence with accessible interfaces
and algorithms calibrated to local regulations and conditions. The approach based on open programming
environments and personalized scripts emerges as the most coherent response to break dependence on costly
licenses. This strategy allows the creation of “tailor-made” tools that empower both engineers and students,
ensuring that the transition to digital design is inclusive and effective for the reality of single-family housing.
In conclusion, the analysis of methodological evolution suggests that the sector is stuck between unsafe
empiricism and inaccessible high technology. The only viable path to move toward a sustainable and safe
housing model is the implementation of intermediate systems: automated, intelligent, and accessible tools that
optimize resources and bridge the current technological gap.
CONCLUSIONS
The state-of-the-art review presented in this article allows us to conclude that the structural design of foundations
for single-family housing is currently at a turning point. It has been shown that empiricism, although accessible,
is unsustainable under modern regulations and represents a latent risk for safety. At the same time, commercial
software, while technologically superior, presents economic and technical barriers that restrict it to a niche of
users, excluding the vast majority of social housing and self-construction projects.
The literature consistently demonstrates that artificial intelligence tools and optimization algorithms are not
merely a technological trend, but a proven technical resource to improve structural efficiency and reduce material
costs. Therefore, the integration of these technologies into open programming environments is proposed as a
viable solution to close the accessibility gap.
The future of foundation design lies in the democratization of technology through automation, thus ensuring
safer, more efficient housing aligned with sustainable development goals.
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